WO2021236961A1

WO2021236961A1 - System and method for processing medical claims

Info

Publication number: WO2021236961A1
Application number: PCT/US2021/033462
Authority: WO
Inventors: Daniel Posnack; Peter ARN; Wendy Para; S. Adam Hacking; Steven Mason; Micheal Mueller; Joseph GUANERI; Jonathan Green
Original assignee: Rom Technologies, Inc.
Priority date: 2020-05-21
Filing date: 2021-05-20
Publication date: 2021-11-25

Abstract

A computer-implemented system for processing medical claims is disclosed. The system includes a medical device configured to be manipulated by a user while the user performs a treatment plan; a patient interface associated with the medical device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session; and a processor. During the telemedicine session, the processor is configured to receive information from a medical device. Using the device-generated information, the processor is further configured to determine device-based medical coding information. The processor is further configured to transmit the device-based medical coding information to a claim adjudication server.

Description

SYSTEM AND METHOD FOR PROCESSING MEDICAL CLAIMS

CROSS-REFERENCES TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/147,642, filed on January 13, 2021, which is a continuation of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 63/028,392, filed on May 21, 2020, the entire disclosures of which are incorporated herein by reference.

[0002] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/149,457, filed on January 14, 2021, which is a continuation of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 63/028,399, filed on May 21, 2020, the entire disclosures of which are incorporated herein by reference.

[0003] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/147,593, filed on January 13, 2021, which is a continuation of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 63/028,420, filed on May 21, 2020, the entire disclosures of which are incorporated herein by reference.

[0004] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/148,354, filed on January 13, 2021, which is a continuation of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which is a continuation of U.S. Patent Application Serial No. 16/987,087, filed on August 6, 2020, the entire disclosures of which are incorporated herein by reference. [0005] This application claims priority to and the benefit of U.S. Patent Application Serial No. 17/148,339, filed on January 13, 2021, which is a continuation of U.S. Patent Application Serial No. 17/021,895, filed on September 15, 2020, which claims priority to and the benefit of U.S. Provisional Patent Application Serial No. 62/910,232, filed on October 3, 2019; and which is a continuation of U.S. Patent Application Serial No. 16/987,048, filed on August 6, 2020, the entire disclosures of which are incorporated herein by reference.

TECHNICAL FIELD

[0006] This disclosure relates generally to systems and methods for adjudication of medical device claims. This disclosure also relates generally to systems and methods of transmitting and processing data. This disclosure also relates generally to systems and methods for processing medical claims using biometric signatures. BACKGROUND

[0007] Electronic medical record (EMR) systems may be used to generate and maintain an electronic record of health-related information relating to or about individuals within a health care organization. The health- related information may be input by a variety of entities, e.g., the individuals’ health care providers, where such entries may be made by any medically -related entity or its representatives, for example: administrators, nurses, doctors, or other authorized individuals; insurance companies; billing companies; hospitals; testing centers, such as those related to radiologic services, blood and bodily fluid testing services; and psychological service providers, such as psychologists, social workers, addiction and other counselors, and psychiatrists. Each healthcare service may have one or more medical billing codes, for example Diagnosis-Related Group (DRG) and/or International Classification of Diseases (ICD) codes, e.g., ICD-10, assigned for billing purposes. Some of the individual’s EMRs, including the one or more medical billing codes, may be transferred to a third-party payor, such as an insurance company, for invoicing the individual’s medical claims for the individual’s healthcare services. A medical claim, or a claim, is a medical bill, or bill, submitted to a health insurance carrier, or other party responsible for payment, for services rendered and/or goods provided to patients by health care providers. After a medical claim is submitted to the insurance company, the insurance company determines its financial responsibility for the payment to the healthcare provider (i.e., claim adjudication). The insurance company may have procedures to ensure that no false medical claims are approved for payment, for example, by rejecting payment for medical billing codes inconsistent with the healthcare services provided. As a result of such procedures, the insurance company may decide to pay the medical claim in full, reduce the medical bill, deny the full medical claim, or revise the nature of the claim such that it becomes eligible for full or partial payment.

[0008] Medical billing may present difficulties in medical billing code adjudication, often making it difficult for the healthcare provider to be paid for its healthcare services. The data transfer from the healthcare provider to the insurance company may not always be reliable, due in part to the volume of data, data security, and data consistency issues (i.e., errors in the information). Further, human error or malicious interlopers can reduce the reliability of such systems. The use of telemedicine may result in additional risks related to fraud, waste, and abuse, risks which bad actors can exploit. For example, if, at a location other than a healthcare facility, the medical device is being used, a healthcare provider may not oversee the use (e.g., treatment, rehabilitation, or testing), and therefore, the healthcare provider may not be able to easily confirm or validate the accuracy of the medical billing. Further, mass transfer of this data as data packets between various parts of the systems may increase network loads and slow processing time.

[0009] Remote medical assistance, or telemedicine, may aid a patient in performing various aspects of a rehabilitation regimen for a body part. The patient may use a patient interface in communication with an assistant interface for receiving the remote medical assistance via audio and/or audiovisual communications. SUMMARY

[0010] In general, the present disclosure provides a system and methods for processing medical claims based on medical services. Such medical services may have been performed by an individual, by a medical device, or by combinations thereof, e.g., individuals using certain medical devices.

[0011] An aspect of the disclosed embodiments includes a computer-implemented system for processing medical claims. The computer-implemented system includes a medical device configured to be manipulated by a user while the user performs a treatment plan; a patient interface associated with the medical device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session; and a processor. During the telemedicine session, the processor is configured to receive information from a medical device. Using the device -generated information, the processor is further configured to determine device-based medical coding information. The processor is further configured to transmit the device-based medical coding information to a claim adjudication server.

[0012] An aspect of the disclosed embodiments includes a system for processing medical claims. The system includes a processor configured to receive device-generated information from a medical device. Using the device-generated information received, the processor is configured to determine device-based medical coding information. The processor is further configured to transmit the device-based medical coding information to a claim adjudication server.

[0013] An aspect of the disclosed embodiments includes a method for a clinic server to process medical claims. The method includes receiving information from a medical device. The method further includes, using the device-generated information, determining device-based medical coding information. The method further includes transmitting the device-based medical coding information to a claim adjudication server.

[0014] An aspect of the disclosed embodiments includes a tangible, non-transitoiy computer-readable medium. The tangible, non-transitoiy computer-readable medium stores instructions that, when executed, cause a processor to receive device-generated information from a medical device. Using the device-generated information received, the processor determines device-based medical coding information. The processor further transmits the device-based medical coding information to a claim adjudication server.

[0015] In yet another aspect, a system for generating and processing medical billing codes is disclosed. The system includes a medical device and a computing device. The computing device comprises a processor in communication with the medical device. The processor is configured to receive information from the medical device and transmit the device-generated information to a clinic server. Using the device-generated information received, the processor is configured to determine device-based medical coding information. The processor is further configured to cause the clinic server to transmit the device-based medical coding information to a claim adjudication server.

[0016] An aspect of the disclosed embodiments includes a method for operating a medical device. The method includes receiving information from the medical device. The method further includes transmitting the device generated information to the clinic server. Using the device-generated information received, the method further includes causing the clinic server to determine device-based medical coding information. The method further includes causing the clinic server to transmit the device-based medical coding information to a claim adjudication server. [0017] An aspect of the disclosed embodiments includes a tangible, non-transitor computer-readable medium. The tangible, non-transitory computer-readable medium stores instructions that, when executed, cause a processor to receive information from the medical device. The instructions further cause the processor to transmit the device -generated information to a clinic server. The instructions further cause the processor to cause the clinic server to, using the device-generated information, determine device-based medical coding information. The instructions further cause the processor to cause the clinic server to transmit the device-based medical coding information to a claim adjudication server.

[0018] An aspect of the disclosed embodiments includes a computer-implemented system for processing medical claims. The computer-implemented system includes a medical device configured to be manipulated by a user while the user performs a treatment plan; a patient interface associated with the medical device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session; and a processor. The processor is configured to, during the telemedicine session, receive device generated information from the medical device; generate a first biometric signature; using the device-generated information, generate a second biometric signature; using the first and second biometric signatures, generate a signature comparison; using the signature comparison, generate a signature indicator; and transmit the signature indicator.

[0019] An aspect of the disclosed embodiments includes a computer-implemented system includes a treatment apparatus configured to be manipulated by a patient while performing a treatment plan and a server computing device configured to execute an artificial intelligence engine to generate the treatment plan and a billing sequence associated with the treatment plan. The server computing device receives information pertaining to the patient, generates, based on the information, the treatment plan including instructions for the patient to follow, and receives a set of billing procedures associated with the instructions. The set of billing procedures includes rules pertaining to billing codes, timing, constraints, or some combination thereof. The server computing device generates, based on the set of billing procedures, the billing sequence for at least a portion of the instructions. The billing sequence is tailored according to a certain parameter. The server computing device transmits the treatment plan and the billing sequence to a computing device.

[0020] An aspect of the present disclosure includes a computer-implemented system includes a treatment apparatus configured to be manipulated by a patient while performing a treatment plan, and a server computing device configured. The server computing device receives treatment plans that, when applied to patients, cause outcomes to be achieved by the patients, receives monetary value amounts associated with the treatment plans, where a respective monetary value amount of the monetary value amounts is associated with a respective treatment plan of the treatment plans. The server computing device receives constraints including rules pertaining to billing codes associated with the treatment plans. An artificial intelligence engine generates optimal treatment plans for a patient based on the treatment plans, the monetary value amounts, and the constraints, wherein each of the optimal treatment plans complies with the constraints and represents a patient outcome and an associated monetary value amount generated.

[0021] Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims. [0022] Another aspect of the disclosed embodiments includes a system that includes a processing device and a memory communicatively coupled to the processing device and capable of storing instructions. The processing device executes the instructions to perform any of the methods, operations, or steps described herein.

[0023] Another aspect of the disclosed embodiments includes a tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to perform any of the methods, operations, or steps disclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that, according to common practice, the various features of the drawings are not to-scale. On the contrary, the dimensions of the various features are arbitrarily expanded or reduced for clarity.

[0025] For a more complete understanding of this disclosure and its advantages, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which:

[0026] FIG. 1 generally illustrates a component diagram of an illustrative medical system according to the principles of the present disclosure.

[0027] FIG. 2 generally illustrates an example medical device according to the principles of the present disclosure.

[0028] FIG. 3 generally illustrates a component diagram of an illustrative clinic server system according to the principles of the present disclosure.

[0029] FIG. 4 generally illustrates a component diagram and method of an illustrative medical claim processing system and information flow according to the principles of the present disclosure.

[0030] FIG. 5 generally illustrates a component diagram of an alternative arrangement of an illustrative medical claim processing system according to the principles of the present disclosure.

[0031] FIG. 6 generally illustrates a method of processing medical claims at a clinic server according to the principles of the present disclosure.

[0032] FIG. 7 generally illustrates a method of processing medical claims at a medical system according to the principles of the present disclosure.

[0033] FIG. 8 generally illustrates an example computer system according to certain aspects of this disclosure; [0034] FIG. 9 generally illustrates a perspective view of an embodiment of the device, such as a treatment device, according to certain aspects of this disclosure.

[0035] FIG. 10 generally illustrates a perspective view of a pedal of the treatment device of FIG. 9 according to certain aspects of this disclosure.

[0036] FIG. 11 generally illustrates a perspective view of a person using the treatment device of FIG. 9 according to certain aspects of this disclosure.

[0037] FIG. 12 illustrates a component diagram of an illustrative system for transmitting and ordering asynchronous data according to certain aspects of this disclosure.

[0038] FIG. 13 illustrates an example information-generating device according to certain aspects of this disclosure. [0039] FIGS. 14A and 14B illustrate a method for transmitting data and ordering asynchronous data according to certain aspects of this disclosure.

[0040] FIGS. 15 illustrate a method for transmitting data according to certain aspects of this disclosure.

[0041] FIGS. 16A and 16B illustrate a method for ordering asynchronous data according to certain aspects of this disclosure.

[0042] FIG. 17 generally illustrates a component diagram of an illustrative medical system according to the principles of this disclosure.

[0043] FIG. 18 generally illustrates an example medical device according to the principles of this disclosure. [0044] FIG. 19 generally illustrates a component diagram of an illustrative clinic server system according to the principles of this disclosure.

[0045] FIG. 20 generally illustrates a component diagram and method of an illustrative medical claim processing system according to the principles of this disclosure.

[0046] FIG. 21 generally illustrates a component diagram of an alternative arrangement of an illustrative medical claim processing system according to the principles of this disclosure.

[0047] FIGS. 22A and 22B generally illustrate a method of processing medical claims according to the principles of this disclosure.

[0048] FIG. 23 generally illustrates a perspective view of an embodiment of the device, such as a treatment device according to certain aspects of this disclosure.

[0049] FIG. 24 generally illustrates a perspective view of a pedal of the treatment device of FIG. 23 according to certain aspects of this disclosure.

[0050] FIG. 25 generally illustrates a perspective view of a person using the treatment device of FIG. 7 according to certain aspects of this disclosure.

[0051] FIG. 26 generally illustrates an example computer system according to certain aspects of this disclosure.

[0052] FIG. 27 shows a block diagram of an embodiment of a computer implemented system for managing a treatment plan according to the present disclosure.

[0053] FIG. 28 shows a perspective view of an embodiment of a treatment apparatus according to the present disclosure.

[0054] FIG. 29 shows a perspective view of a pedal of the treatment apparatus of FIG. 28 according to the present disclosure.

[0055] FIG. 30 shows a perspective view of a person using the treatment apparatus of FIG. 28 according to the present disclosure.

[0056] FIG. 31 shows an example embodiment of an overview display of an assistant interface according to the present disclosure.

[0057] FIG. 32 shows an example block diagram of training a machine learning model to output, based on data pertaining to the patient, a treatment plan for the patient according to the present disclosure.

[0058] FIG. 33 shows an embodiment of an overview display of the assistant interface presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure. [0059] FIG.34 shows an embodiment of the overview display of the assistant interface presenting, in real-time during a telemedicine session, recommended treatment plans that have changed as a result of patient data changing according to the present disclosure.

[0060] FIG. 35 shows an embodiment of the overview display of the assistant interface presenting, in real time during a telemedicine session, treatment plans and billing sequences tailored for certain parameters according to the present disclosure.

[0061] FIG. 36 shows an example embodiment of a method for generating, based on a set of billing procedures, a billing sequence tailored for a particular parameter, where the billing sequence pertains to a treatment plan according to the present disclosure.

[0062] FIG. 37 shows an example embodiment of a method for receiving requests from computing devices and modifying the billing sequence based on the requests according to the present disclosure.

[0063] FIG. 38 shows an embodiment of the overview display of the assistant interface presenting, in real time during a telemedicine session, optimal treatment plans that generate certain monetary value amounts and result in certain patient outcomes according to the present disclosure.

[0064] FIG. 39 shows an example embodiment of a method for generating optimal treatment plans for a patient, where the generating is based on a set of treatment plans, a set of money value amounts, and a set of constraints according to the present disclosure.

[0065] FIG. 40 shows an example embodiment of a method for receiving a selection of a monetary value amount and generating an optimal treatment plan based on a set of treatment plans, the monetary value amount, and a set of constraints according to the present disclosure.

[0066] FIG. 41 shows an example embodiment of a method for receiving a selection of an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus according to the present disclosure.

[0067] FIG. 42 shows an example computer system according to the present disclosure.

[0068] FIG. 43 shows a block diagram of an embodiment of a computer implemented system for managing a treatment plan according to the present disclosure;

[0069] FIG. 44 shows a perspective view of an embodiment of a treatment apparatus according to the present disclosure;

[0070] FIG. 45 shows a perspective view of a pedal of the treatment apparatus of FIG. 44 according to the present disclosure;

[0071] FIG. 46 shows a perspective view of a person using the treatment apparatus of FIG. 44 according to the present disclosure;

[0072] FIG. 47 shows an example embodiment of an overview display of an assistant interface according to the present disclosure;

[0073] FIG. 48 shows an example block diagram of training a machine learning model to output, based on data pertaining to the patient, a treatment plan for the patient according to the present disclosure;

[0074] FIG. 49 shows an embodiment of an overview display of the assistant interface presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure; [0075] FIG. 50 shows an embodiment of the overview display of the assistant interface presenting, in real time during a telemedicine session, recommended treatment plans that have changed as a result of patient data changing according to the present disclosure;

[0076] FIG. 51 shows an embodiment of the overview display of the assistant interface presenting, in real time during a telemedicine session, treatment plans and billing sequences tailored for certain parameters according to the present disclosure;

[0077] FIG. 52 shows an example embodiment of a method for generating, based on a set of billing procedures, a billing sequence tailored for a particular parameter, where the billing sequence pertains to a treatment plan according to the present disclosure;

[0078] FIG. 53 shows an example embodiment of a method for receiving requests from computing devices and modifying the billing sequence based on the requests according to the present disclosure;

[0079] FIG. 54 shows an embodiment of the overview display of the assistant interface presenting, in real time during a telemedicine session, optimal treatment plans that generate certain monetary value amounts and result in certain patient outcomes according to the present disclosure;

[0080] FIG. 55 shows an example embodiment of a method for generating optimal treatment plans for a patient, where the generating is based on a set of treatment plans, a set of money value amounts, and a set of constraints according to the present disclosure;

[0081] FIG. 56 shows an example embodiment of a method for receiving a selection of a monetary value amount and generating an optimal treatment plan based on a set of treatment plans, the monetary value amount, and a set of constraints according to the present disclosure;

[0082] FIG. 57 shows an example embodiment of a method for receiving a selection of an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus according to the present disclosure; and

[0083] FIG. 58 shows an example computer system according to the present disclosure.

NOTATION AND NOMENCLATURE

[0084] Various terms are used to refer to particular system components. Different companies may refer to a component by different names - this document does not intend to distinguish between components that differ in name but not function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to ... ” Also, the term “couple” or “couples” is intended to mean either an indirect or direct connection. Thus, if a first device couples to a second device, that connection may be through a direct connection or through an indirect connection via other devices and connections.

[0085] The terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms “a,” “an,” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also to be understood that additional or alternative steps may be employed.

[0086] The terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections; however, these elements, components, regions, layers and/or sections should not be limited by these terms. These terms may be only used to distinguish one element, component, region, layer, or section from another region, layer, or section. Terms such as “first,” “second,” and other numerical terms, when used herein, do not imply a sequence or order unless clearly indicated by the context. Thus, a first element, component, region, layer, or section discussed below could be termed a second element, component, region, layer, or section without departing from the teachings of the example embodiments. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C. In another example, the phrase “one or more” when used with a list of items means there may be one item or any suitable number of items exceeding one.

[0087] Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,” “lower,” “above,” “upper,” “top,” “bottom,” “inside,” “outside,” “contained within,” “superimposing upon,” and the like, may be used herein. These spatially relative terms can be used for ease of description to describe one element’s or feature’s relationship to another element(s) or featme(s) as illustrated in the figures. The spatially relative terms may also be intended to encompass different orientations of the device in use, or operation, in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as “below” or “beneath” other elements or features would then be oriented “above” the other elements or features. Thus, the example term “below” can encompass both an orientation of above and below. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptions used herein interpreted accordingly.

[0088] A “treatment plan” may include one or more treatment protocols, and each treatment protocol includes one or more treatment sessions. Each treatment session comprises several session periods, with each session period including a particular exercise for treating the body part of the patient. For example, a treatment plan for post-operative rehabilitation after a knee surgery may include an initial treatment protocol with twice daily stretching sessions for the first 3 days after surgery and a more intensive treatment protocol with active exercise sessions performed 4 times per day starting 4 days after surgery. A treatment plan may also include information pertaining to a medical procedure to perform on the patient, a treatment protocol for the patient using a treatment device, a diet regimen for the patient, a medication regimen for the patient, a sleep regimen for the patient, additional regimens, or some combination thereof.

[0089] The terms telemedicine, telehealth, telemed, teletherapeutic, telemedicine, remote medicine, etc. may be used interchangeably herein.

[0090] The term “monetary value amount” (singular or plural) may refer to fees, revenue, profit (e.g., gross, net, etc.), earnings before interest (EBIT), earnings before interest, depreciation and amortization (EBITDA), cash flow, free cash flow, working capital, gross revenue, a value of warrants, options, equity, debt, derivatives or any other financial instrument, any generally acceptable financial measure or metric in corporate finance or according to Generally Accepted Accounting Principles (GAAP) or foreign counterparts, or the like.

[0091] The term “optimal treatment plan” may refer to optimizing a treatment plan based on a certain parameter or factors or combinations of more than one parameter or factor, such as, but not limited to, a measure of benefit which one or more exercise regimens provide to users, one or more probabilities of users complying with one or more exercise regimens, an amount, quality or other measure of sleep associated with the user, information pertaining to a diet of the user, information pertaining to an eating schedule of the user, information pertaining to an age of the user, information pertaining to a sex of the user, information pertaining to a gender of the user, an indication of a mental state of the user, information pertaining to a genetic condition of the user, information pertaining to a disease state of the user, an indication of an energy level of the user, information pertaining to a microbiome from one or more locations on or in the user (e.g., skin, scalp, digestive tract, vascular system, etc.), or some combination thereof.

[0092] As used herein, the term healthcare provider may include a medical professional (e.g., such as a doctor, a nurse, a therapist, and the like), an exercise professional (e.g., such as a coach, a trainer, a nutritionist, and the like), or another professional sharing at least one of medical and exercise attributes (e.g., such as an exercise physiologist, a physical therapist, an occupational therapist, and the like). As used herein, and without limiting the foregoing, a “healthcare provider” may be a human being, a robot, a virtual assistant, a virtual assistant in virtual and/or augmented reality, or an artificially intelligent entity, such entity including a software program, integrated software and hardware, or hardware alone.

[0093] The term billing sequence may refer to an order in which billing codes associated with procedures or instructions of a treatment plan are billed.

[0094] The term billing codes may refer any suitable type of medical coding, such as Current Procedural Terminology (CPT), Diagnosis Related Groups (DRGs), International Classification of Disease, Tenth Edition (ICD-10), and Healthcare Common Procedural Coding System (HCPCS).

[0095] Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will preferably but not determinatively be less than 10 seconds (or any suitably proximate difference between two different times) but greater than 2 seconds.

[0096] Any of the systems and methods described in this disclosure may be used in connection with rehabilitation. Rehabilitation may be directed at cardiac rehabilitation, rehabilitation from stroke, multiple sclerosis, Parkinson’s disease, myasthenia gravis, Alzheimer’s disease, any other neurodegenative or neuromuscular disease, a brain injury, a spinal cord injury, a spinal cord disease, a joint injury, a joint disease, post-surgical recovery, or the like. Rehabilitation can further involve muscular contraction in order to improve blood flow and lymphatic flow, engage the brain and nervous system to control and affect a traumatized area to increase the speed of healing, reverse or reduce pain (including arthralgias and myalgias), reverse or reduce stiffness, recover range of motion, encourage cardiovascular engagement to stimulate the release of pain blocking hormones or to encourage highly oxygenated blood flow to aid in an overall feeling of well-being. Rehabilitation may be provided for individuals of average weight in reasonably good physical condition having no substantial deformities, as well as for individuals more typically in need of rehabilitation, such as those who are elderly, obese, subject to disease processes, injured and/or who have a severely limited range of motion. Unless expressly stated otherwise, is to be understood that rehabilitation includes prehabilitation (also referred to as "pre-habilitation" or "prehab"). Prehabilitation may be used as a preventative procedure or as a pre-surgical or pre-treatment procedure. Prehabilitation may include any action performed by or on a patient (or directed to be performed by or on a patient, including, without limitation, remotely or distally through telemedicine) to, without limitation, prevent or reduce a likelihood of injury (e.g., prior to the occurrence of the injury); improve recovery time subsequent to surgery; improve strength subsequent to surgery; or any of the foregoing with respect to any non-surgical clinical treatment plan to be undertaken for the purpose of ameliorating or mitigating injury, dysfunction, or other negative consequence of surgical or non-surgical treatment on any external or internal part of a patient's body. For example, a mastectomy may require prehabilitation to strengthen muscles or muscle groups affected directly or indirectly by the mastectomy. As a further non-limiting example, the removal of an intestinal tumor, the repair of a hernia, open-heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to excise them or parts of them, to treat them, etc., can require cutting through, dissecting and/or harming numerous muscles and muscle groups in or about, without limitation, the skull or face, the abdomen, the ribs and/or the thoracic cavity, as well as in or about all joints and appendages. Prehabilitation can improve a patient's speed of recovery, measure of quality of life, level of pain, etc. in all the foregoing procedures. In one embodiment of prehabilitation, a pre-surgical procedure or a pre-non-surgical-treatment may include one or more sets of exercises for a patient to perform prior to such procedure or treatment. Performance of the one or more sets of exercises may be required in order to qualify for an elective surgery, such as a knee replacement. The patient may prepare an area of his or her body for the surgical procedure by performing the one or more sets of exercises, thereby strengthening muscle groups, improving existing muscle memory, reducing pain, reducing stiffness, establishing new muscle memory, enhancing mobility (i.e., improve range of motion), improving blood flow, and/or the like.

[0097] The phrase, and all permutations of the phrase, “respective measure of benefit with which one or more exercise regimens may provide the user” (e.g., “measure of benefit,” “respective measures of benefit,” “measures of benefit,” “measure of exercise regimen benefit,” “exercise regimen benefit measurement,” etc.) may refer to one or more measures of benefit with which one or more exercise regimens may provide the user.

DETAILED DESCRIPTION

[0098] The following discussion is directed to various embodiments of the present disclosure. Although one or more of these embodiments may be preferred, the embodiments disclosed should not be interpreted, or otherwise used, as limiting the scope of the disclosure, including the claims. In addition, one skilled in the art will understand that the following description has broad application, and the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to intimate that the scope of the disclosure, including the claims, is limited to that embodiment.

[0099] Determining optimal remote examination procedures to create an optimal treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; psychographic; geographic; diagnostic; measurement- or test-based; medically historic; behavioral historic; cognitive; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment device, an amount of force exerted on a portion of the treatment device, a range of motion achieved on the treatment device, a movement speed of a portion of the treatment device, a duration of use of the treatment device, an indication of a plurality of pain levels using the treatment device, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, a glucose level or other biomarker, or some combination thereof. It may be desirable to process and analyze the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[0100] Further, another technical problem may involve distally treating, via a computing device during a telemedicine session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling, from the different location, the control of a treatment device used by the patient at the patient’s location. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a medical professional may prescribe a treatment device to the patient to use to perform a treatment protocol at their residence or at any mobile location or temporary domicile. A medical professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, or the like. A medical professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

[0101] When the healthcare provider is located in a location different from the patient and the treatment device, it may be technically challenging for the healthcare provider to monitor the patient’ s actual progress (as opposed to relying on the patient’s word about their progress) in using the treatment device, modify the treatment plan according to the patient’s progress, adapt the treatment device to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

[0102] Further, in addition to the information described above, determining optimal examination procedures for a particular ailment (e.g., injury, disease, any applicable medical condition, etc.) may include physically examining the injured body part of a patient. The healthcare provider, such as a physician or a physical therapist, may visually inspect the injured body part (e.g., a knee joint). The inspection may include looking for signs of inflammation or injury (e.g., swelling, redness, and warmth), deformity (e.g., symmetrical joints and abnormal contours and/or appearance), or any other suitable observation. To determine limitations of the injured body part, the healthcare provider may observe the injured body part as the patient attempts to perform normal activity (e.g., bending and extending the knee and gauging any limitations to the range of motion of the injured knee). The healthcare provide may use one or more hands and/or fingers to touch the injured body part. By applying pressure to the injured body part, the healthcare provider can obtain information pertaining to the extent of the injury. For example, the healthcare provider’s fingers may palpate the injured body part to determine if there is point tenderness, warmth, weakness, strength, or to make any other suitable observation.

[0103] It may be desirable to compare characteristics of the injured body part with characteristics of a corresponding non-injured body part to determine what an optimal treatment plan for the patient may be such that the patient can obtain a desired result. Thus, the healthcare provider may examine a corresponding non- injured body part of the patient. For example, the healthcare provider’s fingers may palpate a non-injured body part (e.g., a left knee) to determine a baseline of how the patient’s non-injured body part feels and functions. The healthcare provider may use the results of the examination of the non-injured body part to determine the extent of the injury to the corresponding injured body part (e.g., a right knee). Additionally, injured body parts may affect other body parts (e.g., a knee injury may limit the use of the affected leg, leading to atrophy of leg muscles). Thus, the healthcare provider may also examine additional body parts of the patient for evidence of atrophy of or injury to surrounding ligaments, tendons, bones, and muscles, examples of muscles being such as quadriceps, hamstrings, or calf muscle groups of the leg with the knee injury. The healthcare provider may also obtain information as to a pain level of the patient before, during, and/or after the examination.

[0104] The healthcare provider can use the information obtained from the examination (e.g., the results of the examination) to determine a proper treatment plan for the patient. If the healthcare provider cannot conduct a physical examination of the one or more body parts of the patient, the healthcare provider may not be able to fully assess the patient’s injury and the treatment plan may not be optimal. Accordingly, embodiments of the present disclosure pertain to systems and methods for conducting a remote examination of a patient. The remote examination system provides the healthcare provider with the ability to conduct a remote examination of the patient, not only by communicating with the patient, but by virtually observing and/or feeling the patient’s one or more body parts.

[0105] In some embodiments, the systems and methods described herein may be configured for manipulation of a medical device. For example, the systems and methods may be configured to use a medical device configured to be manipulated by an individual while the individual is performing a treatment plan. The individual may include a user, patient, or other a person using the treatment device to perform various exercises for prehabilitation, rehabilitation, stretch training, e.g., pliability, medical procedures, and the like. The systems and methods described herein may be configured to use and/or provide a patient interface comprising an output device, wherein the output device is configured to present telemedicine information associated with a telemedicine session.

[0106] In some embodiments, the systems and methods described herein may be configured for processing medical claims. For example, the system includes a processor configured to receive device-generated information from a medical device. Using the device-generated information received, the processor is configured to determine device-based medical coding information. The processor is further configured to transmit the device-based medical coding information to a claim adjudication server. Any or all of the methods described may be implemented during a telemedicine session or at any other desired time.

[0107] In some embodiments, the medical claims may be processed, during a telemedicine or telehealth session, by a healthcare provider. The healthcare provider may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment device. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive data, instructions, or the like and/or operate distally from the patient and the treatment device.

[0108] In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a medical professional. The video may also be accompanied by audio, text and other multimedia information and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation), and without limitation, gesture recognition, gesture control, touchless user interfaces (TUIs), kinetic user interfaces (KUIs), tangible user interfaces, wired gloves, depth-aware cameras, stereo cameras, and gesture-based controllers. Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds (or any suitably proximate difference between two different times) but greater than 2 seconds.

[0109] FIGS. 1-11, discussed below, and the various embodiments used to describe the principles of this disclosure are by way of illustration only and should not be construed in any way to limit the scope of the disclosure.

[0110] FIG. 1 illustrates a component diagram of an illustrative medical system 100 in accordance with aspects of this disclosure. The medical system 100 may include a medical device 102. The medical device 102 may be a testing device, a diagnostic device, a therapeutic device, or any other suitable medical device. “Medical device” as used in this context means any hardware, software, mechanical or other device, such as a treatment device (e.g., medical device 102, treatment device 10, or the like), that may assist in a medical service, regardless of whether it is FDA (or other governmental regulatory body of any given country) approved, required to be FDA (or other governmental regulatory body of any given country) approved or available commercially or to consumers without such approval. Non-limiting examples of medical devices include a thermometer, an MRI machine, a CT-scan machine, a glucose meter, an apheresis machine, and a physical therapy machine such as a physical therapy cycle. Non-limiting examples of places where the medical device 102 may be located include a healthcare clinic, a physical rehabilitation center, and a user’s home to allow for telemedicine treatment, rehabilitation, and/or testing. FIG. 2 illustrates an example of the medical device 102 where the medical device 102 is a physical therapy cycle.

[0111] As generally illustrated in FIG. 2, the medical device 102 may comprise an electromechanical device, such as a physical therapy device. FIG. 2 generally illustrates a perspective view of an example of a medical device 102 according to certain aspects of this disclosure. Specifically, the medical device 102 illustrated is an electromechanical device 202, such as an exercise and rehabilitation device (e.g., a physical therapy device or the like). The electromechanical device 202 is shown having pedal 210 on opposite sides that are adjustably positionable relative to one another on respective radially-adjustable couplings 208. The depicted electromechanical device 202 is configured as a small and portable unit so that it is easily transported to different locations at which rehabilitation or treatment is to be provided, such as at patients’ homes, alternative care facilities, or the like. The patient may sit in a chair proximate the electromechanical device 202 to engage the electromechanical device 202 with the patient’s feet, for example. The electromechanical device 202 includes a rotary device such as radially-adjustable couplings 208 or flywheel or the like rotatably mounted such as by a central hub to a frame or other support. The pedals 210 are configured for interacting with a patient to be rehabilitated and may be configured for use with lower body extremities such as the feet, legs, or upper body extremities, such as the hands, arms, and the like. For example, the pedal 210 may be a bicycle pedal of the type having a foot support rotatably mounted onto an axle with bearings. The axle may or may not have exposed end threads for engaging a mount on the radially-adjustable coupling 208 to locate the pedal on the radially- adjustable coupling 208. The radially-adjustable coupling 208 may include an actuator configured to radially adjust the location of the pedal to various positions on the radially-adjustable coupling 208. [0112] Alternatively, the radially-adjustable coupling 208 may be configured to have both pedals 210 on opposite sides of a single coupling 208. In some embodiments, as depicted, a pair of radially-adjustable couplings 208 may be spaced apart from one another but interconnected to an electric motor 206. In the depicted example, the computing device 104 may be mounted on the frame of the electromechanical device 202 and may be detachable and held by the user while the user operates the electromechanical device 202. The computing device 104 may present the patient portal 212 and control the operation of the electric motor 206, as described herein.

[0113] In some embodiments, as described in U.S. Patent No. 10,173,094 (U.S. Appl. No. 15/700,293), which is incorporated by reference herein in its entirety for all purposes, the medical device 102 may take the form of a traditional exercise/rehabilitation device which is more or less non-portable and remains in a fixed location, such as a rehabilitation clinic or medical practice. The medical device 102 may include a seat and is less portable than the medical device 102 shown in FIGURE 2. FIG. 2 is not intended to be limiting: the electromechanical device 202 may include more or fewer components than those illustrated in FIG. 2.

[0114] FIGS. 9-10 generally illustrate an embodiment of a treatment device, such as a treatment device 10. More specifically, FIG. 9 generally illustrates a treatment device 10 in the form of an electromechanical device, such as a stationary cycling machine 14, which may be called a stationary bike, for short. The stationary cycling machine 14 includes a set of pedals 12 each attached to a pedal arm 20 for rotation about an axle 16. In some embodiments, and as generally illustrated in FIG. 10, the pedals 12 are movable on the pedal arm 20 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 16 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 16. A pressure sensor 18 is attached to or embedded within one of the pedals 12 for measuring an amount of force applied by the patient on the pedal 102. The pressure sensor 18 may communicate wirelessly to the treatment device 10 and/or to the patient interface 26. FIGS. 9-10 are not intended to be limiting: the treatment device 10 may include more or fewer components than those illustrated in FIGS. 9-10.

[0115] FIG. 11 generally illustrates a person (a patient) using the treatment device 10 of FIG. 9, and showing sensors and various data parameters connected to a patient interface 26. The example patient interface 26 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 26 may be embedded within or attached to the treatment device 10. FIG. 11 generally illustrates the patient wearing the ambulation sensor 22 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 22 has recorded and transmitted that step count to the patient interface 26. FIG. 11 also generally illustrates the patient wearing the goniometer 24 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 24 is measuring and transmitting that knee angle to the patient interface 26. FIG. 11 generally illustrates a right side of one of the pedals 12 with a pressure sensor 18 showing “FORCE 12.5 lbs.”, indicating that the right pedal pressure sensor 18 is measuring and transmitting that force measurement to the patient interface 26. FIG. 11 also generally illustrates a left side of one of the pedals 12 with a pressure sensor 18 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 18 is measuring and transmitting that force measurement to the patient interface 26. FIG. 11 also generally illustrates other patient data, such as an indicator of “SESSION TIME 0:04:13”, indicating that the patient has been using the treatment device 10 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 26 based on information received from the treatment device 10. FIG. 11 also generally illustrates an indicator showing “PAIN LEVEL 3”, Such a pain level may be obtained from the patient in response to a solicitation, such as a question, presented upon the patient interface 26.

[0116] The medical device 102 may include, be coupled to, or be in communication with a computing device 104. The computing device 104 may include a processor 106. The processor 106 can include, for example, computers, intellectual property (IP) cores, application-specific integrated circuits (ASICs), programmable logic arrays, optical processors, programmable logic controllers, microcode, microcontrollers, servers, microprocessors, digital signal processors, any other suitable circuit, or any combination thereof.

[0117] The computing device 104 may include a memory device 108 in communication with the processor 106. The memory device 108 can include any type of memory capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a flash drive, a compact disc (CD), a digital video disc (DVD), a solid state drive (SSD), or any other suitable type of memory.

[0118] The computing device 104 may include an input device 110 in communication with the processor 106. Examples of the input device 110 include a keyboard, a keypad, a mouse, a microphone supported by speech- to-text software, or any other suitable input device. The input device 110 may be used by a medical system operator to input information, such as user identifying information, observational notes, or any other suitable information. An operator is to be understood throughout this disclosure to include both people and computer software, such as programs or artificial intelligence.

[0119] The computing device 104 may include an output device 112 in communication with the processor 106. The output device 112 may be used to provide information to the medical device operator or a user of the medical device 102. Examples of the output device 112 include a display screen, a speaker, an alarm system, or any other suitable output device, including haptic, tactile, olfactory, or gustatory ones, and without limitation, gesture recognition, gesture control, touchless user interfaces (TUIs), kinetic user interfaces (KUIs), tangible user interfaces, wired gloves, depth-aware cameras, stereo cameras, and gesture-based controllers. In some embodiments, such as where the computing device 104 includes a touchscreen, the input device 110 and the output device 112 may be the same device.

[0120] For communicating with remote computers and servers, the computing device 104 may include a network adapter 114 in communication with the processor 106. The network adapter 114 may include wired or wireless network adapter devices or a wired network port.

[0121] Any time information is transmitted or communicated, the information may be in EDI file format or any other suitable file format. In any of the methods or steps of the method, file format conversions may take place. By utilizing Internet of Things (IoT) gateways, data streams, ETL bucketing, EDI mastering, or any other suitable technique, data can be mapped, converted, or transformed into a carrier preferred state. As a result of the volume of data being transmitted, the data security requirements, and the data consistency requirements, enterprise grade architecture may be utilized for reliable data transfer.

[0122] FIG. 1 is not intended to be limiting; the medical system 100 and the computing device 104 may include more or fewer components than those illustrated in FIG. 1.

[0123] FIG. 3 illustrates a component diagram of an illustrative clinic server system 300 in accordance with aspects of this disclosure. The clinic server system 300 may include a clinic server 302. The clinic server system 300 or clinic server 302 may be servers owned or controlled by a medical clinic (such as a doctor's office, testing site, or therapy clinic) or by a medical practice group (such as a testing company, outpatient procedure clinic, diagnostic company, or hospital). The clinic server 302 may be proximate to the medical system 100. In other embodiments, the clinic server 302 may be remote from the medical system 100. For example, during telemedicine-based or telemedicine-mediated treatment, rehabilitation, or testing, the clinic server 302 may be located at a healthcare clinic and the medical system 100 may be located at a patient’s home. The clinic server 302 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, any other suitable computing device, or any combination of the above. The clinic server 302 may be cloud-based or be a real-time software platform, and it may include privacy (e.g., anonymization, pseudo nymization, or other) software or protocols, and/or include security software or protocols. The clinic server 302 may include a computing device 304. The computing device 304 may include a processor 306. The processor 306 can include, for example, computers, intellectual property (IP) cores, application-specific integrated circuits (ASICs), programmable logic arrays, optical processors, programmable logic controllers, microcode, microcontrollers, servers, microprocessors, digital signal processors, any other suitable circuit, or any combination thereof.

[0124] The computing device 304 may include a memory device 308 in communication with the processor 306. The memory device 308 can include any type of memory capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a flash drive, a compact disc (CD), a digital video disc (DVD), a solid state drive (SSD), or any other suitable type of memory.

[0125] The computing device 304 may include an input device 310 in communication with the processor 306. Examples of the input device 310 include a keyboard, a keypad, a mouse, a microphone supported by speech- to-text software, or any other suitable input device.

[0126] The computing device 304 may include an output device 312 in communication with the processor 106. Examples of the output device 312 include a display screen, a speaker, or any other suitable output device, including haptic, tactile, olfactory, or gustatory ones, and without limitation, gesture recognition, gesture control, touchless user interfaces (TUIs), kinetic user interfaces (KUIs), tangible user interfaces, wired gloves, depth- aware cameras, stereo cameras, and gesture-based controllers. In some embodiments, such as where the computing device 304 includes a touchscreen, the input device 310 and the output device 312 may be the same device.

[0127] The computing device 304 may include a network adapter 314 in communication with the processor 306 for communicating with remote computers and/or servers. The network adapter 314 may include wired or wireless network adapter devices.

[0128] FIG. 3 is not intended to be limiting; the clinic server system 300, the clinic server 302, and the computing device 304 may include more or fewer components than those illustrated in FIG. 3.

[0129] FIG. 4 illustrates a component diagram and method of an illustrative medical claim processing system 400 and information flow according to aspects of this disclosure. The medical claim processing system 400 may include the medical system 100. The medical claim processing system 400 may include a clinic server 302. [0130] The medical claim processing system 400 may include a patient notes database 402. The medical claim processing system 400 may include an electronic medical records (EMR) database 404. One or both of the patient notes database 402 and the EMR database 404 may be located on the clinic server 302, on one or more remote servers, or on any other suitable system or server. [0131] The medical claim processing system 400 may include a biller server 406. The biller server 406 may be owned or controlled by a medical practice group (such as a testing company, outpatient procedure clinic, diagnostic company, or a hospital), a health insurance company, a governmental entity, or any other organization (including third-party organizations) associated with medical billing procedures. The biller server 406 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, any other suitable computing device, or any combination of the above. The biller server 406 may be cloud-based or be a real-time software platform, and it may include privacy (e.g., anonymization, pseudonymization, or other) software or protocols, and/or include security software or protocols. The biller server 406 may contain a computing device including any combination of the components of the computing device 304 as illustrated in FIG. 3. The biller server 406 may be proximate to or remote from the clinic server 302.

[0132] The medical claim processing system 400 may include a claim adjudication server 408. The claim adjudication server 408 may be owned or controlled by a health insurance company, governmental entity, or any other organization (including third-party organizations) associated with medical billing procedures. The claim adjudication server 408 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, any other suitable computing device, or any combination of the above. The claim adjudication server 408 may be cloud- based or be a real-time software platform, and it may include privacy (e.g., anonymization, pseudonymization, or other) software or protocols, and/or include security software or protocols. The claim adjudication server 408 may contain a computing device including any combination of the components of the computing device 304 as illustrated in FIG. 3. The claim adjudication server 408 may be proximate to or remote from the biller server 406. The claim adjudication server 408 may be configured to make or receive a determination about whether a claim should be paid.

[0133] At step 410, device-generated information may be transmitted from the medical system 100 to the clinic server 302. The device-generated information may include medical result information generated by the medical device 102. Medical result information can include information pertaining to a patient's medical condition and can include, without limitation, medical test results. Such medical test results can include, without limitation, CT scans, X-ray images, blood test results, and/or biopsy results. For example, the CT scans may include medical result information pertaining to a patient’s medical condition.

[0134] The device-generated information may include medical coding information generated by the medical device 102. Medical coding information refers, without limitation, to medical information represented by a code, such as a DRG, an ICD-10 code, or other codes embodying, representing, or encoding information related to a medical procedure or a medical test. Medical coding information can be device-based. Such device-based medical coding information (e.g., an ICD-10 code) can be derived from device-generated information (e.g., a CT scan), produced by, or otherwise sourced from a medical device (e.g., CT machine) or actions performed by, with, or on a medical device (e.g., rehabilitation on a physical therapy cycle). Device-based medical coding information can be used to supplement and/or replace medical coding information from alternative sources (e.g., remote databases, billing agencies, etc.). In an example where telemedicine is used to facilitate or mediate blood glucose testing on a patient using a glucose meter located at the patient’ s home or carried by the patient, device generated information may include medical coding information (e.g., an ICD-10 code) indicating that a procedure, such as a blood glucose test, was performed by using a glucose meter and that the glucose meter generated blood glucose test results. In this example, the glucose meter (a medical device) produces device generated information when it produces the blood glucose test results and when it generates the medical coding information. The medical system 100 and the clinic server 302 may be communicatively coupled via the network adapter 114. The device-generated information may be transmitted from the medical system 100 to the clinic server 302 via, for example, the network adapter 114.

[0135] At step 412, clinic server information may be transmitted from clinic server 302 to the patient notes database 402. The clinic server information may include medical result information generated by the medical device 102. Using the device-generated information received from the medical device 10, the clinic server information may include device-based medical result information determined by the clinic server 302. The clinic server information may include medical coding information generated by the medical device 102. Using the device-generated information received from the medical device 102, the clinic server information may include device-based medical coding information determined by the clinic server 302, including a DRG, an ICD-10, or another code generated by the medical device 102 and determined to be valid by the clinic server 302. The clinic server information may include an ICD-10 code that is determined using an analysis of the device-generated information. An example of determining the ICD-10 code using the analysis of the device-generated information includes the following: 1) the clinic server 302 identifies the image from the MRI scan as being an MRI scan of an upper spine of a patient, and 2) the clinic server 302 determines the ICD-10 code associated with the MRI scan of the upper spine. A clinic operator may also enter additional information into the patient notes database 402. For example, a doctor may enter additional medical result information and additional medical coding information into the patient notes database 402.

[0136] At step 414, patient notes information may be transmitted from the patient notes database 402 to the EMR database 404. The patient notes information may include medical result information generated by the medical device 102. Using the device-generated information received from the medical device 102, the patient notes information may include device-based medical result information determined by the clinic server 302. The patient notes information may include reviewed medical coding information (i.e., medical coding information that has been reviewed and/or input by a clinic operator). The patient notes information may include additional medical coding or result information entered by the clinic operator. A clinic operator may make a decision about what patient notes information should be or is transmitted from the from the patient notes database 402 to the EMR database 404. For example, a clinic operator may determine that it is not necessary to transmit a portion of the medical result information generated by the medical device 102, as, e.g., that information could be test data that does not reflect factual medical results of tests performed on a patient.

[0137] At step 416, EMR information may be transmitted from the EMR database 404 to the biller server 406. The EMR information may include medical result information generated by the medical device 102. Using the device-generated information received from the medical device 102, the EMR information may include device- based medical result information determined by the clinic server 302. The EMR information may include reviewed medical coding information. The EMR information may include additional medical result information entered by the clinic operator. The EMR information may include additional medical coding information entered by the clinic operator. A clinic operator may make a decision about which EMR information is transmitted from the EMR database 404 to the biller server 406. For example, a clinic operator may determine that it is not necessaiy to transmit a portion of the medical result information generated by the medical device 102, as, e.g., that information may be test data that does not reflect factual medical results of tests performed on a patient. A biller operator may enter biller notes into the biller server 406.

[0138] At step 418, biller information may be transmitted from the biller server 406 to the claim adjudication server 408. The biller information may include medical result information generated by the medical device 102. Using the device-generated information received from the medical device 102, the biller information may include device-based medical result information determined by the clinic server 302. The biller information may include reviewed medical coding information. The biller information may include additional medical result information entered by the clinic operator. The biller information may include additional medical coding information entered by the clinic operator. The biller information may include biller notes entered by the biller operator. For example, a biller operator may enter biller notes to pay in full, pay a reduced amount, reject the bill, or add any other suitable note. A biller operator may make a decision about which biller information is transmitted from the biller server 406 to the claim adjudication server 408. For example, the biller operator may determine that certain biller information is suspect and requires additional review, flag that biller information for review, and not send that information to claim adjudication.

[0139] At step 420, clinic server information may be transmitted from the clinic server 302 to the EMR database 404. The clinic server information may include medical result information generated by the medical device 102. Using the device-generated information received from the medical device 102, the clinic server information may include device-based medical result information determined by the clinic server 302. The clinic server information may include medical coding information generated by the medical device 102. Using the device-generated information received from the medical device 102, the clinic server information may include device-based medical coding information determined by the clinic server 302. Bypassing the patient notes database 402 may allow for unmodified clinic server information to be entered into the electronic medical records.

[0140] At step 422, information may be transmitted from the EMR database 404 to the clinic server 302. The information may include additional medical result or coding information entered by the clinic operator. The information may include reviewed medical coding information approved or modified by the clinic operator. The clinic server 302 may cross-reference the medical coding information that was sent by the clinic server 302 and the information that was received by the clinic server 302. The clinic server 302 may determine whether the medical coding information received by the clinic server 302 can be reconciled with the medical coding information sent by the clinic server 302. In the context of this application, "reconciled" or "reconcilable" means that the medical coding information received by the clinic server 302 and the medical coding information sent by the clinic server 302 are not contradictor . For instance, if a knee injury is indicated, but an elbow surgery is performed, the two are not reconcilable. However, if an elbow injury is indicated, and an elbow surgery is performed, the two are reconcilable.

[0141] At step 424, information may be transmitted from the clinic server 302 to the claim adjudication server 408. The information may include medical result information generated by the medical device 102. Using the device-generated information received from the medical device 102, the information may include device-based medical result information determined by the clinic server 302. The information may include medical coding information generated by the medical device 102. Using the device-generated information received from the medical device 102, the information may include device-based medical coding information determined by the clinic server 302. The information may include additional medical result and coding information entered by the clinic operator. The information may include reviewed medical coding information. The information may include the determination about whether the medical coding information received by the clinic server 302 can be reconciled with the medical coding information sent by the clinic server 302. By transmitting the device- based medical coding information directly to the claim adjudication server 408, fewer total data packets may be transmitted, reducing total processor and network loads. This is as compared to current systems, where all medical coding information is transmitted a multitude of times: through, inter alia, patient notes databases 402, EMR databases 404, biller servers 406, and finally claim adjudication servers 408.

[0142] FIG. 4 is not intended to be limiting; medical claim processing system 400 and any sub-components thereof may include more or fewer components, steps, and/or processes than those illustrated in FIG. 4. Any or all of the methods described may be implemented during a telemedicine session or at any other desired time. [0143] FIG. 5 illustrates a component diagram of an illustrative medical claim processing system 500 according to aspects of this disclosure. The medical claim processing system 500 can include the medical system 100 of FIG. 1. The medical system 100 may be in communication with a network 502. The network 502 may be a public network (e.g., connected to the Internet via wired (Ethernet) or wireless (Wi-Fi)), a private network (e.g., a local area network (LAN) or wide area network (WAN)), a combination thereof, or any other suitable network.

[0144] The medical claim processing system 500 can include the clinic server 302 of FIG. 3. The clinic server 302 may be in communication with the network 502.

[0145] The medical claim processing system 500 can include a cloud-based learning system 504. The cloud- based learning system 504 may be in communication with the network 502. The cloud-based learning system 504 may include one or more training servers 506 and form a distributed computing architecture. Each of the training servers 506 may include a computing device, including any combination of one or more of the components of the computing device 304 as illustrated in FIG. 3, or any other suitable components. The training servers 506 may be in communication with one another via any suitable communication protocol. The training servers 506 may store profiles for users including, but not limited to, patients, clinics, practice groups, and/or insurers. The profiles may include information such as historical device-generated information, historical device-based medical coding information, historical reviewed medical coding information, historical computer- based determinations on whether the reviewed medical coding information can be reconciled with the device- based coding information, and historical human-based determinations on whether the reviewed medical coding information has been reconciled with the device-based coding information. Other non-limiting examples of desired historical information can include any information relating to a specific patient, condition, or population that was recorded at a time prior to the interaction being billed as the medical claim.

[0146] In some aspects, the cloud-based learning system 504 may include a training engine 508 capable of generating one or more machine learning models 510. The machine learning models 510 may be trained to generate “determination” algorithms that, using the device-generated information, aid in determining device- based medical coding information. For instance, if the medical device 102 is an MRI, the machine learning models 510 may generate progressively more accurate algorithms to determine, using device-generated information such as MRI images, which type of MRI was performed and which type of medical coding information to associate with the type of MRI performed. To generate the one or more machine learning models 510, the training engine 508 may train the one or more machine learning models 510. The training engine 508 may use a base data set of historical device-generated information, historical device-based medical coding information, historical reviewed medical coding information, any other desired historical information and/or historical computer-based or human-based determinations on whether the reviewed medical coding information can be reconciled with the device-based coding information. The training engine 508 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, an Internet of Things (IoT) node or sensor, any other suitable computing device, or any combination of the above. The training engine 508 may be cloud-based or be a real time software platform, include privacy-enhancing, privacy-preserving or privacy modifying software or protocols, and/or include security software or protocols. Using training data that includes training inputs and corresponding target outputs, the one or more machine learning models 510 may refer to model artifacts created by the training engine 508. The training engine 508 may find patterns in the training data that map the training input to the target output and generate the machine learning models 510 that identify, store, or use these patterns. Although depicted separately from the medical system 100, the clinic server 302, the biller server 406, the claim adjudication server 408, the training engine 508, and the machine learning models 510 may reside on the medical system 100. Alternatively, the clinic server 302, the biller server 406, the claim adjudication server 408, the training engine 508, and the machine learning models 510 may reside on the clinic server 302, the biller server 406, the claim adjudication server 408, and/or any other suitable server.

[0147] The machine learning models 510 may include one or more neural networks, such as an image classifier, a recurrent neural network, a convolutional network, a generative adversarial network, a fully connected neural network, any other suitable network, or any combination thereof. In some embodiments, the machine learning models 510 may be composed of a single level of linear or non-linear operations or may include multiple levels of non-linear operations. For example, the machine learning models 510 may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neural nodes. [0148] FIG. 5 is not intended to be limiting; the medical claim processing system 500, the medical system 100, the computing device 104, the clinic server 302, the clinic server 302, the computing device 304, the cloud- based learning system 504, and any sub-components thereof may include more or fewer components than those illustrated in FIG. 5. Any or all of the methods described may be implemented during a telemedicine session or at any other desired time.

[0149] FIG. 6 illustrates a computer-implemented method 600 for a clinic server 302 processing medical claims. The method 600 may be implemented on a system including a processor, such as the processor 306, and a memory device, such as the memory device 308. The method 600 may be implemented on a processor configured to perform the steps of the method 600. The method 600 may be implemented on the clinic server system 300. The method 600 may include operations implemented in computer instructions stored in a memory device, such as the memory device 308, and executed by a processor, such as the processor 306, of a computing device, such as the computing device 304. The steps of the method 600 may be stored in a non-transient computer-readable storage medium. [0150] At step 602, the method 600 can include receiving device-generated information from a medical device. The medical device may include the medical device 102 and/or the medical system 100. The device-generated information may include medical coding and/or medical result information.

[0151] At step 604, the method 600 can include, using the device-generated information, determining device- based medical coding information. This determination can include cross-referencing information about actions performed by the medical device 102 with a reference list associating those actions with certain medical codes. The reference list could be stored on the clinic server 302, the cloud-based learning system 504, or on any other suitable server, database, or system. Furthermore, this determination can include identifying a portion of the device-generated information containing medical coding information.

[0152] At step 606, the method 600 can include, using the information, determining device-based medical result information. This determination can include determining that the device-generated information includes test results (e.g., a blood glucose measurement, a cholesterol measurement, etc.), medical imaging data (e.g., an X-Ray image, anMRI image, etc.), orphysical therapy (or rehabilitation) measurements (e.g., heart-rate, oxygen content, etc.).

[0153] At step 608, the method 600 can include transmitting the device-based medical result information to a patient notes database. The patient notes database can include the patient notes database 402.

[0154] At step 610, the method 600 can include transmitting the device-based medical coding information to the EMR database. The EMR database can include the EMR database 404.

[0155] At step 612, the method 600 can include receiving reviewed medical coding information from the EMR database. The EMR database can include the EMR database 404.

[0156] At step 614, the method 600 can include, using the reviewed medical coding information and the device-based medical coding information, determining a match indicator. The match indicator indicates whether the reviewed medical coding information can be reconciled with the device-based medical coding information. This determination can include cross-referencing the reviewed medical coding information with the device- based medical coding information to generate the match indicator. For example, both the reviewed medical coding information and the device-based medical coding information may be cross-referenced with a database to determine which reviewed medical coding information is and is not reconcilable with the device-based medical coding information. For instance, device-based medical coding information for a CT scan of a knee may be reconcilable with reviewed medical coding information of a patient being fitted for a knee brace, but not reconcilable with reviewed medical coding information of a patient being fitted for an elbow brace.

[0157] At step 616, the method 600 can include transmitting the device-based medical coding information directly to a claim adjudication server while bypassing the EMR database 404 and the biller server 406. The claim adjudication server may be the claim adjudication server 408. By transmitting the device-based medical coding information directly to the claim adjudication server 408, fewer total data packets may be sent, reducing network loads.

[0158] At step 618, the method 600 can include transmitting the determination of whether the reviewed medical coding information can be reconciled with the device-based medical coding information. The determination may be transmitted to the claim adjudication server 408. The determination may be transmitted to the biller server. The biller server may be the biller server 406. The determination may be transmitted to the patient notes database 402 or the EMR database 404 and/or to a personal computer or mobile device of a clinic operator.

[0159] FIG. 6 is not intended to be limiting; the method 600 can include more or fewer steps and/or processes than those illustrated in FIG. 6. Any or all of the steps of method 600 may be implemented during a telemedicine session or at any other desired time.

[0160] FIG. 7 illustrates a computer-implemented method 700 for a medical system processing medical claims. The method 700 may be implemented on a system including a processor, such as the processor 106, and a memory device, such as the memory device 108. The method 700 may be implemented on a processor configured to perform the steps of the method 700. The method may be implemented on the medical system 100. The method 700 may include operations that are implemented in computer instructions stored in a memory device, such as the memory device 108, and executed by a processor, such as the processor 106, of a computing device, such as the computing device 104. The steps of the method 700 may be stored in a non-transient computer-readable storage medium.

[0161] At step 702, the method 700 can include transmitting the device-generated information to a clinic server. The clinic server may be the clinic server 302. The medical device may include the medical device 102. The medical device may include the medical system 100. The device-generated information may include medical coding or medical result information.

[0162] At step 704, the method 700 can include transmitting device-generated information to the clinic server 302.

[0163] At step 706, the method 700 can include causing the clinic server 302, using the device-generated information, to determine device-based medical coding information. This determination can include cross- referencing information about actions performed by the medical device 102 with a reference list associating those actions with certain medical codes and/or identifying a portion of the device-generated information containing medical coding information.

[0164] At step 708, the method 700 can include causing the clinic server 302 to determine device-based medical result information using the device-generated information. This determination can include determining that the device-generated information includes test results (such as a blood glucose measurement).

[0165] At step 710, the method 700 can include causing the clinic server 302 to transmit the medical result information to a patient notes database. The patient notes database can include the patient notes database 402. [0166] At step 712, the method 700 can include causing the clinic server 302 to transmit the device-based medical coding information to the EMR database. The EMR database can include the EMR database 404. [0167] At step 714, the method 700 can include causing the clinic server 302 to receive from the EMR database 404 reviewed medical coding information.

[0168] At step 716, using the reviewed medical coding information and the device-based medical coding information, the method 700 can include causing the clinic server 302 to determine a match indicator. The match indicator indicates whether the reviewed medical coding information can be reconciled with the device-based medical coding information. Examples of such determination include (i) cross-referencing the reviewed medical coding information with the device-based medical coding information and/or (ii) cross-referencing both the reviewed medical coding information and the device-based medical coding information with a database to generate the match indicator. For instance, the device-based medical coding information for a CT scan of a knee may be reconcilable with reviewed medical coding information of patient being fitted for a knee brace, but not reconcilable with reviewed medical coding information of a patient being fitted for an elbow brace.

[0169] At step 718, the method 700 can include causing the clinic server 302 to transmit the device-based medical coding information to a claim adjudication server while bypassing the EMR database 404 and the biller server 406. The claim adjudication server may be the claim adjudication server 408. By transmitting the device- based medical coding information directly to the claim adjudication server 408, fewer total data packets may be sent, reducing network loads.

[0170] At step 720, the method 700 can include causing the clinic server 302 to transmit the determination. The determination may be transmitted to the claim adjudication server. The claim adjudication server may be the claim adjudication server 408. The determination may be transmitted to the biller server. The biller server may be the biller server 406. The determination may be transmitted to the patient notes database 402 or the EMR database 404. The determination may be transmitted to a personal computer or mobile device of a clinic operator. [0171] FIG. 7 is not intended to be limiting; the method 700 can include more or fewer steps and/or processes than those illustrated in FIG. 7. Any or all of the steps of method 700 may be implemented during a telemedicine session or at any other desired time.

[0172] FIG. 8 shows an example computer system 800 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 800 may include a computing device and correspond to an assistance interface, a reporting interface, a supervisory interface, a clinician interface, a server (including an AI engine), a patient interface, an ambulatory sensor, a goniometer, a treatment device 10, a medical device 102, a pressure sensor, or any suitable component. The computer system 800 may be capable of executing instructions implementing the one or more machine learning models of the artificial intelligence engine. The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet, including via the cloud or a peer- to-peer network. The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a wearable (e.g., wristband), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[0173] The computer system 800 includes a processing device 802, a main memory 804 (e.g., read-only memory (ROM), flash memory, solid state drives (SSDs), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 806 (e.g., flash memory, solid state drives (SSDs), static random access memory (SRAM)), and a data storage device 808, which communicate with each other via a bus 810.

[0174] Processing device 802 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 802 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 802 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 802 is configured to execute instructions for performing any of the operations and steps discussed herein.

[0175] The computer system 800 may further include a network interface device 812. The computer system 800 also may include a video display 814 (e.g., a liquid crystal display (LCD), a light -emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), one or more input devices 816 (e.g., a keyboard and/or a mouse or a gaming-like control), and one or more speakers 818 (e.g., a speaker). In one illustrative example, the video display 814 and the input device(s) 816 may be combined into a single component or device (e.g., an LCD touch screen).

[0176] The data storage device 816 may include a computer-readable medium 820 on which the instructions 822 embodying any one or more of the methods, operations, or functions described herein is stored. The instructions 822 may also reside, completely or at least partially, within the main memory 804 and/or within the processing device 802 during execution thereof by the computer system 800. As such, the main memory 804 and the processing device 802 also constitute computer-readable media. The instructions 822 may further be transmitted or received over a network via the network interface device 812.

[0177] While the computer-readable storage medium 820 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer- readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0178] FIG. 8 is not intended to be limiting; the system 800 may include more or fewer components than those illustrated in FIG. 8.

[0179] The term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer- readable storage medium” shall also be taken to include any medium capable of storing, encoding or carrying a set of instructions for execution by the machine and causing the machine to perform any one or more of the methodologies of the present disclosure. The term “computer- readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0180] Any of the systems and methods described in this disclosure may be used in connection with rehabilitation. Unless expressly stated otherwise, is to be understood that rehabilitation includes prehabilitation (also referred to as "pre-habilitation" or "prehab"). Prehabilitation may be used as a preventative procedure or as a pre-surgical or pre-treatment procedure. Prehabilitation may include any action performed by or on a patient (or directed to be performed by or on a patient, including, without limitation, remotely or distally through telemedicine) to, without limitation, prevent or reduce a likelihood of injury (e.g., prior to the occurrence of the injury); improve recovery time subsequent to surgery; improve strength subsequent to surgery; or any of the foregoing with respect to any non-surgical clinical treatment plan to be undertaken for the purpose of ameliorating or mitigating injury, dysfunction, or other negative consequence of surgical or non-surgical treatment on any external or internal part of a patient's body. For example, a mastectomy may require prehabilitation to strengthen muscles or muscle groups affected directly or indirectly by the mastectomy. As a further non-limiting example, the removal of an intestinal tumor, the repair of a hernia, open-heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to excise them or parts of them, to treat them, etc., can require cutting through and harming numerous muscles and muscle groups in or about, without limitation, the abdomen, the ribs and/or the thoracic cavity. Prehabilitation can improve a patient's speed of recovery, measure of quality of life, level of pain, etc. in all the foregoing procedures. In one embodiment of prehabilitation, a pre-surgical procedure or a pre-non-surgical-treatment may include one or more sets of exercises for a patient to perform prior to such procedure or treatment. The patient may prepare an area of his or her body for the surgical procedure by performing the one or more sets of exercises, thereby strengthening muscle groups, improving existing and/or establishing new muscle memory, enhancing mobility, improving blood flow, and/or the like.

[0181] In some embodiments, the systems and methods described herein may use artificial intelligence and/or machine learning to generate a prehabilitation treatment plan for a user. Additionally, or alternatively, the systems and methods described herein may use artificial intelligence and/or machine learning to recommend an optimal exercise machine configuration for a user. For example, a data model may be trained on historical data such that the data model may be provided with input data relating to the user and may generate output data indicative of a recommended exercise machine configuration for a specific user. Additionally, or alternatively, the systems and methods described herein may use machine learning and/or artificial intelligence to generate other types of recommendations relating to prehabilitation, such as recommended reading material to educate the patient, a recommended health professional specialist to contact, and/or the like.

[0182] Consistent with the above disclosure, the examples of systems and method enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples.

[0183] Clause 1. A computer-implemented system for processing medical claims, comprising: a medical device configured to be manipulated by a user while the user performs a treatment plan; a patient interface associated with the medical device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session; and a processor configured to: during the telemedicine session, receive device-generated information from the medical device; using the device-generated information, determine device-based medical coding information; and transmit the device-based medical coding information to a claim adjudication server. [0184] Clause 2. The computer-implemented system of any clause herein, wherein, during the telemedicine session, the device-generated information is generated by the medical device. [0185] Clause 3. The computer-implemented system of any clause herein, wherein, using the device-generated information, the processor is further configured to determine device-based medical result information.

[0186] Clause 4. The computer-implemented system of any clause herein, wherein the processor is further configured to transmit the device-based medical result information to a patient notes database.

[0187] Clause 5. The computer-implemented system of any clause herein, wherein the processor is further configured to transmit the device-based medical coding information to an electronic medical records database. [0188] Clause 6. The computer-implemented system of any clause herein, wherein the processor is further configured to: receive reviewed medical coding information from an electronic medical records database, wherein, using the reviewed medical coding information and the device-based medical coding information, the processor is further configured to determine a match indicator; and transmit the match indicator to the claim adjudication server.

[0189] Clause 7. A system for processing medical claims, comprising: a processor configured to: receive device-generated information from a medical device; using the device -generated information, determine device-based medical coding information; and transmit the device-based medical coding information to a claim adjudication server.

[0190] Clause 8. The system of any clause herein, wherein the device-generated information is generated by the medical device.

[0191] Clause 9. The system of any clause herein, wherein, using the device-generated information, the processor is further configured to determine device-based medical result information.

[0192] Clause 10. The system of any clause herein, wherein the processor is further configured to transmit the device-based medical result information to a patient notes database.

[0193] Clause 11. The system of any clause herein, wherein the processor is further configured to transmit the device-based medical coding information to an electronic medical records database.

[0194] Clause 12. The system of any clause herein, wherein the processor is further configured to receive reviewed medical coding information from an electronic medical records database.

[0195] Clause 13. The system of any clause herein, wherein, using the reviewed medical coding information and the device-based medical coding information, the processor is further configured to determine a match indicator.

[0196] Clause 14. The system of any clause herein, wherein the processor is further configured to transmit the match indicator to the claim adjudication server.

[0197] Clause 15. The system of any clause herein, further comprising a memory device operatively coupled to the processor, wherein the memory device stores instructions, and wherein the processor is configured to execute the instructions..

[0198] Clause 16. A method for a clinic server processing medical claims, comprising: receiving device-generated information from a medical device; using the device-generated information, determining device-based medical coding information; and transmitting the device-based medical coding information to a claim adjudication server. [0199] Clause 17. The method of any clause herein, wherein the device-generated information is generated by the medical device.

[0200] Clause 18. The method of any clause herein, further comprising using the device-generated information to determine device-based medical result information.

[0201] Clause 19. The method of any clause herein, further comprising transmitting the device-based medical result information to a patient notes database.

[0202] Clause 20. The method of any clause herein, further comprising transmitting the device-based medical coding information to an electronic medical records database.

[0203] Clause 21. The method of any clause herein, further comprising receiving reviewed medical coding information from an electronic medical records database.

[0204] Clause 22. The method of any clause herein, further comprising determining, using the reviewed medical coding information and the device-based medical coding information, a match indicator.

[0205] Clause 23. The method of any clause herein, further comprising transmitting the match indicator to the claim adjudication server.

[0206] Clause 24. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processor to: receive device-generated information from a medical device; using the device-generated information, determine device-based medical coding information; and transmit the device-based medical coding information to a claim adjudication server.

[0207] Clause 25. The tangible, non-transitory computer-readable medium of any clause herein, wherein the device-generated information is generated by the medical device.

[0208] Clause 26. The tangible, non-transitory computer-readable medium of any clause herein, wherein, using the device-generated information, the instructions further cause the processor to determine device-based medical result information.

[0209] Clause 27. The tangible, non-transitory computer-readable medium of any clause herein, wherein the instructions further cause the processor to transmit the device-based medical result information to a patient notes database.

[0210] Clause 28. The tangible, non-transitory computer-readable medium of any clause herein, wherein the instructions further cause the processor to transmit the device-based medical coding information to an electronic medical records database.

[0211] Clause 29. The tangible, non-transitory computer-readable medium of any clause herein, wherein the processor is further configured to receive reviewed medical coding information from an electronic medical records database.

[0212] Clause 30. The tangible, non-transitory computer-readable medium of any clause herein, wherein, using the reviewed medical coding information and the device-based medical coding information, the instructions further cause the processor to determine a match indicator.

[0213] Clause 31. The tangible, non-transitory computer-readable medium of any clause herein, wherein the instructions further cause the processor to transmit the match indicator to the claim adjudication server.

[0214] Clause 32. A system for generating and processing medical billing codes, comprising: a medical device; and a computing device comprising a processor in communication with the medical device, wherein the processor is configured to: receive device-generated information from the medical device; transmit the device -generated information to a clinic server; using the device-generated information, cause the clinic server to determine device-based medical coding information; and cause the clinic server to transmit the device-based medical coding information to a claim adjudication server.

[0215] Clause 33. The system of any clause herein, wherein the device-generated information is generated by the medical device.

[0216] Clause 34. The system of any clause herein, wherein, using the device-generated information, the processor is further configured to cause the clinic server to determine device-based medical result information. [0217] Clause 35. The system of any clause herein, wherein the processor is further configured to cause the clinic server to transmit the device-based medical result information to a patient notes database.

[0218] Clause 36. The system of any clause herein, wherein the processor is further configured to cause the clinic server to transmit the device-based medical coding information to an electronic medical records database. [0219] Clause 37. The system of any clause herein, wherein the processor is further configured to cause the clinic server to receive reviewed medical coding information from an electronic medical records database. [0220] Clause 38. The system of any clause herein, wherein the processor is further configured to cause the clinic server to, using the reviewed medical coding information and the device-based medical coding information, determine a match indicator.

[0221] Clause 39. The system of any clause herein, wherein the processor is further configured to cause the clinic server to transmit the match indicator to the claim adjudication server.

[0222] Clause 40. The system of any clause herein, wherein the computing device is operatively coupled to the medical device.

[0223] Clause 41. The system of any clause herein, wherein the computing device is integral to the medical device.

[0224] Clause 42. The system of any clause herein, further comprising a memory device operatively coupled to the processor, wherein the memory device stores instructions, and wherein the processor is configured to execute the instructions..

[0225] Clause 43. A method for operating a medical device, the method comprising: receiving device-generated information from the medical device transmitting the device-generated information to a clinic server; using the device -generated information, causing the clinic server to determine device-based medical coding information; and causing the clinic server to transmit the device-based medical coding information to a claim adjudication server.

[0226] Clause 44. The method of any clause herein, wherein the device-generated information is generated by the medical device. [0227] Clause 45. The method of any clause herein, further comprising using the device-generated information to cause the clinic server to determine device-based medical result information.

[0228] Clause 46. The method of any clause herein, further comprising causing the clinic server to transmit the device-based medical result information to a patient notes database.

[0229] Clause 47. The method of any clause herein, further comprising causing the clinic server to transmit the device-based medical coding information to an electronic medical records database.

[0230] Clause 48. The method of any clause herein, further comprising causing the clinic server to receive reviewed medical coding information from an electronic medical records database.

[0231] Clause 49. The method of any clause herein, further comprising causing the clinic server to determine a match indicator.

[0232] Clause 50. The method of any clause herein, further comprising causing the clinic server to transmit the match indicator to the claim adjudication server by using the reviewed medical coding information and the device-based medical coding information.

[0233] Clause 51. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processor to: receive device-generated information from a medical device; transmit the device-generated information to a clinic server; using the device-generated information, cause the clinic server to determine device-based medical coding information; and cause the clinic server to transmit the device-based medical coding information to a claim adjudication server.

[0234] Clause 52. The tangible, non-transitory computer-readable medium if any clause herein, wherein the device-generated information is generated by the medical device.

[0235] Clause 53. The tangible, non-transitory computer-readable medium of any clause herein, wherein the instructions further cause the processor to cause the clinic server to determine device-based medical result information.

[0236] Clause 54. The tangible, non-transitory computer-readable medium of any clause herein, wherein the instructions further cause the processor to cause the clinic server to transmit the device-based medical result information to a patient notes database.

[0237] Clause 55. The tangible, non-transitory computer-readable medium of any clause herein, wherein the instructions further cause the processor to cause the clinic server to transmit the device-based medical coding information to an electronic medical records database.

[0238] Clause 56. The tangible, non-transitory computer-readable medium of any clause herein, wherein the instructions further cause the processor to cause the clinic server to receive reviewed medical coding information from an electronic medical records database.

[0239] Clause 57. The tangible, non-transitory computer-readable medium of any clause herein, wherein, using the reviewed medical coding information and the device-based medical coding information, the instructions further cause the processor to cause the clinic server to determine a match indicator. [0240] Clause 58. The tangible, non-transitor computer-readable medium of any clause herein, wherein the instructions further cause the processor to cause the clinic server to transmit the match indicator to the claim adjudication server.

[0241] No part of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) unless the exact words “means for” are followed by a participle.

[0242] The foregoing description, for purposes of explanation, use specific nomenclature to provide a thorough understanding of the described embodiments. However, it should be apparent to one skilled in the art that the specific details are not required in order to practice the described embodiments. Thus, the foregoing descriptions of specific embodiments are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. It should be apparent to one of ordinary skill in the art that many modifications and variations are possible in view of the above teachings.

[0243] The above discussion is meant to be illustrative of the principles and various embodiments of the present invention. Once the above disclosure is fully appreciated, numerous variations and modifications will become apparent to those skilled in the art. It is intended that the following claims be interpreted to embrace all such variations and modifications.

SYSTEM AND METHOD FOR TRANSMITTING DATA AND ORDERING ASYNCHRONOUS DATA

[0244] FIG. 12 illustrates a component diagram of an illustrative system 2100 for transmitting and ordering asynchronous data in accordance with aspects of this disclosure. The system 2100 may include an information generating device 2102. The information-generating device 2102 may be a medical device. The medical device may be a testing device, a diagnostic device, a therapeutic device, or any other suitable medical device. “Medical device” as used in this context may refer to hardware, software, or a mechanical or other device that may assist in a medical service, regardless of whether it is FDA (or other governmental regulatory body of any given country) approved, required to be FDA (or other governmental regulatory body of any given country) approved or available commercially or to consumers without such approval. Non-limiting examples of the medical devices include an insulin pump, a thermometer, an MRI machine, a CT-scan machine, a glucose meter, an apheresis machine, and a physical therapy machine (e.g., an orthopedic rehabilitation device, such as a physical therapy cycle). Non-limiting examples of places where the medical device may be located include a healthcare clinic, a physical rehabilitation center, and a user’s home to allow for telemedicine treatment, rehabilitation, and/or testing. FIG. 13 illustrates an example of the information-generating device 2102 in which the information generating device 2102 is a physical therapy cycle 2200.

[0245] The information-generating device 2102 may include an electromechanical device 2104, such as pedals 2202 of the physical therapy cycle 2200, a goniometer configured to attach to a joint and measure joint angles, or any other suitable electromechanical device. The electromechanical device 2104 may be configured to be manipulated by a patient while performing an exercise session. The electromechanical device 2104 may be configured to transmit information, such as pedal position information. A non-limiting example of positioning information includes information relating to the location of the electromechanical device 2104 (e.g., the pedals 2202).

[0246] The information-generating device 2102 may include a sensor 2106. The sensor 2106 can be used for obtaining information, such as fingerprint information, retina information, voice information, height information, weight information, vital sign information (e.g., blood pressure, heart rate, etc.), response information to physical stimuli (e.g., change in heart rate while running on a treadmill), performance information (rate of speed of rotation of the pedals 2202 of the physical therapy cycle 2200), or any other suitable information. The sensor 2106 may be a temperature sensor (such as a thermometer or thermocouple), a strain gauge, a proximity sensor, an accelerometer, an inclinometer, an infrared sensor, a pressure sensor, a light sensor, a smoke sensor, a chemical sensor, any other suitable sensor, a fingerprint scanner, a sound sensor, a microphone, or any combination thereof. The sensor2106 maybe located on an interior or exterior ofthe device. For example, the sensor 2106 may be a pedal position sensor located on the pedals 2202 of the physical therapy cycle 2200.

[0247] The information-generating device 2102 may include a camera 2108, such as a still image camera, a video camera, an infrared camera, an X-ray camera, any other suitable camera, or any combination thereof. The information-generating device 2102 may include an imaging device 2110, such as an MRI imaging device, an X-ray imaging device, a thermal imaging device, any other suitable imaging device, or any combination thereof. The information-generating device 2102 may include a device-side processor 2112. The device-side processor 2112 can include, for example, computers, intellectual property (IP) cores, application-specific integrated circuits (ASICs), programmable logic arrays, optical processors, programmable logic controllers, microcode, microcontrollers, servers, microprocessors, digital signal processors, any other suitable circuit, or any combination thereof. The device-side processor may be in communication with the electromechanical device 2104, the sensor 2106, the camera 2108, the imaging device 2110, any other suitable device, or any combination thereof.

[0248] The information-generating device 2102 may include a device-side memory 2114 in communication with the device-side processor 2112. The device-side memory 2114 can include any type of memory capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a flash drive, a compact disc (CD), a digital video disc (DVD), solid state drive (SSD), or any other suitable type of memory. The device-side memory 2114 may store instructions that cause the device-side processor 2112 to perform a series of actions or processes.

[0249] The information-generating device 2102 may include a device-side input 2116 in communication with the device-side processor 2112. Examples of the device-side input 2116 include a keyboard, a keypad, a mouse, a microphone supported by speech-to-text software, or any other suitable input device. The device-side input 2116 may be used by a medical system operator to input information, such as user-identifying information, observational notes, or any other suitable information. An operator is to be understood throughout this disclosure to include people, bots, robots, hardware, and/or computer software, such as programs or artificial intelligence, and any combination thereof.

[0250] The information-generating device 2102 may include a device-side output 2118 in communication with the device-side processor 2112. The device-side output 2118 may be used to provide information to the operator or a user (or patient) of the information-generating device 2102. For the purposes of this disclosure, user and patient are used interchangeably. Examples of the device-side output 2118 may include a display screen, a speaker, an alarm system, or any other suitable output device, including haptic, tactile, olfactory, or gustatory ones. In some embodiments, such as where the information-generating device 2102 includes a touchscreen, the device-side input 2116 and the device-side output 2118 may be the same device.

[0251] For communicating with remote computers and servers, the information-generating device 2102 may include a device-side network adapter 2120 in communication with the device-side processor 2112. The device side network adapter 2120 may include wired or wireless network adapter devices (e.g., a wireless modem or Bluetooth) or a wired network port.

[0252] The information-generating device 2102 may be coupled to or be in communication with a remote computing device 2122. The remote computing device 2122 may include a remote processor 2124. The remote processor 2124 can include, for example, computers, intellectual property (IP) cores, application-specific integrated circuits (ASICs), programmable logic arrays, optical processors, programmable logic controllers, microcode, microcontrollers, servers, microprocessors, digital signal processors, any other suitable circuit, or any combination thereof.

[0253] The remote computing device 2122 may include a remote memory 2126 in communication with the remote processor 2124. The remote memory 2126 can include any type of memory capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a flash drive, a compact disc (CD), a digital video disc (DVD), solid state drive (SSD), or any other suitable type of memory. The remote memory 2126 may store instructions that cause the remote processor 2124 to perform a series of actions or processes.

[0254] The remote computing device 2122 may include a remote input 2128 in communication with the remote processor 2124. Examples of the remote input 2128 include a keyboard, a keypad, a mouse, a microphone supported by speech-to-text software, or any other suitable input device. The remote input 2128 may be used by a medical system operator to input information, such as user-identifying information, observational notes, or any other suitable information. An operator is to be understood throughout this disclosure to include people, bots, robots, hardware, and/or computer software, such as programs or artificial intelligence, and any combination thereof.

[0255] The remote computing device 2122 may include a remote output 2130 in communication with the remote processor 2124. The remote output 2130 may be used to provide information to the operator or a user (or patient) of the remote computing device 2122. For the purposes of this disclosure, user and patient are used interchangeably. Examples of the remote output 2130 may include a display screen, a speaker, an alarm system, or any other suitable output device, including haptic, tactile, olfactory, or gustatory ones. In some embodiments, such as where the remote computing device 2122 includes a touchscreen, the remote input 2128 and the remote output 2130 may be the same device.

[0256] For communicating with the information-generating device 2102, as well as remote computers and servers, the remote computing device 2122 may include a remote network adapter 2132 in communication with the remote processor 2124. The remote network adapter 2122 may include wired or wireless network adapter devices (e.g., a wireless modem or Bluetooth) or a wired network port.

[0257] Both the device-side network adapter 2120 and the remote network adapter 2132 may be in communication with a network 2134. Transmissions between the information-generating device 102 and the remote computing device 2122 may pass through the network 2134. The network 2134 may be apublic network (e.g., connected to the Internet via wired (Ethernet) or wireless (Wi-Fi)), a private network (e.g., a local area network (LAN) or wide area network (WAN)), a combination thereof, or any other suitable network.

[0258] Any time information is transmitted or communicated, the information may be in EDI file format or any other suitable file format. In any of the processes or steps of the method, file format conversions may take place. By utilizing Internet of Things (IoT) devices or gateways, data streams, ETL bucketing, EDI mastering, or any other suitable technique, data can be mapped, converted, translated, or transformed into a carrier-preferred state. As a result of the volume of data being transmitted, the data security requirements, and the data consistency requirements, an enterprise grade architecture may be utilized for reliable data transfer.

[0259] FIG. 12 is not intended to be limiting: the system 2100, the information-generating device 2102, and the remote computing device 2122 may include more or fewer components than those illustrated in FIG. 12. [0260] FIGS. 14A and 14B illustrate a computer-implemented method 2300 for transmitting data and ordering asynchronous data. The method 2300 may be performed by the system 2100 using the information-generating device 2102 and the remote computing device 2122. The method 2300 may be implemented on a pair of processors, such as the device-side processor 2112 and the remote processor 2124, which are together configured to perform the steps of the method 2300. The method 2300 may include operations implemented in instructions stored on one or more memory devices, such as the device-side memory 2114 and the remote memory 2126, and be executed by one or more processors, such as the device-side processor 2112 and the remote processor 2124. The steps of the method 2300 may be stored in one or more non-transient computer-readable storage media.

[0261] At step 2302, the method 2300 includes, at the information-generating device 2102, receiving data. For example, the device-side processor 2112 can receive data from the electromechanical device 2104, the sensor 2106, the camera, 2108, the imaging device 2110, the device-side input 2116, or any other suitable device. As a more specific example, the device-side processor 2112 may receive an MRI image from an MRI imaging device (i.e., the imaging device 2110). The data may be received as a stream of data. The stream of data may be a continuous stream of data. The device-side processor 2112 may initially receive the data as a digital signal, an analog signal, or any other suitable signal. The device-side processor 2112 may convert data from an analog signal to a digital signal.

[0262] At step 2304, the method 2300 includes, at the information-generating device, generating a map packet. The map packet contains data mapping information that indicates a means, a method, an approach or another mechanism for receiving the continuity packets. In some embodiments, the map packet includes end-of-file information that function as information against which data from later-received continuity packets can be compared for determining whether data transmission for a given file has ended. For example, the map packet may contain data mapping information indicating that the continuity packets will have a header following the format of “AA######AA”, and an end-of-file continuity packet will have an end-of-file header following the format of “AA######ZZ”. In this example, “######” indicates a numerical value starting at “000000” and going to a possible maximum of “999999” and “ZZ” functions as an end tag to indicate that the tagged continuity packet is the final continuity packet of the given file. [0263] At step 2306, the method 2300 includes, at the information-generating device, transmitting the map packet. For example, the device-side processor 2112 may direct the device-side network adapter 120 to transmit the map packet to the remote network adapter 2132 of the remote computing device 2122.

[0264] At step 2308, the method 2300 includes, at the information-generating device, generating the continuity packets. Each of the continuity packets is a data packet that includes a contiguous portion of the data. The continuity packets may be generated using the data. For example, the device-side processor 2112 may take a contiguous portion of the data and place that contiguous portion into one of the continuity packets. One or more of the continuity packets may include header information that the processor can use to order the continuity packets. For example, a first continuity packet may include a first header including first header information of “AA000000AA”, and a second continuity packet may include a second header including second header information of “AA000001AA”. A contiguous portion of the data mapping information of the map packet may correspond to a contiguous portion of the header information. For example, the header information may include a contiguous portion of data, including the string “AA”. The string “AA” corresponds with a portion of the mapping information of the map packet, thereby indicating that header information of relevant continuity packets will contain the string “AA”. The header information may also include information pertaining to the portion of data contained in the continuity packet. The information-generating device generates the continuity packets in an initial order; however, a remote computing device 2122 may not receive the continuity packets in the initial order (e.g., a first continuity packet may be generated first and a second continuity packet may be generated second, but the second packet may be received before the first packet is received). Thus, the header information may include information that the remote computing device 2122 can use to order (e.g., reassemble) the continuity packets, such as the initial order that the continuity packets were generated. The header information of an end-of-file continuity packet can include an end tag corresponding to a contiguous portion of the end-of-file information. For example, an end-of-file continuity packet may include end-of-file header information of “AA000002ZZ”, where “ZZ” functions as the end tag. The generation of the continuity packets may occur all at once or be spread out over time as more data is received, so the end-of file header information is used to indicate an end of the data stream.

[0265] At step 2310, the method 2300 includes, at the information-generating device, transmitting the continuity packets. For example, the device-side processor 2112 may direct the device-side network adapter 2120 to transmit the continuity packets to the remote network adapter 2132 of the remote computing device 2122. This transmission may occur after all continuity packets have been generated, as the continuity packets are being generated, or any combination thereof. In cases where the generation of the continuity packets is spread out over time as more data is received, the generation and the transmission of the continuity packets allow for a reduced memory requirement and reduced peak network loads relative to first waiting for all of the data to be received. For instance, if, before generating the continuity packets, the information-generating device waits until all of the data is received (e.g., from the sensors), the device-side memory 2114 may have to store the entirety of the data (i.e., which may require a substantial amount of memory to store an extremely large file), rather than temporarily storing a portion of the data while the device-side processor 2112 generates and transmits each continuity packet. Similarly, if, before transmitting the continuity packets, the information-generating device waits until all of the data has been received and all of the continuity packets have been generated, the network loads required for the transmission may be higher because a larger amount of data is being transmitted at once (e.g., all of the continuity packets are being transmitted in a short time period).

[0266] At step 2312, the method 2300 includes, at the remote computing device (e.g., the remote computing device 2122), receiving the map packet. The map packet may be received from the information-generating device 2102. For example, the remote computing device 2122 may receive the map packet by way of the remote network adapter 2132.

[0267] At step 2314, the method 2300 includes, at the remote computing device, receiving continuity packets in an initial order. The continuity packets may be received from the information-generating device 2102. For example, continuity packets may be received by the remote computing device 2122 by way of the remote network adapter 2132 in an initial order wherein the second continuity packet is received first, the first continuity packet is received second, and the end-of-file continuity packet is received third.

[0268] At step 2316, the method 2300 includes, at the remote computing device, generating an output file. Responsive to receiving at least two of the continuity packets and the map packet, the map packet may be used to generate an output file. The output file may be generated by ordering the continuity packets from the initial order into an output order. For example, given the initial order described above in step 2314, the remote processor 2124 may order the continuity packets, or contiguous portions of the continuity packets corresponding to contiguous portions of the data, into an output order. The output order may be as follows: 1) the first continuity packet, 2) the second continuity packet, and 3) the end-of-file continuity packet. In some embodiments, as the remote processor receives the continuity packets, the remote processor may contemporaneously generate the output file. For example, the remote computing device 2124 may receive the second continuity packet first and the first continuity packet second, but not yet have received the end-of-file continuity packet, in which case the remote processor 2124 may order the continuity packets into an output order having the first continuity packet first and the second continuity packet second. In some embodiments, while the output file is being generated, the continuity packets are configured to be readable by external processes. Examples of such external processes include maintenance processes configured to check for device maintenance status or error messages. Such external processes may be able to read and/or respond to maintenance requests or errors prior to ordering, such that an error message contained in the continuity packets can be read prior to completing the generation of the output file. For example, if a patient is undergoing a CT scan performed by a CT scanner, a processor may monitor and read the data in real-time or near real-time to detect an error message. In this example, if the CT scanner generates a continuity packet containing an error message indicating a fault with the CT scanner (e.g., the data obtained by the CT scanner will be unusable), then, at the direction of such an external monitoring process, the remote processor 124 may read the error message prior to ordering and generating the output file and stop the CT scanner during the CT scan. Stopping the CT scan prior to its completion would limit the patient’s unnecessary exposure to X-rays, as any exposure after the error may not result in usable data.

[0269] At step 2318, the method 2300 may include, at the remote computing device, using the end tag to generate an end-of-file indicator. For example, a flag may be used or a variable may be set as an end-of-file indicator when the end-of-file continuity packet containing the end tag “ZZ” is received (i.e., the remote processor may change a variable “end-of-file-reached” from “false” to “true”).

[0270] At step 2320, the method 2300 may include using the header information, the map packet, and the end- of-file indicator to determine whether any continuity packets remain to be received. For example, if the first continuity packet containing the first header information of “AA000000AA” and the end-of-file continuity packet containing the end-of-file header information “AA000002ZZ” (and thus the end tag “ZZ”) have been received, the remote processor 2124 may determine that the second continuity packet has not been received. If any continuity packets remain to be received, the method 2300 proceeds to step 2322. If all continuity packets have been received, the method 2300 proceeds to step 2330.

[0271] At step 2322, if any continuity packets remain to be received, the method 2300 may include determining a non-zero wait time period. For example, if the second continuity packet has not been received, the remote processor 2124 may determine a wait time period. The wait time period may be between two seconds and ten seconds, or any other suitable period of time.

[0272] At step 2324, the method 2300 may include, at the remote computing device, determining if any continuity packets were received within the wait time period. For example, if the second continuity packet, which had not been previously received, is received within the wait time period, the remote computing device may determine that a continuity packet was received within the wait time period, subsequent to which the method 2300 proceeds to step 2326. However, if the second continuity packet is not received within the wait time period, the remote processor 2124 may determine that the continuity packet was not received within the wait time period, subsequent to which the method 3200 proceeds to step 2328.

[0273] At step 2326, responsive to receiving another continuity packet within the non-zero wait time period, the method 2300 may include the remote computing device continuing to generate the output file. For example, if the determination is that the second continuity packet that had not been previously received is received within the wait time period, then the remote processor 2124 may continue generating the output file. The method 2300 may return to step 2320.

[0274] At step 2328, responsive to determining the non-zero wait time period and not receiving another continuity packet within the non-zero wait time period, the method 2300 may include the remote computing device transmitting an error signal. For example, if the determination is that the second continuity packet that had not been previously received was not received within the wait time period, the remote processor 2124 may direct the remote network adapter 2132 to transmit an error message and/or the remote output 2130 to present the error message (e.g., “Error: Incomplete Data”).

[0275] At step 2330, responsive to determining that every continuity packet has been received, the method 2300 includes transmitting the output file. For example, if the first continuity packet, the second continuity packet, and the end-of-file continuity packet have been received and ordered (e.g., into an output file), the remote processor 2124 may direct the remote network adapter 2132 to transmit the output file via the network 2134. [0276] FIG. 15 illustrates a computer-implemented method 2400 for transmitting data. U sing the information generating device 2102, the method 2400 may be performed by the system 2100. The method 2400 may be implemented on a processor, such as the device-side processor 2112 configured to perform the steps of the method 2400. The method 2400 may include operations implemented in instructions stored on a memory device, such as the device-side memory 2114 executed by a processor, such as the device-side processor 2112. The steps of the method 3200 may be stored on a non-transient computer-readable storage medium.

[0277] At step 2402, the method 2400 includes, at the information-generating device (e.g., the information generating device 2102), receiving data. For example, the device-side processor 2112 can receive data from the electromechanical device 2104, the sensor 2106, the camera 2108, the imaging device 2110, the device-side input 2116, or any other suitable device. As a more specific example, the device-side processor 2112 may receive an MRI image from an MRI imaging device (i.e., the imaging device 2110). The data may be received as a stream of data. The stream of data may be a continuous stream of data. The device-side processor 2112 may initially receive the data as a digital signal, an analog signal, or any other suitable signal. The device-side processor 2112 may convert data from an analog signal to a digital signal.

[0278] At step 2404, the method 2400 includes, at the information-generating device, generating a map packet. The map packet contains data mapping information that indicates a means, a method, an approach, or another mechanism for receiving the continuity packets. In some embodiments, the map packet includes end-of-file information that function as information against which data from later-received continuity packets can be compared for determining whether data transmission for a given file has ended. For example, the map packet may contain data mapping information indicating that the continuity packets will have a header following the format of “AA######AA”, and an end-of-file continuity packet will have an end-of-file header following the format of “AA######ZZ”. In this example, “######” indicates a numerical value starting at “000000” and going to a possible maximum of “999999” and “ZZ” functions as an end tag to indicate that the tagged continuity packet is the final continuity packet of the given file.

[0279] At step 2406, the method 2400 includes, at the information-generating device, transmitting the map packet. For example, the device-side processor 2112 may direct the device-side network adapter 2120 to transmit the map packet to the remote network adapter 2132 of the remote computing device 2122.

[0280] At step 2408, the method 2400 includes, at the information-generating device, generating the continuity packets. Each of the continuity packets is a data packet that includes a contiguous portion of the data. The continuity packets may be generated using the data. For example, the device-side processor 2112 may take a contiguous portion of the data and place that contiguous portion into one of the continuity packets. One or more of the continuity packets may include header information that the processor can use to order the continuity packets. For example, a first continuity packet may include a first header including first header information of “AA000000AA”, and a second continuity packet may include a second header including second header information of “AA000001AA”. A contiguous portion of the data mapping information of the map packet may correspond to a contiguous portion of the header information. For example, the header information may include a contiguous portion of data including the string “ AA” . The string “ AA” corresponds to a portion of the mapping information of the map packet, indicating that header information of relevant continuity packets will contain the string “AA”. The header information may also include information pertaining to the portion of data contained in the continuity packet. The information-generating device generates the continuity packets in an initial order; however, a remote computing device 2122 may not receive the continuity packets in the initial order (e.g., a first continuity packet may be generated first and a second continuity packet may be generated second, but the second packet may be received before the first packet has been received). Thus, the header information may include information that the remote computing device 2122 can use to order (e.g., reassemble) the continuity packets, such as the initial order that the continuity packets were generated. The header information of an end-of-file continuity packet can include an end tag corresponding to a contiguous portion of the end-of-file information. For example, an end-of-file continuity packet may include end-of-file header information of “AA000002ZZ”, where “ZZ” functions as the end tag. The generation of the continuity packets may occur all at once or be spread out over time as more data is received, so the end-of file header information may be used to indicate an end of the data stream.

[0281] At step 2410, the method 2400 includes, at the information-generating device, transmitting the continuity packets. For example, the device-side processor 2112 may direct the device-side network adapter 2120 to transmit the continuity packets to the remote network adapter 2132 of the remote computing device 2122. This transmission may occur after all continuity packets have been generated, as the continuity packets are being generated, or any combination thereof. In cases where the generation of the continuity packets is spread out over time as more data is received, the generation and the transmission of the continuity packets allow for a reduced memory requirement and reduced peak network loads relative to waiting for all of the data to be received. For instance, if, before generating the continuity packets, the information-generating device waits until all of the data is received (e.g., from the sensors), the device-side memory 2114 may have to store the entirety of the data (i.e., which may require a substantial amount of memory to store an exceedingly large file), rather than temporarily storing a portion of the data while the device-side processor 2112 generates and transmits each continuity packet. Similarly, if, before transmitting the continuity packets, the information-generating device waits until all of the data has been received and all of the continuity packets have been generated, the network loads required for the transmission may be higher because a larger amount of data is being transmitted at once (e.g., all of the continuity packets are being transmitted in a short time period). The method 2400 may proceed to step 2412 or step 2416.

[0282] At step 2412, the method 2400 may include causing the remote computing device (e.g., the remote computing device 2122) to receive the map packet. The map packet may be received from the information generating device 2102. For example, the remote computing device 2122 may receive the map packet by way of the remote network adapter 2132.

[0283] At step 2414, the method 2400 may include causing the remote computing device to receive continuity packets in an initial order. The continuity packets may be received from the information-generating device 2102. For example, continuity packets may be received by the remote computing device 2122 by way of the remote network adapter 2132 in an initial order where the second continuity packet is received first, the first continuity packet is received second, and the end-of-file continuity packet is received third.

[0284] At step 2416, the method 2400 includes, at the remote computing device, generating an output file. Responsive to receiving at least two of the continuity packets and the map packet, the map packet may be used to generate an output file. The output file may be generated by ordering the continuity packets from the initial order into an output order. For example, given the initial order described above in step 2414, the remote processor 2124 may order the continuity packets, or contiguous portions of the continuity packets corresponding to contiguous portions of the data, into an output order. The output order may be as follows : 1 ) the first continuity packet, 2) the second continuity packet, and 3) the end-of-file continuity packet. In some embodiments, as the remote processor receives the continuity packets, the remote processor may contemporaneously generate the output file. For example, the remote computing device 2124 may receive the second continuity packet first and the first continuity packet second, but not yet have received the end-of-file continuity packet, after which the remote processor 2124 may order the continuity packets into an output order having the first continuity packet first and the second continuity packet second. In some embodiments, while the output file is being generated, the continuity packets are configured to be readable by external processes. Examples of such external processes include maintenance processes configured to check for device maintenance status or error messages. Such external process may be able to read and/or respond to maintenance requests or errors prior to ordering, such that an error message contained in the continuity packets can be read prior to completing the generation of the output file. For example, if a patient is undergoing a CT scan performed by a CT scanner, a processor may monitor and read the data in real-time or near real-time to detect an error message. In this example, if the CT scanner generates a continuity packet containing an error message indicating a fault with the CT scanner (e.g., the data obtained by the CT scanner will be unusable), then, at the direction of such an external monitoring process, the remote processor 124 may read the error message prior to ordering and generating the output file and stop the CT scanner during the CT scan. Stopping the CT scan prior to its completion would limit the patient’s unnecessary exposure to X-rays, as any exposure after the error may not result in usable data.

[0285] FIGS. 16A and 16B illustrate a computer-implemented method 2500 for ordering asynchronous data. The method 2500 may be performed by the system 2100 using the remote computing device 2122. The method 2500 may be implemented on a processor, such as the remote processor 2124, configured to perform the steps of the method 2500. The method 2500 may include operations implemented in instructions stored on a memory devices, such as the remote memory 2126, and executed on a processor, such as the remote processor 2124. The steps of the method 2500 may be stored in one or more non-transient computer-readable storage media.

[0286] At step 2502, the method 2500 includes, at the remote computing device (e.g., the remote computing device 2122), receiving the map packet. The map packet may be received from the information-generating device 2102. For example, the remote computing device 2122 may receive the map packet by way of the remote network adapter 2132. The map packet contains data mapping information that functions as an indicator of how continuity packets will be received. In some embodiments, the map packet includes end-of-file information that function as information against which data from later-received continuity packets can be compared for determining whether data transmission for a given file has ended. For example, the map packet may contain data mapping information indicating that the continuity packets will have a header following the format of “AA######AA”, and an end-of-file continuity packet will have an end-of-file header following the format of “AA######ZZ”. In this case, “######” indicates a numerical value starting at “000000” and going to a possible maximum of “999999” and “ZZ” functions as an end tag to indicate that the tagged continuity packet is the final continuity packet of the given file.

[0287] At step 2504, the method 2500 includes, at the remote computing device, receiving continuity packets in an initial order. The continuity packets may be received from the information-generating device 2102. For example, continuity packets may be received by the remote computing device 2122 by way of the remote network adapter 2132 in an initial order where the second continuity packet is received first, the first continuity packet is received second, and the end-of-file continuity packet is received third. Each of the continuity packets is a data packet that includes a contiguous portion of the data. The continuity packets may be generated using the data. For example, the device-side processor 2112 may take a contiguous portion of the data and place that contiguous portion into one of the continuity packets. One or more of the continuity packets may include header information that the processor can use to order the continuity packets. For example, a first continuity packet may include a first header including first header information of “ AA000000 AA”, and a second continuity packet may include a second header including second header information of “ AA000001 AA” . A contiguous portion of the data mapping information of the map packet may correspond to a contiguous portion of the header information. For example, the header information may include a contiguous portion of data including the string “AA”. The string “AA” corresponds to a portion of the mapping information of the map packet, indicating that header information of relevant continuity packets will contain the string “AA.” The header information may also include information pertaining to the portion of data contained in the continuity packet. The information generating device generates the continuity packets in an initial order; however, a remote computing device 2122 may not receive the continuity packets in the initial order (e.g., a first continuity packet may be generated first and a second continuity packet may be generated second, but the second packet may be received before the first packet is received). Thus, the header information may include information that the remote computing device 2122 can use to order (e.g., reassemble) the continuity packets, such as in the initial order that the continuity packets were generated. The header information of an end-of-file continuity packet can include an end tag corresponding to a contiguous portion of the end-of-file information. For example, an end-of-file continuity packet may include end-of-file header information of “AA000002ZZ”, where “ZZ” functions as the end tag. [0288] At step 2506, the method 2500 includes, at the remote computing device, generating an output file. Responsive to receiving at least two of the continuity packets and the map packet, the map packet may be used to generate an output file. The output file may be generated by ordering the continuity packets from the initial order into an output order. For example, given the initial order described above in step 2504, the remote processor 2124 may order the continuity packets, or contiguous portions of the continuity packets corresponding to contiguous portions of the data, into an output order. The output order may be as follows : 1 ) the first continuity packet, 2) the second continuity packet, and 3) the end-of-file continuity packet. In some embodiments, as the remote processor receives the continuity packets, the remote processor may contemporaneously generate the output file. For example, the remote computing device 2124 may receive the second continuity packet first and the first continuity packet second, but not yet have received the end-of-file continuity packet; and after that, the remote processor 124 may order the continuity packets into an output order having the first continuity packet first and the second continuity packet second. In some embodiments, while the output file is being generated, the continuity packets are configured to be readable by external processes. Examples of such external processes include maintenance processes configured to check for device maintenance status or error messages. Such external process may be able to read and/or respond to maintenance requests or errors prior to ordering, such that an error message contained in the continuity packets can be read prior to completing the generation of the output file. For example, if a patient is undergoing a CT scan performed by a CT scanner, a processor may monitor and read the data in real-time or near real-time to detect an error message. In this example, if the CT scanner generates a continuity packet containing an error message indicating a fault with the CT scanner (e.g., the data obtained by the CT scanner will be unusable), then, at the direction of such an external monitoring process, the remote processor 2124 may read the error message prior to ordering and generating the output file and stop the CT scanner during the CT scan. Stopping the CT scan prior to its completion would limit the patient’s unnecessary exposure to X-rays, as any exposure after the error may not result in usable data.

[0289] At step 2508, the method 2500 may include, at the remote computing device, using the end tag to generate an end-of-file indicator. For example, a flag may be used or a variable may be set as an end-of-file indicator when the end-of-file continuity packet containing the end tag “ZZ” is received (i.e., the remote processor may change a variable “end-of-file-reached” from “false” to “true”). [0290] At step 2510, the method 2500 may include using the header information, the map packet, and the end- of-file indicator to determine whether any continuity packets remain to be received. For example, if the first continuity packet containing the first header information of “AA000000AA” and the end-of-file continuity packet containing the end-of-file header information “AA000002ZZ” (and thus the end tag “ZZ”) have been received, the remote processor 2124 may determine that the second continuity packet has not been received. If any continuity packets remain to be received, the method 2500 proceeds to step 2512. If all continuity packets have been received, the method 2300 proceeds to step 2520.

[0291] At step 2512, if any continuity packets remain to be received, the method 2500 may include determining a non-zero wait time period. For example, if the second continuity packet has not been received, the remote processor 2124 may determine a wait time period. The wait time period may be between two seconds and ten seconds, or any other suitable period of time.

[0292] At step 2514, the method 2500 may include, at the remote computing device, determining if any continuity packets were received within the wait time period. For example, if the second continuity packet, which had not been previously received, is received within the wait time period, the remote computing device may determine that a continuity packet was received within the wait time period, subsequent to which the method 2500 proceeds to step 2516. However, if the second continuity packet is not received within the wait time period, the remote processor 2124 may determine that the continuity packet was not received within the wait time period, subsequent to which the method 2500 proceeds to step 2518.

[0293] At step 2516, responsive to receiving another continuity packet within the non-zero wait time period, the method 2500 may include the remote computing device continuing to generate the output file. For example, if the determination is that the second continuity packet that had not been previously received is received within the wait time period, then the remote processor 2124 may continue generating the output file. The method 2500 may then return to step 2520.

[0294] At step 2518, responsive to determining the non-zero wait time period and not receiving another continuity packet within the non-zero wait time period, the method 2500 may include the remote computing device transmitting an error signal. For example, if the determination is that the second continuity packet that had not been previously received was not received within the wait time period, the remote processor 2124 may direct the remote network adapter 2132 to transmit an error message and/or the remote output 2130 to present the error message (e.g., “Error: Incomplete Data”).

[0295] At step 2520, responsive to determining that every continuity packet has been received, the method 2500 includes transmitting the output file. For example, if the first continuity packet, the second continuity packet, and the end-of-file continuity packet have been received and ordered (e.g., into an output file), the remote processor 2124 may direct the remote network adapter 2132 to transmit the output file via the network 2134. [0296] FIGS. 14A, 14B, 15, 16A, and 16B are not intended to be limiting: the methods 2300, 2400, and 2500 can include more or fewer steps and/or processes than those illustrated in FIGS. 14A, 14B, 15, 16A and 16B. Further, the order of the steps of the methods 2300, 2400, and 2500 is not intended to be limiting; the steps can be arranged in any suitable order.

[0297] The term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium capable of storing, encoding or carrying a set of instructions for execution by the machine and causing the machine to perform any one or more of the methodologies of the present disclosure. The term “computer- readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0298] Any of the systems and methods described in this disclosure may be used in connection with rehabilitation. Unless expressly stated otherwise, is to be understood that rehabilitation includes prehabilitation (also referred to as "pre-habilitation" or "prehab"). Prehabilitation may be used as a preventative procedure or as a pre-surgical or pre-treatment procedure. Prehabilitation may include any action performed by or on a patient (or directed to be performed by or on a patient, including, without limitation, remotely or distally through telemedicine) to, without limitation, prevent or reduce a likelihood of injury (e.g., prior to the occurrence of the injury); improve recovery time subsequent to surgery; improve strength subsequent to surgery; or any of the foregoing with respect to any non-surgical clinical treatment plan to be undertaken for the purpose of ameliorating or mitigating injury, dysfunction, or other negative consequence of surgical or non-surgical treatment on any external or internal part of a patient's body. For example, a mastectomy may require prehabilitation to strengthen muscles or muscle groups affected directly or indirectly by the mastectomy. As a further non-limiting example, the removal of an intestinal tumor, the repair of a hernia, open-heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to excise them or parts of them, to treat them, etc., can require cutting through and harming numerous muscles and muscle groups in or about, without limitation, the abdomen, the ribs and/or the thoracic cavity. Prehabilitation can improve a patient's speed of recovery, measure of quality of life, level of pain, etc. in all the foregoing procedures. In one embodiment of prehabilitation, a pre-surgical procedure or a pre-non-surgical-treatment may include one or more sets of exercises for a patient to perform prior to such procedure or treatment. The patient may prepare an area of his or her body for the surgical procedure by performing the one or more sets of exercises, thereby strengthening muscle groups, improving existing and/or establishing new muscle memory, enhancing mobility, improving blood flow, and/or the like.

[0299] In some embodiments, the systems and methods described herein may use artificial intelligence and/or machine learning to generate a prehabilitation treatment plan for a user. Additionally, or alternatively, the systems and methods described herein may use artificial intelligence and/or machine learning to recommend an optimal exercise machine configuration for a user. For example, a data model may be trained on historical data such that the data model may be provided with input data relating to the user and may generate output data indicative of a recommended exercise machine configuration for a specific user. Additionally, or alternatively, the systems and methods described herein may use machine learning and/or artificial intelligence to generate other types of recommendations relating to prehabilitation, such as recommended reading material to educate the patient, a recommended health professional specialist to contact, and/or the like.

[0300] Consistent with the above disclosure, the examples of systems and method enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples.

[0301] Clause 1.1. A system for transmitting data comprising: an information-generating device; a processor in communication with the information-generating device, wherein the processor is configured to: receive data; generate a map packet; transmit the map packet; using the data, generate continuity packets , wherein each of the continuity packets comprises a contiguous portion of the data; transmit the continuity packets; and using the map packet and the continuity packets, cause an output file to be generated.

[0302] Clause 2.1. The system of any clause herein, wherein the processor is further configured to: cause a remote processor to receive the map packet; cause the remote processor to receive the continuity packets; and wherein, responsive to the remote processor receiving the map packet and at least two of the continuity packets, the remote processor generates the output file.

[0303] Clause 3.1. The system of any clause herein, wherein, as the continuity packets are received, the remote processor generates the output file in real-time or near real time.

[0304] Clause 4.1. The system of any clause herein, wherein the remote processor receives the continuity packets in an initial order; and wherein, using the map packet, the processor is configured to cause the remote processor to generate the output file by ordering the continuity packets from the initial order into an output order.

[0305] Clause 5.1. The system of any clause herein, wherein one or more of the continuity packets comprise header information; and wherein a contiguous portion of the map packet corresponds to a contiguous portion of the header information.

[0306] Clause 6.1. The system of any clause herein, wherein each of the continuity packets comprises header information.

[0307] Clause 7.1. The system of any clause herein, wherein the map packet comprises end-of-file information; wherein one or more of the continuity packets comprise header information; and wherein header information of an end-of-file continuity packet comprises an end tag corresponding to a contiguous portion of the end-of-file information.

[0308] Clause 8.1. The system of any clause herein, wherein the information-generating device comprises a medical device.

[0309] Clause 9.1. The system of any clause herein, wherein the medical device is an orthopedic rehabilitation device.

[0310] Clause 10.1. The system of any clause herein, further comprising a memory device operatively coupled to the processor, wherein the memory device stores instructions, and wherein the processor is configured to execute the instructions.

[0311] Clause 11.1. A method for operating an information-generating device, comprising: receiving data; generating a map packet; transmitting the map packet; using the data to generate continuity packets, wherein each of the continuity packets comprises a contiguous portion of the data; transmitting the continuity packets; and using the map packet and the continuity packets to cause an output file to be generated.

[0312] Clause 12.1. The method of any clause herein, further comprising: causing a remote processor to receive the map packet; causing the remote processor to receive the continuity packets; and wherein, responsive to the remote processor receiving the map packet and at least two of the continuity packets, the remote processor generates the output file.

[0313] Clause 13.1. The method of any clause herein, wherein, as the continuity packets are received, the remote processor generates the output file in real-time or near real time.

[0314] Clause 14.1. The method of any clause herein, wherein the remote processor receives the continuity packets in an initial order; and wherein, using the map packet, the method further comprises causing the remote processor to generate the output file by ordering the continuity packets from the initial order into an output order.

[0315] Clause 15.1. The method of any clause herein, wherein one or more of the continuity packets comprise header information; and wherein a contiguous portion of the map packet corresponds to a contiguous portion of the header information.

[0316] Clause 16.1. The method of any clause herein, wherein each of the continuity packets comprises header information.

[0317] Clause 17.1. The method of any clause herein, wherein the map packet comprises end-of-file information; wherein one or more of the continuity packets comprise header information; and wherein header information of an end-of-file continuity packet comprises an end tag corresponding to a contiguous portion of the end-of-file information.

[0318] Clause 18.1. The method of any clause herein, wherein the information-generating device comprises a medical device.

[0319] Clause 19.1. The method of any clause herein, wherein the medical device is an orthopedic rehabilitation device.

[0320] Clause 20.1. A tangible, non-transitoiy computer-readable storage medium storing instructions that, when executed, cause a processor to: receive data from an information-generating device; generate a map packet; transmit the map packet; using the data, generate continuity packets, wherein each of the continuity packets comprises a contiguous portion of the data; transmit the continuity packets; and using the map packet and the continuity packets, cause an output file to be generated. [0321] Clause 21.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processor to: cause a remote processor to receive the map packet; cause the remote processor to receive the continuity packets; and responsive to the remote processor receiving the map packet and at least two of the continuity packets, cause the remote processor to generate the output file.

[0322] Clause 22.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein, as the continuity packets are received, the remote processor generates the output file.

[0323] Clause 23.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the remote processor receives the continuity packets in an initial order; and wherein, using the map packet, the instructions further cause the processor to cause the remote processor to generate the output file by ordering the continuity packets from the initial order into an output order. [0324] Clause 24.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein one or more of the continuity packets comprise header information; and wherein a contiguous portion of the map packet corresponds to a contiguous portion of the header information.

[0325] Clause 25.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein each of the continuity packets comprises header information.

[0326] Clause 26.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein wherein the map packet comprises end-of-file information; wherein one or more of the continuity packets comprise header information; and wherein header information of an end-of-file continuity packet comprises an end tag corresponding to a contiguous portion of the end-of-file information.

[0327] Clause 27.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the information-generating device comprises a medical device.

[0328] Clause 28.1. The tangible, non-transitoiy computer-readable storage medium of any preceding clause, wherein the medical device is an orthopedic rehabilitation device.

[0329] Clause 29.1. A system for ordering of asynchronously transmitted data, comprising: a processor configured to: receive, from an information-generating device, a map packet; receive, from the information-generating device, continuity packets in an initial order; and responsive to receiving the map packet and at least two of the continuity packets, use the map packet to generate an output file by ordering the continuity packets from the initial order into an output order.

[0330] Clause 30.1. The system of any clause herein, wherein, as the continuity packets are received , the processor is configured to generate the output file in real-time or near real time.

[0331] Clause 31.1. The system of any clause herein, wherein, while the output file is being generated, the continuity packets are configured to be readable by external processes.

[0332] Clause 32.1. The system of any clause herein, wherein one or more of the continuity packets comprise header information; and wherein a contiguous portion of the map packet corresponds to a contiguous portion of the header information.

[0333] Clause 33.1. The system of any clause herein, wherein each of the continuity packets comprises header information.

[0334] Clause 34.1. The system of any clause herein, wherein the map packet comprises end-of-file information; wherein one or more of the continuity packets comprise header information; and wherein header information of an end-of-file continuity packet comprises an end tag corresponding to a contiguous portion of the end-of-file information.

[0335] Clause 35.1. The system of any clause herein, wherein, using the end tag, the processor is further configured to generate an end-of-file indication.

[0336] Clause 36 1. The system of any clause herein, wherein the processor is further configured to: use the header information, the map packet, and the end-of-file indication, to determine whether any continuity packet remains to be received; responsive to any continuity packets remaining to be received, determine a non-zero wait time period; responsive to receiving another continuity packet within the non-zero wait time period, continue to generate the output file; and responsive to receiving no further continuity packets within the non-zero wait time period, transmit an error signal.

[0337] Clause 37 1. The system of any clause herein, wherein the processor is further configured to: use the header information, the map packet, and the end-of-file indication to determine whether every continuity packet has been received; and if every continuity packet has been received, transmit the output file.

[0338] Clause 38.1. The system of any clause herein, wherein the information-generating device comprises a medical device.

[0339] Clause 39.1. The system of any clause herein, wherein the medical device is an orthopedic rehabilitation device.

[0340] Clause 40 1. The system of any clause herein, further comprising a memory device operatively coupled to the processor, wherein the memory device stores instructions, and wherein the processor is configured to execute the instructions.

[0341] Clause 41.1. A method for operating a computing device, comprising: receiving, from an information-generating device, a map packet; receiving, from the information-generating device, continuity packets in an initial order; and responsive to receiving the map packet and at least two of the continuity packets, using the map packet to generate an output file by ordering the continuity packets from the initial order into an output order.

[0342] Clause 42.1. The method of any clause herein, wherein, as the continuity packets are received, the output file is generated in real-time or near real-time.

[0343] Clause 43.1. The method of any clause herein, wherein, while the output file is being generated, the continuity packets are configured to be readable by external processes. [0344] Clause 44.1. The method of any clause herein, wherein one or more of the continuity packets comprise header information; and wherein a contiguous portion of the map packet corresponds to a contiguous portion of the header information.

[0345] Clause 45.1. The method of any clause herein, wherein each of the continuity packets comprises header information.

[0346] Clause 46.1. The method of any clause herein, wherein the map packet comprises end-of-file information; wherein one or more of the continuity packets comprise header information; and wherein header information of an end-of-file continuity packet comprises an end tag corresponding to a contiguous portion of the end-of-file information.

[0347] Clause 47.1. The method of any clause herein, further comprising using the end tag to generate an end- of-file indication.

[0348] Clause 48.1. The method of any clause herein, further comprising: using the header information, the map packet, and the end-of-file indication to determine whether every continuity packet has been received; responsive to any continuity packets remaining to be received, determining a non- zero wait time period; responsive to receiving another continuity packet within the non- zero wait time period, continuing to generate the output file; and responsive to receiving no further continuity packets within the non-zero wait time period, transmitting an error signal.

[0349] Clause 49.1. The method of any clause herein, further comprising: using the header information, the map packet, and the end-of-file indication to determine whether every continuity packet has been received; and if every continuity packet has been received, transmitting the output file.

[0350] Clause 50.1. The method of any clause herein, wherein the information-generating device comprises a medical device.

[0351] Clause 51.1. The method of any clause herein, wherein the medical device is an orthopedic rehabilitation device.

[0352] Clause 52.1. A tangible, non-transitory computer-readable storage medium storing instructions that, when executed, cause a processor to: receive, from an information-generating device, a map packet; receive, from the information-generating device, continuity packets in an initial order; and responsive to receiving the map packet and at least two of the continuity packets, using the map packet to generate an output file by ordering the continuity packets from the initial order into an output order.

[0353] Clause 53.1. The tangible, non-transitory computer-readable storage medium of any clause herein, wherein, as the continuity packets are received, the processor contemporaneously generates the output file. [0354] Clause 54.1. The tangible, non-transitory computer-readable storage medium of any clause herein, wherein the continuity packets are configured to be readable by external processes while the output file is being generated. [0355] Clause 55.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein one or more of the continuity packets comprise header information; and wherein a contiguous portion of the map packet corresponds to a contiguous portion of the header information.

[0356] Clause 56. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein each of the continuity packets comprises header information.

[0357] Clause 57.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the map packet comprises end-of-file information; wherein one or more of the continuity packets comprise header information; and wherein header information of an end-of-file continuity packet comprises an end tag corresponding to a contiguous portion of the end-of-file information.

[0358] Clause 58.1. The tangible, non-transitoiy computer-readable storage medium of any preceding clause, wherein the instructions further cause the processor to use the end tag to generate an end-of-file indication. [0359] Clause 59.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processor to: use the header information, the map packet, and the end-of-file indication, to determine whether any continuity packet remains to be received; responsive to any continuity packet remaining to be received, determine a non-zero wait time period; responsive to receiving another continuity packet within the non-zero wait time period, continue generating the output file; and responsive to receiving no further continuity packets within the non-zero wait time period, transmit an error signal.

[0360] Clause 60.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processor to: use the header information, the map packet, and the end-of-file indication to determine whether every continuity packet has been received; and responsive to determining that every continuity packet has been received, transmit the output file. [0361] Clause 61.1. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the information-generating device comprises a medical device.

[0362] Clause 62.1. The tangible, non-transitoiy computer-readable storage medium of any preceding clause, wherein the medical device is an orthopedic rehabilitation device.

[0363] Clause 63.1. A system for transmitting data and ordering asynchronous data, comprising: an information-generating device comprising a device-side processor configured to: receive data; generate a map packet; transmit the map packet; use the data to generate continuity packets, wherein each of the continuity packets comprises a contiguous portion of the data; transmit the continuity packets; and a remote computing device comprising a remote processor configured to: receive, from the information-generating device, the map packet; receive, from the information-generating device, the continuity packets in an initial order; and responsive to receiving at least two of the continuity packets and the map packet, use the map packet to generate an output file by ordering the continuity packets from the initial order into an output order.

[0364] Clause 64.1. The system of any clause herein, wherein, as the remote processor receives the continuity packets, the remote processor contemporaneously generates the output file.

[0365] Clause 65.1. The system of any clause herein, wherein, while the output file is being generated, the continuity packets are configured to be readable by external processes.

[0366] Clause 66.1. The system of any clause herein, wherein one or more of the continuity packets comprise header information; and wherein a contiguous portion of the map packet corresponds to a contiguous portion of the header information.

[0367] Clause 67.1. The system of any clause herein, wherein each of the continuity packets comprises header information.

[0368] Clause 68.1. The system of any clause herein, wherein the map packet comprises end-of-file information; wherein one or more of the continuity packets comprise header information; and wherein header information of an end-of-file continuity packet comprises an end tag corresponding to a contiguous portion of the end-of-file information.

[0369] Clause 69.1. The system of any clause herein, wherein the remote processor is further configured to, using the end tag, generate an end-of-file indication.

[0370] Clause 70.1. The system of any clause herein, wherein the remote processor is further configured to: use the header information, the map packet, and the end-of-file indication to determine whether any continuity packets remain to be received; if any continuity packets remain to be received, determine a non-zero wait time period; responsive to determining the non-zero wait time period and receiving another continuity packet within the non-zero wait time period, continue generating the output file; and responsive to determining the non-zero wait time period and not receiving another continuity packet within the non-zero wait time period, transmit an error signal.

[0371] Clause 71.1. The system of any clause herein, wherein the remote processor is further configured to : use the header information, the map packet, and the end-of-file indication to determine whether every continuity packet has been received; and responsive to determining that every continuity packet has been received, transmit the output file. [0372] Clause 72.1. The system of any clause herein, wherein the information-generating device comprises a medical device.

[0373] Clause 73.1. The system of any clause herein, wherein the medical device is an orthopedic rehabilitation device. [0374] Clause 74.1. The system of any clause herein, further comprising a device-side memory device operatively coupled to the device-side processor, wherein the device-side memory device stores device-side instructions, and wherein the device-side processor is configured to execute the device-side instructions.

[0375] Clause 75.1. The system of any clause herein, further comprising a remote memory device operatively coupled to the remote processor, wherein the remote memory device stores remote instructions, and wherein the remote processor is configured to execute the remote instructions.

[0376] Clause 76.1. A computer-implemented system, comprising:

[0377] an electromechanical device configured to be manipulated by a patient while performing an exercise session;

[0378] a processor in communication with the electromechanical device, wherein the processor is configured to:

[0379] receive data;

[0380] generate a map packet;

[0381] transmit the map packet;

[0382] using the data, generate continuity packets, wherein each of the continuity packets comprises a contiguous portion of the data;

[0383] transmit the continuity packets; and

[0384] using the map packet and the continuity packets, cause an output file to be generated.

[0385] Clause 77.1. The computer-implemented system of any clause herein, wherein the processor is further configured to:

[0386] cause a remote processor to receive the map packet;

[0387] cause the remote processor to receive the continuity packets; and

[0388] wherein, responsive to the remote processor receiving the map packet and at least two of the continuity packets, the remote processor generates the output file.

[0389] Clause 78.1. The computer-implemented system of any clause herein, wherein, as the continuity packets are received, the remote processor generates the output file in real-time or near real time.

[0390] No part of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) unless the exact words “means for” are followed by a participle.

[0391] The foregoing description, for purposes of explanation, use specific nomenclature to provide a thorough understanding of the described embodiments. However, it should be apparent to one skilled in the art that the specific details are not required to practice the described embodiments. Thus, the foregoing descriptions of specific embodiments are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. It should be apparent to one of ordinary skill in the art that many modifications and variations are possible in view of the above teachings. [0392] The above discussion is meant to be illustrative of the principles and various embodiments of the present invention. Once the above disclosure is fully appreciated, numerous variations and modifications will become apparent to those skilled in the art. It is intended that the following claims be interpreted to embrace all such variations and modifications. SYSTEM AND METHOD FOR PROCESSING MEDICAL CLAIMS USING BIOMETRIC SIGNATURES

[0393] Determining optimal remote examination procedures to create an optimal treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; psychographic; geographic; diagnostic; measurement- or test-based; medically historic; behavioral historic; cognitive; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment device, an amount of force exerted on a portion of the treatment device, a range of motion achieved on the treatment device, a movement speed of a portion of the treatment device, a duration of use of the treatment device, an indication of a plurality of pain levels using the treatment device, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, a glucose level or other biomarker, or some combination thereof. It may be desirable to process and analyze the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[0394] Further, another technical problem may involve distally treating, via a computing device during a telemedicine session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling, from the different location, the control of a treatment device used by the patient at the patient’ s location. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a medical professional may prescribe a treatment device to the patient to use to perform a treatment protocol at their residence or at any mobile location or temporary domicile. A medical professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, or the like. A medical professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

[0395] When the healthcare provider is located in a location different from the patient and the treatment device, it may be technically challenging for the healthcare provider to monitor the patient’ s actual progress (as opposed to relying on the patient’s word about their progress) in using the treatment device, modify the treatment plan according to the patient’s progress, adapt the treatment device to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

[0396] Further, in addition to the information described above, determining optimal examination procedures for a particular ailment (e.g., injury, disease, any applicable medical condition, etc.) may include physically examining the injured body part of a patient. The healthcare provider, such as a physician or a physical therapist, may visually inspect the injured body part (e.g., a knee joint). The inspection may include looking for signs of inflammation or injury (e.g., swelling, redness, and warmth), deformity (e.g., symmetrical joints and abnormal contours and/or appearance), or any other suitable observation. To determine limitations of the injured body part, the healthcare provider may observe the injured body part as the patient attempts to perform normal activity (e.g., bending and extending the knee and gauging any limitations to the range of motion of the injured knee). The healthcare provide may use one or more hands and/or fingers to touch the injured body part. By applying pressure to the injured body part, the healthcare provider can obtain information pertaining to the extent of the injury. For example, the healthcare provider’s fingers may palpate the injured body part to determine if there is point tenderness, warmth, weakness, strength, or to make any other suitable observation.

[0397] It may be desirable to compare characteristics of the injured body part with characteristics of a corresponding non-injured body part to determine what an optimal treatment plan for the patient may be such that the patient can obtain a desired result. Thus, the healthcare provider may examine a corresponding non- injured body part of the patient. For example, the healthcare provider’s fingers may palpate a non-injured body part (e.g., a left knee) to determine a baseline of how the patient’s non-injured body part feels and functions. The healthcare provider may use the results of the examination of the non-injured body part to determine the extent of the injury to the corresponding injured body part (e.g., a right knee). Additionally, injured body parts may affect other body parts (e.g., a knee injury may limit the use of the affected leg, leading to atrophy of leg muscles). Thus, the healthcare provider may also examine additional body parts of the patient for evidence of atrophy of or injury to surrounding ligaments, tendons, bones, and muscles, examples of muscles being such as quadriceps, hamstrings, or calf muscle groups of the leg with the knee injury. The healthcare provider may also obtain information as to a pain level of the patient before, during, and/or after the examination.

[0398] The healthcare provider can use the information obtained from the examination (e.g., the results of the examination) to determine a proper treatment plan for the patient. If the healthcare provider cannot conduct a physical examination of the one or more body parts of the patient, the healthcare provider may not be able to fully assess the patient’s injury and the treatment plan may not be optimal. Accordingly, embodiments of the present disclosure pertain to systems and methods for conducting a remote examination of a patient. The remote examination system provides the healthcare provider with the ability to conduct a remote examination of the patient, not only by communicating with the patient, but by virtually observing and/or feeling the patient’s one or more body parts.

[0399] In some embodiments, the systems and methods described herein may be configured for manipulation of a medical device. For example, the systems and methods may be configured to use a medical device configured to be manipulated by an individual while the individual is performing a treatment plan. The individual may include a user, patient, or other a person using the treatment device to perform various exercises for prehabilitation, rehabilitation, stretch training, e.g., pliability, medical procedures, and the like. The systems and methods described herein may be configured to use and/or provide a patient interface comprising an output device, wherein the output device is configured to present telemedicine information associated with a telemedicine session.

[0400] In some embodiments, the systems and methods described herein may be configured for processing medical claims. For example, the system includes a processor configured to receive device-generated information from a medical device. Using the device-generated information received, the processor is configured to determine device-based medical coding information. The processor is further configured to transmit the device-based medical coding information to a claim adjudication server. Any or all of the methods described may be implemented during a telemedicine session or at any other desired time.

[0401] In some embodiments, the medical claims may be processed, during a telemedicine or telehealth session, by a healthcare provider. The healthcare provider may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment device. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive data, instruct ructions, or the like and/or operate distally from the patient and the treatment device.

[0402] In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a medical professional. The video may also be accompanied by audio, text and other multimedia information and/or other sensorial or perceptive (e.g., tactile, gustatory, haptic, pressure-sensing-based or electromagnetic (e.g., neurostimulation), and without limitation, gesture recognition, gesture control, touchless user interfaces (TUIs), kinetic user interfaces (KUIs), tangible user interfaces, wired gloves, depth-aware cameras, stereo cameras, and gesture-based controllers). Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds (or any suitably proximate difference between two different times) but greater than 2 seconds.

[0403] FIGS. 17-26, discussed below, and the various embodiments used to describe the principles of this disclosure are by way of illustration only and should not be construed in any way to limit the scope of the disclosure.

[0404] FIG. 17 illustrates a component diagram of an illustrative medical system 3100 in accordance with aspects of this disclosure. The medical system 3100 may include a medical device 3102. The medical device 3102 may be a testing device, a diagnostic device, a therapeutic device, or any other suitable medical device. “Medical device” as used in this context means any hardware, software, mechanical, such as a treatment device (e.g., medical device 3102, treatment device 3010, or the like), that may assist in a medical service, regardless of whether it is FDA (or other governmental regulatory body of any given country) approved, required to be FDA (or other governmental regulatory body of any given country) approved or available commercially or to consumers without such approval. Non-limiting examples of medical devices include a thermometer, an MRI machine, a CT-scan machine, a glucose meter, an apheresis machine, and a physical therapy machine, such as a physical therapy cycle. Non-limiting examples of places where the medical device 3102 may be located include a healthcare clinic, a physical rehabilitation center, and a user’s home to allow for telemedicine treatment, rehabilitation, and/or testing. FIG. 18 illustrates an example of the medical device 3102 where the medical device 3102 is a physical therapy cycle.

[0405] As generally illustrated in FIG. 18, the medical device 3102 may comprise an electromechanical device, such as a physical therapy device. FIG. 18 generally illustrates a perspective view of an example of a medical device 3102 according to certain aspects of this disclosure. Specifically, the medical device 3102 illustrated is an electromechanical device 3202, such as an exercise and rehabilitation device (e.g., a physical therapy device orthe like). The electromechanical device 3202 is shown having pedal 3210 on opposite sides that are adjustably positionable relative to one another on respective radially-adjustable couplings 3208. The depicted electromechanical device 3202 is configured as a small and portable unit so that it is easily transported to different locations at which rehabilitation or treatment is to be provided, such as at patients’ homes, alternative care facilities, or the like. The patient may sit in a chair proximate the electromechanical device 3202 to engage the electromechanical device 3202 with the patient’s feet, for example. The electromechanical device 3202 includes a rotary device such as radially-adjustable couplings 3208 or flywheel or the like rotatably mounted such as by a central hub to a frame or other support. The pedals 3210 are configured for interacting with a patient to be rehabilitated and may be configured for use with lower body extremities such as the feet, legs, or upper body extremities, such as the hands, arms, and the like. For example, the pedal 3210 may be a bicycle pedal of the type having a foot support rotatably mounted onto an axle with bearings. The axle may or may not have exposed end threads for engaging a mount on the radially-adjustable coupling 3208 to locate the pedal on the radially-adjustable coupling 3208. The radially-adjustable coupling 3208 may include an actuator configured to radially adjust the location of the pedal to various positions on the radially-adjustable coupling 3208.

[0406] Alternatively, the radially-adjustable coupling 3208 may be configured to have both pedals 3210 on opposite sides of a single coupling 3208. In some embodiments, as depicted, a pair of radially-adjustable couplings 3208 may be spaced apart from one another but interconnected to an electric motor 3206. In the depicted example, the computing device 3112 may be mounted on the frame of the electromechanical device 3202 and may be detachable and held by the user while the user operates the electromechanical device 3202. The computing device 3112 may present the patient portal 3212 and control the operation of the electric motor 3206, as described herein.

[0407] In some embodiments, as described in U.S. Patent No. 10,173,094 (U.S. Appl. No. 15/700,293), which is incorporated by reference herein in its entirety for all purposes, the medical device 3102 may take the form of a traditional exercise/rehabilitation device which is more or less non-portable and remains in a fixed location, such as a rehabilitation clinic or medical practice. The medical device 3102 may include a seat and is less portable than the medical device 3102 shown in FIGURE 18. FIG. 18 is not intended to be limiting; the electromechanical device 3202 may include more or fewer components than those illustrated in FIG. 18.

[0408] FIGS. 23-24 generally illustrate an embodiment of a treatment device, such as a treatment device 3010. More specifically, FIG. 23 generally illustrates a treatment device 3010 in the form of an electromechanical device, such as a stationary cycling machine 3014, which may be called a stationary bike, for short. The stationary cycling machine 3014 includes a set of pedals 3012 each attached to a pedal arm 3020 for rotation about an axle 3016. In some embodiments, and as generally illustrated in FIG. 24, the pedals 3012 are movable on the pedal arm 3020 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 3016 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 16. A pressure sensor 3018 is attached to or embedded within one of the pedals 3012 for measuring an amount of force applied by the patient on the pedal 3102. The pressure sensor 3018 may communicate wirelessly to the treatment device 3010 and/or to the patient interface 3026. FIGS. 23-24 are not intended to be limiting; the treatment device 3010 may include more or fewer components than those illustrated in FIGS. 23-24.

[0409] FIG. 25 generally illustrates a person (a patient) using the treatment device 3010 of FIG. 23, and showing sensors and various data parameters connected to a patient interface 3026. The example patient interface 3026 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 3026 may be embedded within or attached to the treatment device 10. FIG. 25 generally illustrates the patient wearing the ambulation sensor 3022 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 3022 has recorded and transmitted that step count to the patient interface 3026. FIG. 25 also generally illustrates the patient wearing the goniometer 3024 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 3024 is measuring and transmitting that knee angle to the patient interface 3026. FIG. 25 generally illustrates a right side of one of the pedals 3012 with a pressure sensor 3018 showing “FORCE 12.5 lbs.”, indicating that the right pedal pressure sensor 3018 is measuring and transmitting that force measurement to the patient interface 3026. FIG. 25 also generally illustrates a left side of one of the pedals 3012 with a pressure sensor 3018 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 3018 is measuring and transmitting that force measurement to the patient interface 3026. FIG. 25 also generally illustrates other patient data, such as an indicator of “SESSION TIME 0:04: 13”, indicating that the patient has been using the treatment device 3010 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 3026 based on information received from the treatment device 3010. FIG. 25 also generally illustrates an indicator showing “PAIN LEVEL 3”, Such a pain level may be obtained from the patient in response to a solicitation, such as a question, presented upon the patient interface 3026.

[0410] The medical device 3102 may include an electromechanical device 3104, such as pedals of a physical therapy cycle, a goniometer configured to attach to a joint and measure joint angles, or any other suitable electromechanical device 3104. The electromechanical device 3104 may be configured to transmit information, such as positioning information. A non-limiting example of positioning information includes information relating to the location of pedals of the physical therapy cycle 3200. The medical device 3102 may include a sensor 3106. The sensor 3106 can be used for obtaining information to be used in generating a biometric signature. A biometric signature, for the purpose of this disclosure, is a signature derived from certain biological characteristics of a user. The biometric signature can include information of a user, such as fingerprint information, retina information, voice information, height information, weight information, vital sign information (e.g., blood pressure, heart rate, etc.), response information to physical stimuli (e.g., change in heart rate while running on a treadmill), performance information (rate of speed on the electromechanical device 3104), or any other suitable biological characteristic(s) of the user. The biometric signature may include and/or be determined by a kinesiological signature. A kinesiological signature, for the purpose of this disclosure, refers to a signature derived from human body movement, such as information about a range of motion of or about a user's joint, e.g., a knee, an elbow, a neck, a spine, or any other suitable joint, ligament, tendon, or muscle of a human. The sensor 3106 may be a temperature sensor (such as a thermometer or thermocouple), a strain gauge, a proximity sensor, an accelerometer, an inclinometer, an infrared sensor, a pressure sensor, a light sensor, a smoke sensor, a chemical sensor, any other suitable sensor, a fingerprint scanner, a sound sensor, a microphone, or any combination thereof. The medical device 3102 may include, for obtaining information to be used in generating a biometric signature, a camera 3108, such as a still image camera, a video camera, an infrared camera, an X-ray camera, any other suitable camera, or any combination thereof. The medical device 3102 may include, for obtaining information to be used in generating a biometric signature, an imaging device 3110, such as an MRI imaging device, an X-ray imaging device, a thermal imaging device, any other suitable imaging device, or any combination thereof.

[0411] The medical device 3102 may include, be coupled to, or be in communication with a computing device 112. The computing device 3112 may include a processor 3114. The processor 3114 can include, for example, computers, intellectual property (IP) cores, application-specific integrated circuits (ASICs), programmable logic arrays, optical processors, programmable logic controllers, microcode, microcontrollers, servers, microprocessors, digital signal processors, any other suitable circuit, or any combination thereof.

[0412] The computing device 3112 may include a memory device 3116 in communication with the processor 3114. The memory device 3116 can include any type of memory capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a flash drive, a compact disc (CD), a digital video disc (DVD), solid state drive (SSD), or any other suitable type of memory.

[0413] The computing device 3112 may include an input device 3118 in communication with the processor 3114. Examples of the input device 3118 include a keyboard, a keypad, a mouse, a microphone supported by speech-to-text software, or any other suitable input device. The input device 3118 may be used by a medical system operator to input information, such as user-identifying information, observational notes, or any other suitable information. An operator is to be understood throughout this disclosure to include both people and computer software, such as programs or artificial intelligence.

[0414] The computing device 3112 may include an output device 3120 in communication with the processor 3114. The output device 3120 may be used to provide information to the medical device operator or a user of the medical device 3102. Examples of the output device 3120 may include a display screen, a speaker, an alarm system, or any other suitable output device, including haptic, tactile, olfactory, or gustatory ones, and without limitation, gesture recognition, gesture control, touchless user interfaces (TUIs), kinetic user interfaces (KUIs), tangible user interfaces, wired gloves, depth-aware cameras, stereo cameras, and gesture-based controllers. In some embodiments, such as where the computing device 3112 includes a touchscreen, the input device 3118 and the output device 3120 may be the same device.

[0415] For communicating with remote computers and servers, the computing device 3112 may include a network adapter 3122 in communication with the processor 3114. The network adapter 3122 may include wired or wireless network adapter devices or a wired network port.

[0416] Any time information is transmitted or communicated, the information may be in EDI file format or any other suitable file format. In any of the methods or steps of the method, file format conversions may take place. By utilizing Internet of Things (IoT) gateways, data streams, ETL bucketing, EDI mastering, or any other suitable technique, data can be mapped, converted, or transformed into a carrier preferred state. As a result of the volume of data being transmitted, the data security requirements, and the data consistency requirements, enterprise grade architecture may be utilized for reliable data transfer.

[0417] FIG. 17 is not intended to be limiting; the medical system 3100 and the computing device 3112 may include more or fewer components than those illustrated in FIG. 17.

[0418] FIG. 19 illustrates a component diagram of an illustrative clinic server system 3300 in accordance with aspects of this disclosure. The clinic server system 3300 may include a clinic server 3302. The clinic server system 3300 or clinic server 3302 may be servers owned or controlled by a medical clinic (such as a doctor's office, testing site, or therapy clinic) or by a medical practice group (such as a testing company, outpatient procedure clinic, diagnostic company, or hospital). The clinic server 3302 may be proximate to the medical system 3100. In other embodiments, the clinic server 3302 may be remote from the medical system 3100. For example, during telemedicine-based or telemedicine-mediated treatment, rehabilitation, or testing, the clinic server 3302 may be located at a healthcare clinic and the medical system 3100 may be located at a patient’s home. The clinic server 3302 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, any other suitable computing device, or any combination of the above. The clinic server 302 may be cloud-based or be a real-time software platform, and it may include privacy (e.g., anonymization, pseudonymization, or other) software or protocols, and/or include security software or protocols. The clinic server 3302 may include a computing device 3304. The computing device 3304 may include a processor 3306. The processor 3306 can include, for example, computers, intellectual property (IP) cores, application-specific integrated circuits (ASICs), programmable logic arrays, optical processors, programmable logic controllers, microcode, microcontrollers, servers, microprocessors, digital signal processors, any other suitable circuit, or any combination thereof.

[0419] The computing device 3304 may include a memory device 3308 in communication with the processor 3306. The memory device 3308 can include any type of memory capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a flash drive, a compact disc (CD), a digital video disc (DVD), a solid state drive (SSD), or any other suitable type of memory.

[0420] The computing device 3304 may include an input device 3310 in communication with the processor 3306. Examples of the input device 3310 include a keyboard, a keypad, a mouse, a microphone supported by speech-to-text software, or any other suitable input device.

[0421] The computing device 3304 may include an output device 3312 in communication with the processor 3114. Examples of the output device 3312 include a display screen, a speaker, an alarm system, or any other suitable output device, including haptic, tactile, olfactory, or gustatory ones, and without limitation, gesture recognition, gesture control, touchless user interfaces (TUIs), kinetic user interfaces (KUIs), tangible user interfaces, wired gloves, depth-aware cameras, stereo cameras, and gesture-based controllers. In some embodiments, such as where the computing device 3304 includes a touchscreen, the input device 3310 and the output device 3312 may be the same device.

[0422] The computing device 3304 may include a network adapter 3314 in communication with the processor 3306 for communicating with remote computers and/or servers. The network adapter 3314 may include wired or wireless network adapter devices.

[0423] FIG. 19 is not intended to be limiting; the clinic server system 3300, the clinic server 3302, and the computing device 3304 may include more or fewer components than those illustrated in FIG. 19.

[0424] FIG. 20 illustrates a component diagram and method of an illustrative medical claim processing system 3400 and information flow according to aspects of this disclosure. The medical claim processing system 3400 may include the medical system 3100. The medical claim processing system 3400 may include a clinic server 3302.

[0425] The medical claim processing system 3400 may include a patient notes database 3402. The patient notes database 3402 may include information input by a clinic operator or information received from the clinic server 3302. For example, the clinic operator may enter information obtained manually about a patient's height and weight and/or information received from the patient about a condition from which the patient is suffering. The medical claim processing system 3400 may include an electronic medical records (EMR) database 3404. The EMR database 3404 may include information input by a clinic operator and/or information received from the clinic server 3302 or the patient notes database 3402. For example, the EMR database 3404 may contain information received from the medical devices 3102 or historical information obtained from patient notes database 3402, such as historical height and weight information. One or both of the patient notes database 3402 and the EMR database 3404 may be located on the clinic server 3302, on one or more remote servers, or on any other suitable system or server.

[0426] The medical claim processing system 3400 may include a biller server 3406. The biller server 3406 may receive medical service information from the medical system 3100; the clinic server 3302; the patient notes database 3402; the EMR database 3404; any suitable system, server, or database; or any combination thereof. The medical service information may include medical coding information. By correlating the medical service information with an associated medical code, the biller server 3406 may determine medical coding information. The biller server 3406 may determine one or more responsible parties for payment of medical bills. Using the medical codes, the biller server 3406 may generate an invoice. The biller server 3406 may transmit the medical coding information and medical service information to the responsible party or parties. The biller server 3406 may be owned or controlled by a medical practice group (such as a testing company, an outpatient procedure clinic, a diagnostic company, or a hospital), a health insurance company, a governmental entity, or any other organization (including third-party organizations) associated with medical billing procedures. The biller server 3406 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, any other suitable computing device, or any combination of the above. The biller server 3406 may be cloud-based or be a real-time software platform, and it may include privacy (e.g., anonymization, pseudonymization, or other) software or protocols, and/or include security software or protocols. The biller server 3406 may contain a computing device including any combination of the components of the computing device 3304 as illustrated in FIG. 19. The biller server 3406 may be proximate to or remote from the clinic server 3302.

[0427] The medical claim processing system 3400 may include a claim adjudication server 3408. The claim adjudication server 3408 may be owned or controlled by a health insurance company, governmental entity, or any other organization (including third-party organizations) associated with medical billing procedures. The claim adjudication server 3408 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, any other suitable computing device, or any combination of the above. The claim adjudication server 3408 may be cloud- based or be a real-time software platform, and it may include privacy (e.g., anonymization, pseudonymization, or other) software or protocols, and/or include security software or protocols. The claim adjudication server 3408 may contain a computing device including any combination of the components of the computing device 3304 as illustrated in FIG. 19. The claim adjudication server 3408 may be proximate to or remote from the biller server 3406. The claim adjudication server 3408 may be configured to make or receive a determination about whether a claim should be paid.

[0428] The medical claim processing system 3400 may include a fraud, waste, and abuse (FWA) server 3410. The FWA server 3410 may be owned or controlled by a health insurance company, a governmental entity, or any other organization (including a third-party organization) associated with medical billing procedures. The FWA server 3410 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, any other suitable computing device, or any combination of the above. The FWA server 3410 may be cloud-based or be a real time software platform, and it may include privacy-enhancing, privacy-preserving, or privacy modifying software or protocols (e.g., anonymization, pseudonymization, or other), and/or include security software or protocols. The FWA server 3410 may contain a computing device including any combination of the components of the computing device 3304 as illustrated in FIG. 19. The FWA server 3410 may be proximate to or remote from the claim adjudication server 3408. The FWA server 3410 may be configured to make or receive a determination about whether a medical claim should be paid. The FWA server 3410 may be configured to make or receive a determination about whether a proposed payment for a medical claim is a result of fraud, waste, or abuse.

[0429] The medical claim processing system 3400 may include a payment server 3412. The payment server 3412 may be owned or controlled by a health insurance company, a governmental entity, or any other organization (including a third-party organization) associated with medical billing procedures. The payment server 3412 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, any other suitable computing device, or any combination of the above. The payment server 3412 may be cloud-based or be a real-time software platform, and it may include privacy -enhancing, privacy -preserving, or privacy modifying software or protocols (e.g., anonymization, pseudonymization, or other), and/or include security software or protocols. The payment server 3412 may contain a computing device including any combination of the components of the computing device 3304. The payment server 3412 may be proximate to or remote from the biller server 3406 and/or the FWA server 3410. The payment server 3412 may be configured to make or receive a determination about whether a claim should be paid. The payment server 3412 may be configured to make or receive a determination about whether a proposed payment is, wholly or partially, a direct or indirect result of fraud, waste, or abuse. The payment server 3412 may be configured to process or transmit a payment to the service provider.

[0430] FIG. 20 is not intended to be limiting; the medical claim processing system 3400 and any sub components thereof may include more or fewer components, steps, and/or processes than those illustrated in FIG. 20. Any of the components of the medical claim processing system 3400 may be in direct or indirect communication with each other. Any or all of the methods described may be implemented during a telemedicine session or at any other desired time.

[0431] FIG. 21 illustrates a component diagram of an illustrative medical claim processing system 3500 according to aspects of this disclosure. The medical claim processing system 3500 can include the medical system 3100 of FIG. 17. The medical system 3100 may be in communication with a network 3502. The network 3502 may be a public network (e.g., connected to the Internet via wired (Ethernet) or wireless (Wi-Fi)), a private network (e.g., a local area network (LAN) or a wide area network (WAN)), a combination thereof, or any other suitable network.

[0432] The medical claim processing system 3500 can include the clinic server 3302. The clinic server 3302 may be in communication with the network 3502. The clinic server 3302 is shown as an example of servers that canbe in communication with the network 3502. In addition to or in place of the clinic server 3302, the medical claim processing system 3500 can include the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, or any combination thereof.

[0433] The medical claim processing system 3500 can include a cloud-based learning system 3504. For example, the cloud-based learning system 3504 may be used to update or change a first biometric signature (i.e., a predicted biometric signature) of a user using historical device-based signature information relating to the user or other users, such as those with similar medical conditions, medical services provided, demographics, or any other suitable similarity. The cloud-based learning system 3504 may be used to update or change an algorithm for generating a signature indicator. The signature indicator can include whether the first biometric signature (i.e., the predicted biometric signature) matches a second biometric signature (i.e., a device-based biometric signature). Examples of signature indicators include flags and computer-coded variables. The cloud-based learning system 3504 may be in communication with the network 3502. The cloud-based learning system 3504 may include one or more training servers 3506 and form a distributed computing architecture. Each of the training servers 3506 may include a computing device, including any combination of one or more of the components of the computing device 3304 as illustrated in FIG. 19, or any other suitable components. The training servers 3506 maybe in communication with one another via any suitable communication protocol. The training servers 3506 may store profiles for users including, but not limited to, patients, clinics, practice groups, and/or insurers. The profiles may include information such as historical device-generated information, historical device-based medical coding information, historical reviewed medical coding information, historical electronic medical records (EMRs), historical predicted biometric signatures, historical device-based biometric signatures, historical signature comparisons, historical signature indicators, historical emergency biometric signatures, historical emergency comparisons, historical emergency indicators, and any other suitable historical information. Other non-limiting examples of suitable historical information can include any information relating to a specific patient, a condition, or a population that was recorded at a time prior to the interaction presently being billed as the medical claim.

[0434] In some aspects, the cloud-based learning system 3504 may include a training engine 3508 capable of generating one or more machine learning models 3510. The machine learning models 3510 may be trained to generate algorithms that aid in determining the device-based medical coding information, for example, by using the device generated information or generation of predicted biometric signatures, device-based biometric signatures, signature indicators, emergency biometric signatures, and/or emergency indicators. For example, if the medical device 3102 is anMRI machine, the machine learning models 3510 may use the device-generated information generated by the MRI machine (e.g., MRI images) to generate progressively more accurate algorithms to determine which type of medical procedure (e.g., MRI scan) was performed and which type of medical coding information (e.g., 73720, 73723, and 74183) to associate with the medical procedure performed, predicted biometric signatures, and/or signature indicators. To generate the one or more machine learning models 3510, the training engine 3508 may train the one or more machine learning models 3510. The training engine 508 may use a base data set of historical device-generated information (e.g., generated from the medical device), historical device-based medical coding information, historical reviewed medical coding information, historical electronic medical records (EMRs), historical predicted biometric signatures, historical device-based biometric signatures, historical signature comparisons, historical signature indicators, historical emergency biometric signatures, historical emergency comparisons, historical emergency indicators, and any other suitable historical information. The training engine 3508 may be in communication with the training servers 3506. The training engine 3508 may be located on the training servers 3506.

[0435] The training engine 3508 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, an Internet of Things (IoT) node or sensor, any other suitable computing device, or any combination of the above. The training engine 3508 may be cloud-based or be a real-time software platform, and it may include privacy -enhancing, privacy-preserving, or privacy modifying software or protocols (e.g., anonymization, pseudonymization, or other), and/or include security software or protocols. Using training data that includes training inputs and corresponding target outputs, the one or more machine learning models 3510 may refer to model artifacts created by the training engine 3508. The training engine 3508 may find patterns in the training data that map the training input to the target output and generate the machine learning models 3510 that identify, store, or use these patterns. Although depicted separately from the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the training engine 3508, and the machine learning models 3510 may reside on the medical system 3100. Alternatively, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the training engine 3508, and the machine learning models 3510 may reside on the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, any other suitable computer device or server, or any combination thereof.

[0436] The machine learning models 3510 may include one or more neural networks, such as an image classifier, a recurrent neural network, a convolutional network, a generative adversarial network, a fully connected neural network, any other suitable network, or combination thereof. In some embodiments, the machine learning models 3510 may be composed of a single level of linear or non-linear operations or may include multiple levels of non-linear operations. For example, the machine learning models 3510 may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neural nodes. [0437] Any of the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to receive device-generated information from a medical device. The device-generated information may be information generated by the medical device. The medical device may include the medical device 3102. The medical device 3102 may include the medical system 3100. The device generated information can include information obtained by the electromechanical device 3104, the sensor 3106, the camera 3108, the imaging device 3110, any other portion of the medical device 3102, any separate or remote electromechanical device, any separate or remote sensor, any separate remote camera, any separate or remote imaging device, any other suitable device, or any combination thereof. The device-generated information may include vital sign information, such as heart rate, blood oxygen content, blood pressure, or any other suitable vital sign. The device-generated information may include images, such as MRI images, X-ray images, video camera images, still camera images, infrared images, or any other suitable images. The device-generated information may also include performance information (i.e., information relating to the physical performance of the user while the user operates a medical device), such as a rate of pedaling of a physical therapy cycle, a slope of a treadmill, a force applied to a strain-gauge, a weight lifted, a (simulated) distance traveled on a treadmill, or any other suitable performance information. The device-generated information may include medical device use information, such as a location of the medical device 3102, a healthcare provider associated with the medical device 3102, a practice group associated with the medical device 3102, a time of day that the medical device 3102 was used, a date that the medical device 3102 was used, a duration that the medical device 3102 was used, or any other suitable medical device use information.

[0438] Any of the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to use the device-generated information to determine device-based medical coding information. Determining device-based medical coding information can include cross- referencing information about actions performed by or with the medical device 3102 contained within the device-generated information with a reference list associating the actions performed by or with the medical device 3102 with certain medical codes. The reference list can be stored on the clinic server 3302, as part of the cloud-based learning system 3504, or on any other suitable server, database, or system. Determining device- based medical coding information can include identifying a portion of the device-generated information containing medical coding information.

[0439] Any of the medical system 3100, the computing device 3112, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof, may be configured to receive reviewed medical coding information. The reviewed medical coding information can include medical coding information reviewed or entered by a clinic operator. Reviewed medical coding information can include information about previously performed medical processes, procedures, surgeries, or any other suitable reviewed coding information. The reviewed medical coding information can be medical coding information that a clinic operator has reviewed on a computing device or entered into a computing device. For example, a surgeon can review and revise, on a computing device, medical coding information about a surgery that the surgeon performed on a patient (user).

[0440] Any of the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to generate a predicted biometric signature (i.e., a first biometric signature). The predicted biometric signature may include and/or be determined by a kinesiological signature. For example, the predicted biometric signature can include a predicted movement such as a range of motion of a user's joint, such as a knee, an elbow, a neck, a spine, or any other suitable joint or muscle of a human. The predicted biometric signature may be based at least in part on historical information. In an example where a patient is using a physical therapy cycle 3200, such cycle preferably located at the patient’s home or residence, as part of telemedicine-enabled or -mediated rehabilitation therapy, then, using past camera images of the user taken by the device, the predicted biometric signature can include an expected image of the user. The predicted biometric signature may be based at least in part on the reviewed medical coding information. For example, where a user has undergone a specific back surgery, the predicted biometric signature may include an MRI image of the user's upper back, such image showing evidence of that surgery. The predicted biometric signature may be based at least in part on the device-based medical coding information. For example, if the device-based medical coding information indicates that the user has undergone an upper-back MRI, the predicted biometric signature may be based at least in part on how the image of the upper back is expected to appear based on other historical information, such as past surgeries identified using the reviewed medical coding information. The predicted biometric signature may be based at least in part on Electronic Medical Records (EMRs). For example, the predicted biometric signature may be based at least in part on a height value and a weight value entered into the EMRs. The predicted biometric signature may be based at least in part on historical performance information (i.e., performance information generated in the past relating to a specific user or other users). For example, the predicted biometric signature may be based at least in part on a determination that a patient's performance on the physical therapy cycle 3200 should be within a certain range of the patient's last performance on the physical therapy cycle 3200. The determination may be modified using the amount of time since the patient has last used the physical therapy cycle 3200. The predicted biometric signature may be derived from any other suitable information, or any combination of any of the previous examples of information from which the predicted biometric signature is derived. Further, if the predicted biometric signature includes a kinesiological signature, the predicted biometric signature may be derived from any other suitable information, or any combination of any of the previous examples of information from which the predicted biometric signature is derived. For example, reviewed medical coding information relating to a knee surgery may be used to determine knee joint motion.

[0441] Any of the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to, using the device-generated information, generate a device-based biometric signature (i.e., a second biometric signature). The device-based biometric signature may be a kinesiological signature. For example, the device-based biometric signature can include the movement and joint range of a user's knee. The device-based biometric signature may be based at least in part on the device-based medical coding information. For example, where the device-based medical coding information suggests that the user has undergone an upper-back MRI, the device-based biometric signature may be based at least in part on the device-generated information about the upper back. The device-based biometric signature may be based at least in part on the performance information. For example, the device-based biometric signature may be derived from a rate of pedaling of a physical therapy cycle 3200, a slope of a treadmill, a force applied to a strain-gauge, a weight lifted, a (simulated) distance traveled on a treadmill, or any other suitable performance information. The device-based biometric signature may be derived from images included in the device-generated information, such as MRI images, X-ray images, video camera images, still camera images, infrared images, or any other suitable images. The device-based biometric signature may be derived from any other suitable information, or any combination of any of the previous examples of information upon with the device-based biometric signature is based. Further, if the device-based biometric signature includes a kinesiological signature, the device-based biometric signature may be derived from any other suitable information, or any combination of any of the previous examples of information upon with the device-based biometric signature is based. For example, camera images may be used to determine knee joint motion as a kinesiological signature embodiment of the device- based biometric signature. [0442] Any of the medical system 3100, the computing device 3112 of the medical system 3100, clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to use the predicted biometric signature and the device-based biometric signature to generate a signature comparison. The signature comparison can include differences in terms of degrees between the predicted biometric signature and the device-based biometric signature. For example, if the predicted biometric signature includes height information for a user with a value of 5 feet 4 inches and the device-based biometric signature includes height information for a user with a value of 5 feet 5 inches, the degree of difference between the predicted biometric signature and the device-based biometric signature may be indicated to be below a FWA threshold value. However, if the predicted biometric signature includes height information for a user with a value of 5 feet 4 inches and the device-based biometric signature includes height information for a user with a value of 5 feet 9 inches tall, the degree of difference between the predicted biometric signature and the device-based biometric signature may be noted to be above the FWA threshold value.

[0443] Any of the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to use the signature comparison to generate a signature indicator. To indicate an incorrect user, the signature indicator can include flagging when the signature comparison is outside an acceptable user threshold. For example, if the predicted biometric signature includes height information for a user with a height value of 5 feet 4 inches tall and the device-based biometric signature includes height information for a user with a value of 5 feet 9 inches tall, a signature indicator may be generated. The signature indicator may indicate that the difference between the predicted biometric signature and the device-based biometric signature is above the FWA threshold value. This difference may be the result of an incorrect user using the medical device. Another example includes generating a signature indicator in response to differences in between the predicted biometric signature and the device-based biometric signature, as derived from the performance metric information and the vital sign information, such that the processor 3114 determines that the differences are above a FWA threshold. In response to this determination, the processor 3114 generates the signature indicator. In this example, a post-knee surgery user walked a mile in 45 minutes on a treadmill with an average heartrate of 190 beats per minute (bpm) (i.e., the user straggled to walk a mile) and the same user later walked 5 miles on the treadmill in 45 minutes with a heartrate of 130 bpm (i.e., the user did not straggle to walk more than a mile in the same time). Another example includes a camera image displaying different images of users for the same billing user, as determined by using facial recognition software. Another example includes a user with a low range of movement in his knee joint on a first day and the same user with a high range of movement in his knee joint on a consecutive day (i.e., a kinesiological signature above the FWA threshold). The signature indicator can include flagging if the differences are determined to be the result of any errors or inconsistencies in the EMRs or reviewed medical coding information. For example, if the predicted biometric signature is based on a certain type of surgery, and the device-based biometric signature is not consistent with such surgery (i.e., consistent with a less-intense surgery — perhaps one not requiring as intense or expensive a physical therapy regimen), a signature indicator may be generated. The signature indicator may be transmitted to an operator to indicate that there is an error or an inconsistency in the EMRs or reviewed medical coding information.

[0444] Any of the medical system 3100, the computing device 3112, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to transmit the signature indicator. For example, any of the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may transmit the signature indicator (e.g., a flag) to the medical system 3100, the computing device 3112 of the medical system 3100, clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof. [0445] The signature indicator may be used by the receiving system or the server. In one exemplary embodiment, at the medical system 3100, the signature indicator can be used to validate information and/or to set a flag to inform an operator of the medical system 3100 or medical device 3102 that there is a biometric signature mismatch and, for example, the wrong therapy may have been prescribed to a patient. In another exemplary embodiment, the clinic server 3302 can use the signature indicator to validate information and/or to determine whether to transmit a message to an operator or administrator. The message may include information indicating a biometric signature mismatch and/or information improperly entered into the EMR database 3404. In another exemplary embodiment, the biller server 3406 may use the signature indicator to validate information received or sent and/or to not send the medical coding information to the claim adjudication server 3408 until the biometric signature is matched. In another exemplary embodiment, the claim adjudication server 3408 may use the may use the signature indicator to (1) validate information received; (2) determine that a flag should be added to the medical coding information prior to transmitting the medical coding information to the FWA server 3410 and/or the payment server 3412; or (3) receive additional information from the FWA server 3410. In another exemplary embodiment, the FWA server 410 can use the signature indicator to ( 1) validate information received, (2) determine whether to transmit a message, and/or (3) make a determination of whether to flag the medical coding information as fraudulent and transmit a message to initiate a FWA investigation. In another exemplary embodiment, the payment server 3412 can use the signature indicator to validate information received and/or to determine whether to pay the medical service provider. In another exemplary embodiment, the training server 3506 and/or the training engine 3508 can use the signature indicator for further machine learning activities (i.e., by increasing the size of the dataset every time a signature indicator is generated). [0446] Any of the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to generate an emergency biometric signature for use in detecting and responding to an emergency event. When a user is undergoing telemedicine-enabled or -mediated rehabilitation therapy without direct supervision by a trained medical professional, automatically (including, without limitation, through means of artificial intelligence and/or machine learning) recognizing and responding to emergency events may be desirable in the event that an emergency situation occurs. The emergency biometric signature can be derived from the predicted biometric signature. The emergency biometric signature may include vital sign information (a user's heart rate or blood pressure being too high or too low), imagery (a user's face turning purple), or any other suitable information. In an example where a patient is using a physical therapy cycle 3200 located at the patient’s home or residence to undergo telemedicine-enabled or -mediated rehabilitation therapy, the emergency biometric signature can include a value of a heart-rate of a user that is above an emergency threshold value. The emergency threshold may be derived from the predicted biometric signature. The value above the emergency threshold value may indicate an emergency condition. The emergency biometric signature can include a kinesiological signature, such as where the emergency biometric signature includes a knee joint having a range of greater than 180°. Non-limiting examples of emergency conditions include broken bones, heart attacks, and blood loss.

[0447] Any of the medical system 3100, the computing device 3112, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to use the device-based biometric signature and/or an emergency biometric signature to generate an emergency comparison. The emergency comparison can be derived from vital sign information, imagery, or any other suitable information. For example, a device-based biometric signature including a heart rate value of 0 bpm can be compared to an emergency biometric signature including an emergency range of heart rate values of 0-40 bpm. In this example, the emergency comparison indicates that the device-based biometric signature heart rate is within the emergency range. As another example, a device-based biometric signature including a user's face being a shade of purple can be compared to an emergency biometric signature including an emergency range of shades of the user's face. In this example, the emergency comparison indicates that the shade of the user's face is within the emergency range. As yet another example, a device-based biometric signature in which the range of motion of the user's joint has extended to 270° canbe compared to an emergency biometric signature in which an emergency range of knee joint extension includes values greater than 180°. In this example, the emergency comparison indicates that the knee joint range is in the emergency range.

[0448] Any of the medical system 3100, the computing device 3112, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to use the emergency comparison to generate an emergency indicator. The emergency indicator can include whether an emergency biometric signature matches a device-based biometric signature. Examples of the emergency indicators include flags and saved variables. For example, the emergency indicator canbe generated when the comparison indicates a similarity or overlap between the device-based biometric signature and the emergency biometric signature. For example, the emergency indicator can be derived from the emergency comparison if the emergency comparison indicates that the knee joint range is in the emergency range.

[0449] Any of the medical system 3100, the computing device 3112, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may be configured to transmit the emergency indicator. For example, any of the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof may transmit the emergency indicator to the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, an emergency services system or computer, any other suitable computing device, or any combination thereof. Further, the biometric information may also be transmitted to provide emergency or clinic services with information about the nature of the emergency. Further, the medical system 3100, the computing device 3112 of the medical system 3100, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, an emergency services system or computer, any other suitable computing device, or any combination thereof can activate an output device 3120, such as an alarm system. The alarm system can include a speaker, a siren, a system that contacts emergency services (e.g., to summon an ambulance), a flashing light, any other suitable alarm component, or any combination thereof.

[0450] FIG. 21 is not intended to be limiting; the medical claim processing system 3500, the medical system 3100, computing device 3112, the clinic server 3302, the clinic server 3302, the computing device 3304, the cloud-based learning system 3504, and any sub-components thereof may include more or fewer components than those illustrated in FIG. 21.

[0451] FIGS. 22A and 22B illustrate a computer-implemented method 3600 for processing medical claims. The method 3600 may be performed on the medical system 3100, the computing device 3112, the clinic server 3302, the biller server 3406, the claim adjudication server 3408, the FWA server 3410, the payment server 3412, the training server 3506, the training engine 3508, any other suitable computing device, or any combination thereof. The method 3600 may be implemented on a processor, such as the processor 3306, configured to perform the steps of the method 3600. The method 3600 may be implemented on a system, such as the medical system 3100 or the clinic server system 3300, that includes a processor, such as the processor 3306, and a memory device, such as the memory device 3308. The method 3600 may be implemented on the clinic server system 3300. The method 3600 may include operations that are implemented in instructions stored in a memory device, such as the memory device 3308, and executed by a processor, such as the processor 3306, of a computing device, such as the computing device 3304. The steps of the method 3600 may be stored in a non transient computer-readable storage medium.

[0452] At step 3602, the method 3600 can include receiving device-generated information from a medical device, such as the medical device 3102. For example, the clinic server 3302 can receive (1) knee angle information from a goniometer attached to a knee of a user of the physical therapy cycle 3200; and (2) pedal speed information and force information from the physical therapy cycle 3200. After the clinic server 3302 receives the device-generated information from the medical device 3102, the clinic server 3302 can proceed to step 3604 or to step 3610. Alternatively, the clinic server 3302 can proceed to steps 3604 and 3610.

[0453] At step 3604, the method 3600 can include using the device-generated information to determine device- based medical coding information. For example, the clinic server 3302 can use the pedal speed information and/or force information from the physical therapy cycle to determine that the user is undergoing a one-hour therapy session. The clinic server 3302 can access the EMRs associated with the user to determine which information is relevant to the therapy session (e.g., that the user has a prior right knee injury). The clinic server 3302 can determine medical coding information associated with one-hour therapy sessions for a right knee injury. After the clinic server 3302 determines the device-based medical information, the clinic server 3302 can proceed to step 3606.

[0454] At step 3606, the method 3600 can include receiving reviewed medical coding information. For example, the clinic server 3302 can receive information input by a doctor that the user has an injury to the user’ s left knee. After the clinic server 3302 receives the reviewed medical coding information, the clinic server 3302 can proceed to step 3608.

[0455] At step 3608, the method 3600 can include receiving electronic medical records (EMRs) for a user of a medical device 3102. For example, the clinic server 3302 can receive information from the EMRs. The information can indicate that the user has an injury to the user’s left knee. After the clinic server 3302 receives the EMRs, the clinic server 3302 can proceed to step 3610.

[0456] At step 3610, the method 3600 can include generating a first biometric signature (i.e., a predicted biometric signature). The clinic server 3302 can generate the first biometric signature. The first biometric signature may be a kinesiological signature of a user. For example, the clinic server 3302 can use the injury and the past performance of the user to generate a first biometric signature, which may include a first kinesiological signature (i.e., an emergency kinesiological signature) having a predicted left knee joint range of motionbetween approximately 130°-140° and a predicted right knee joint range of motion between approximately 165-170°. After the clinic server 3302 generates the first biometric signature, the clinic server 3302 can proceed to step 3612.

[0457] At step 3612, the method 3600 can include generating, using the device -generated information, a second biometric signature (i.e., a device-based biometric signature). The second biometric signature may be a kinesiological signature. For example, the clinic server 3302 can generate, using the measurements from the goniometer, a second biometric signature including a second kinesiological signature (i.e., a device-based kinesiological signature) having a left knee joint range of motion of approximately 170° and a right knee joint range of motion of approximately 135°. After the clinic server 3302 generates the second biometric signature, the clinic server 3302 can proceed to step 3614.

[0458] At step 3614, the method 3600 can include comparing the first and second biometric signatures. For example, the clinic server 3302 can compare the predicted left knee joint range of motion (i.e., the first biometric signature having a predicted a range of motion of approximately 130°-140°) and the measured a left knee joint range of motion (i.e., the second biometric signature with a measured range of motion of approximately 170°). After the clinic server 3302 generates compares the first and second biometric signatures, the clinic server 3302 can proceed to step 3616.

[0459] At step 3616, the method 3600 can include generating, using the first biometric signature and the second biometric signature, a signature comparison. For example, the clinic server 3302 can generate a signature comparison showing that the user’s left knee joint range of motion is outside of the expected range of motion forthe user’s left knee joint (e.g., approximately 30° above the expected maximum range of motion). The clinic server 3302 can generate one or more signature comparisons. For example, the clinic server 302 can generate a second signal comparison that the user’s right knee joint range of motion is outside of the expected range of motion for the user’ s right knee joint (e.g., approximately 30° below the expected minimum range of motion). After the clinic server 3302 generates the signature comparison, the clinic server 3302 can proceed to step 3618. [0460] At step 3618, the method 3600 can include generating, using the signature comparison, a signature indicator (e.g., a variable or flag that indicates whether the differences between the first and second biometric signatures exceed a FWA threshold value). The signature indicator can include flagging if the differences are determined to be the result of an incorrect user. For example, the clinic server 3302 can use the left knee joint range of motion being outside of an expected range of motion threshold to generate a signature indicator flagging that the user may be an incorrect user, that there may be an error in the medical records, that the goniometer measurements may have been incorrect (e.g., another user’s medical records) resulting from an operator error, or that any other suitable error has occurred. After the clinic server 3302 generates the signature indicator, the clinic server 3302 can proceed to step 3620.

[0461] At step 3620, the method 3600 can include transmitting the signature indicator. For example, the clinic server 3302 may transmit the signature indicator to the biller server 3406. After the clinic server 3302 transmits the signature indicator, the clinic server 3302 can end the method or proceed to step 3622.

[0462] At step 3622, the method 3600 can include generating an emergency biometric signature including information indicative of an emergency event (e.g., a heart attack, broken bone, blood loss, etc.). For example, the clinic server 3302 can generate an emergency biometric signature having a knee joint range of motion in excess of 185°. After the clinic server 3302 generates the emergency biometric signature, the clinic server 3302 can proceed to step 3624.

[0463] At step 3624, the method 3600 can include using the second biometric signature and the emergency biometric signature to generate an emergency comparison. For example, if the emergency biometric signature is generated when a knee joint range of motion of a user operating the physical therapy cycle 3200 is greater than an emergency threshold of 185° and the second biometric signature determines that a knee joint range of motion of a user operating the physical therapy cycle 3200 is approximately 270°, the user has exceeded the emergency threshold and the clinic server 3302 can generate the emergency comparison. After the clinic server 3302 generates the emergency comparison, the clinic server 3302 can proceed to step 3626.

[0464] At step 3626, the method 3600 can include, using the emergency comparison to generate an emergency indicator. For example, using the second biometric signature having a knee joint range of motion exceeding the emergency threshold of the emergency biometric signature (e.g., the user’s range of motion is 85° greater than the emergency biometric signature), the clinic server 3302 can determine that there is an emergency condition. After the clinic server 3302 generates the emergency indicator, the clinic server 3302 can proceed to step 3628. [0465] At step 3628, the method 3600 can include transmitting the emergency indicator. For example, in response to the generation of the emergency indicator, the clinic server 3302 can transmit the emergency indicator to an on-site registered nurse. The emergency indicator may include information, device-generated information, EMRs, the emergency comparison, any other suitable information, or any combination thereof. [0466] FIGS. 6A and 6B are not intended to be limiting; the method 600 can include more or fewer steps and/or processes than those illustrated in FIG. 6. Further, the order of the steps of the method 600 is not intended to be limiting; the steps can be arranged in any suitable order. Any or all of the steps of method 600 may be implemented during a telemedicine session or at any other desired time.

[0467] FIG. 26 shows an example computer system 3800 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 3800 may include a computing device and correspond to an assistance interface, a reporting interface, a supervisoiy interface, a clinician interface, a server (including an AI engine), a patient interface, an ambulatory sensor, a goniometer, a treatment device 3010, a medical device 3102, a pressure sensor, or any suitable component. The computer system 3800 may be capable of executing instructions implementing the one or more machine learning models of the artificial intelligence engine. The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet, including via the cloud or a peer-to-peer network. The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a wearable (e.g., wristband), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[0468] The computer system 3800 includes a processing device 3802, a main memory 3804 (e.g., read-only memory (ROM), flash memory, solid state drives (SSDs), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 3806 (e.g., flash memory, solid state drives (SSDs), static random access memory (SRAM)), and a data storage device 3808, which communicate with each other via a bus 810.

[0469] Processing device 3802 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 3802 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 3802 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 3802 is configured to execute instructions for performing any of the operations and steps discussed herein.

[0470] The computer system 3800 may further include a network interface device 3812. The computer system 3800 also may include a video display 3814 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), one or more input devices 3816 (e.g., a keyboard and/or a mouse or a gaming-like control), and one or more speakers 3818 (e.g., a speaker). In one illustrative example, the video display 3814 and the input device(s) 3816 may be combined into a single component or device (e.g., an LCD touch screen).

[0471] The data storage device 3816 may include a computer-readable medium 3820 on which the instructions 3822 embodying any one or more of the methods, operations, or functions described herein is stored. The instructions 3822 may also reside, completely or at least partially, within the main memory 3804 and/or within the processing device 3802 during execution thereof by the computer system 3800. As such, the main memory 3804 and the processing device 3802 also constitute computer-readable media. The instructions 3822 may further be transmitted or received over a network via the network interface device 3812. [0472] While the computer- readable storage medium 3820 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer- readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0473] FIG. 26 is not intended to be limiting; the system 3800 may include more or fewer components than those illustrated in FIG. 26.

[0474] The term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer- readable storage medium” shall also be taken to include any medium capable of storing, encoding or carrying a set of instructions for execution by the machine and causing the machine to perform any one or more of the methodologies of the present disclosure. The term “computer- readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0475] Any of the systems and methods described in this disclosure may be used in connection with rehabilitation. Unless expressly stated otherwise, is to be understood that rehabilitation includes prehabilitation (also referred to as "pre-habilitation" or "prehab"). Prehabilitation may be used as a preventative procedure or as a pre-surgical or pre-treatment procedure. Prehabilitation may include any action performed by or on a patient (or directed to be performed by or on a patient, including, without limitation, remotely or distally through telemedicine) to, without limitation, prevent or reduce a likelihood of injury (e.g., prior to the occurrence of the injury); improve recovery time subsequent to surgery; improve strength subsequent to surgery; or any of the foregoing with respect to any non-surgical clinical treatment plan to be undertaken for the purpose of ameliorating or mitigating injury, dysfunction, or other negative consequence of surgical or non-surgical treatment on any external or internal part of a patient's body. For example, a mastectomy may require prehabilitation to strengthen muscles or muscle groups affected directly or indirectly by the mastectomy. As a further non-limiting example, the removal of an intestinal tumor, the repair of a hernia, open-heart surgery or other procedures performed on internal organs or structures, whether to repair those organs or structures, to excise them or parts of them, to treat them, etc., can require cutting through and harming numerous muscles and muscle groups in or about, without limitation, the abdomen, the ribs and/or the thoracic cavity. Prehabilitation can improve a patient's speed of recovery, measure of quality of life, level of pain, etc. in all the foregoing procedures. In one embodiment of prehabilitation, a pre-surgical procedure or a pre-non-surgical-treatment may include one or more sets of exercises for a patient to perform prior to such procedure or treatment. The patient may prepare an area of his or her body for the surgical procedure by performing the one or more sets of exercises, thereby strengthening muscle groups, improving existing and/or establishing new muscle memory, enhancing mobility, improving blood flow, and/or the like.

[0476] In some embodiments, the systems and methods described herein may use artificial intelligence and/or machine learning to generate a prehabilitation treatment plan for a user. Additionally, or alternatively, the systems and methods described herein may use artificial intelligence and/or machine learning to recommend an optimal exercise machine configuration for a user. For example, a data model may be trained on historical data such that the data model may be provided with input data relating to the user and may generate output data indicative of a recommended exercise machine configuration for a specific user. Additionally, or alternatively, the systems and methods described herein may use machine learning and/or artificial intelligence to generate other types of recommendations relating to prehabilitation, such as recommended reading material to educate the patient, a recommended health professional specialist to contact, and/or the like.

[0477] Consistent with the above disclosure, the examples of systems and method enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples.

[0478] Clause 1.2. A computer-implemented system for processing medical claims, comprising: a medical device configured to be manipulated by a user while the user performs a treatment plan; a patient interface associated with the medical device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session; and a processor configured to: during the telemedicine session, receive device-generated information from the medical device; generate a first biometric signature; using the device-generated information, generate a second biometric signature; using the first and second biometric signatures, generate a signature comparison; using the signature comparison, generate a signature indicator; and transmit the signature indicator.

[0479] Clause 2.2. The computer-implemented system of any clause herein, wherein: the device-generated information is generated by the medical device; using the device-generated information, the processor is further configured to determine device-based medical coding information; and generating the second biometric signature uses the device-based medical coding information.

[0480] Clause 3.2. The computer-implemented system of any clause herein, wherein the processor is further configured to receive reviewed medical coding information; and wherein generating the first biometric signature uses the reviewed medical coding information.

[0481] Clause 4.2. The computer-implemented system of any clause herein, wherein the processor is further configured to receive electronic medical records pertaining to the user of the medical device; and wherein generating the first biometric signature uses the electronic medical records.

[0482] Clause 5.2. The computer-implemented system of any clause herein, wherein the processor is further configured to: using the second biometric signature and an emergency biometric signature, generate an emergency comparison; using the emergency comparison, generate an emergency indicator; and transmit the emergency indicator.

[0483] Clause 6.2. A system for processing medical claims, comprising: a processor configured to: receive device -generated information from a medical device; generate a first biometric signature; using the device-generated information, generate a second biometric signature; using the first biometric signature and the second biometric signature, compare the signatures; using the first and second biometric signatures, generate a signature comparison; using the signature comparison, generate a signature indicator; and transmit the signature indicator.

[0484] Clause 7.2. The system of any clause herein, wherein the device-generated information is generated by the medical device.

[0485] Clause 8.2. The system of any clause herein, wherein, using the device-generated information, the processor is further configured to determine device-based medical coding information; and wherein generating the second biometric signature uses the device-based medical coding information. [0486] Clause 9.2. The system of any clause herein, wherein the processor is further configured to receive reviewed medical coding information; and wherein generating the first biometric signature uses the reviewed medical coding information.

[0487] Clause 10.2. The system of any clause herein , wherein the processor is further configured to receive electronic medical records pertaining to a user of the medical device; and wherein generating the first biometric signature uses the electronic medical records.

[0488] Clause 11.2. The system of any clause herein, wherein the processor is further configured to: using the second biometric signature and an emergency biometric signature, generate an emergency comparison; using the emergency comparison, generate an emergency indicator; and transmit the emergency indicator.

[0489] Clause 12.2. The system of any clause herein, wherein the processor is further configured to generate the emergency biometric signature.

[0490] Clause 13.2. The system of any clause herein, wherein the device-generated information includes at least one of vital sign information, images, and medical device use information.

[0491] Clause 14.2. The system of any clause herein , wherein the device-generated information includes performance information; and wherein generating the second biometric signature uses the performance information.

[0492] Clause 15.2. The system of any clause herein, wherein generating the first biometric signature uses historical performance information.

[0493] Clause 16.2. The system of any clause herein, wherein the first biometric signature includes a first kinesiological signature; and wherein the second biometric signature includes a second kinesiological signature.

[0494] Clause 17.2. The system of any clause herein, further comprising a memory device operatively coupled to the processor, wherein the memory device stores instructions, and wherein the processor is configured to execute the instructions.

[0495] Clause 18.2. A method for processing medical claims, comprising: receiving device-generated information from a medical device; generating a first biometric signature; using the device-generated information, generating a second biometric signature; using the first biometric signature and the second biometric signature, generating a signature comparison; using the signature comparison, generating a signature indicator; and transmitting the signature indicator.

[0496] Clause 19.2. The method of any clause herein, wherein the device-generated information is generated by the medical device.

[0497] Clause 20.2. The method of any clause herein, further comprising using the device-generated information to determine device-based medical coding information; wherein generating the second biometric signature uses the device-based medical coding information. [0498] Clause 21.2. The method of any clause herein, further comprising receiving reviewed medical coding information; wherein generating the first biometric signature uses the reviewed medical coding information. [0499] Clause 22.2. The method of any clause herein, further comprising receiving electronic medical records pertaining to a user of the medical device; wherein generating the first biometric signature uses the electronic medical records.

[0500] Clause 23.2. The method of any clause herein, further comprising: using the second biometric signature and an emergency biometric signature to generate an emergency comparison; using the emergency comparison to generate an emergency indicator; and transmitting the emergency indicator.

[0501] Clause 24.2. The method of any clause herein, further comprising generating the emergency biometric signature.

[0502] Clause 25.2. The method of any clause herein, wherein the device-generated information includes at least one of vital sign information, images, and medical device use information.

[0503] Clause 26.2. The method of any clause herein, wherein the device-generated information includes performance information; and wherein generating the second biometric signature uses the performance information.

[0504] Clause 27.2. The method of any clause herein, wherein generating the first biometric signature uses historical performance information.

[0505] Clause 28.2. The method of any clause herein, wherein the first biometric signature includes a first kinesiological signature; and wherein the second biometric signature includes a second kinesiological signature.

[0506] Clause 29.2. A tangible, non-transitoiy computer-readable storage medium storing instructions that, when executed, cause a processor to: receive device-generated information from a medical device; generate a first biometric signature; using the device-generated information, generate a second biometric signature; using the first biometric signature and the second biometric signature, generate a signature comparison; using the signature comparison, generate a signature indicator; and transmit the signature indicator.

[0507] Clause 30.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the device-generated information is generated by the medical device.

[0508] Clause 31.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein using the device-generated information, the instructions further cause the processor to determine device-based medical coding information; and wherein generating the second biometric signature uses the device-based medical coding information. [0509] Clause 32.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processor to receive electronic medical records pertaining to a user of the medical device; and wherein generating the first biometric signature uses the electronic medical records.

[0510] Clause 33.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processor to: using the second biometric signature and an emergency biometric signature, generate an emergency comparison; using the emergency comparison, generate an emergency indicator; and transmit the emergency indicator.

[0511] Clause 34.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the instructions further cause the processor to generate the emergency biometric signature.

[0512] Clause 35.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the device-generated information includes at least one of vital sign information, images, and medical device use information.

[0513] Clause 36.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the device-generated information includes performance information; and wherein generating the second biometric signature uses the performance information.

[0514] Clause 37.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the device-generated information includes performance information; and wherein generating the second biometric signature uses the performance information.

[0515] Clause 38.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein generating the first biometric signature uses historical performance information.

[0516] Clause 39.2. The tangible, non-transitoiy computer-readable storage medium of any clause herein, wherein the first biometric signature includes a first kinesiological signature; and wherein the second biometric signature includes a second kinesiological signature.

[0517] No part of the description in this application should be read as implying that any particular element, step, or function is an essential element that must be included in the claim scope. The scope of patented subject matter is defined only by the claims. Moreover, none of the claims is intended to invoke 35 U.S.C. § 112(f) unless the exact words “means for” are followed by a participle.

[0518] The foregoing description, for purposes of explanation, use specific nomenclature to provide a thorough understanding of the described embodiments. However, it should be apparent to one skilled in the art that the specific details are not required to practice the described embodiments. Thus, the foregoing descriptions of specific embodiments are presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the described embodiments to the precise forms disclosed. It should be apparent to one of ordinary skill in the art that many modifications and variations are possible in view of the above teachings. [0519] The above discussion is meant to be illustrative of the principles and various embodiments of the present invention. Once the above disclosure is fully appreciated, numerous variations and modifications will become apparent to those skilled in the art. It is intended that the following claims be interpreted to embrace all such variations and modifications.

METHOD AND SYSTEM TO ANALYTICALLY OPTIMIZE TELEHEALTH PRACTICE-BASED BILLING PROCESSES AND REVENUE WHILE ENABLING REGULATORY COMPLIANCE

[0520] Determining a treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; geographic; diagnostic; measurement- or test-based; medically historic; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment apparatus, an amount of force exerted on a portion of the treatment apparatus, a range of motion achieved on the treatment apparatus, a movement speed of a portion of the treatment apparatus, an indication of a plurality of pain levels using the treatment apparatus, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, or some combination thereof. It may be desirable to process the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[0521] Further, another technical problem may involve distally treating, via a computing device during a telemedicine or telehealth session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling the control of, from the different location, a treatment apparatus used by the patient at the location at which the patient is located. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a physical therapist or other medical professional may prescribe a treatment apparatus to the patient to use to perform a treatment protocol at their residence or any mobile location or temporary domicile. A medical professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, or the like. A medical professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

[0522] Since the physical therapist or other medical professional is located in a different location from the patient and the treatment apparatus, it may be technically challenging for the physical therapist or other medical professional to monitor the patient’s actual progress (as opposed to relying on the patient’s word about their progress) using the treatment apparatus, modify the treatment plan according to the patient’s progress, adapt the treatment apparatus to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

[0523] Accordingly, some embodiments of the present disclosure pertain to using artificial intelligence and/or machine learning to assign patients to cohorts and to dynamically control a treatment apparatus based on the assignment during an adaptive telemedical session. In some embodiments, numerous treatment apparatuses may be provided to patients. The treatment apparatuses may be used by the patients to perform treatment plans in their residences, at a gym, at a rehabilitative center, at a hospital, or any suitable location, including permanent or temporary domiciles. In some embodiments, the treatment apparatuses may be communicatively coupled to a server. Characteristics of the patients may be collected before, during, and/or after the patients perform the treatment plans. For example, the personal information, the performance information, and the measurement information may be collected before, during, and/or after the person performs the treatment plans. The results (e.g., improved performance or decreased performance) of performing each exercise may be collected from the treatment apparatus throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g., position of pedal, amount of resistance, etc.) of the treatment apparatus may be collected before, during, and/or after the treatment plan is performed.

[0524] Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be timestamped and may be correlated with a particular step in the treatment plan. Such a technique may enable determining which steps in the treatment plan lead to desired results (e.g., improved muscle strength, range of motion, etc.) and which steps lead to diminishing returns (e.g., continuing to exercise after 3 minutes actually delays or harms recovery).

[0525] Data may be collected from the treatment apparatuses and/or any suitable computing device (e.g., computing devices where personal information is entered, such as a clinician interface or patient interface) over time as the patients use the treatment apparatuses to perform the various treatment plans. The data that may be collected may include the characteristics of the patients, the treatment plans performed by the patients, and the results of the treatment plans.

[0526] In some embodiments, the data may be processed to group certain people into cohorts. The people may be grouped by people having certain or selected similar characteristics, treatment plans, and results of performing the treatment plans. For example, athletic people having no medical conditions who perform a treatment plan (e.g., use the treatment apparatus for 30 minutes a day 5 times a week for 3 weeks) and who fully recover may be grouped into a first cohort. Older people who are classified obese and who perform a treatment plan (e.g., use the treatment plan for 10 minutes a day 3 times a week for 4 weeks) and who improve their range of motion by 75 percent may be grouped into a second cohort.

[0527] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. For example, the one or more machine learning models may be trained to receive an input of characteristics of a new patient and to output a treatment plan for the patient that results in a desired result. The machine learning models may match a pattern between the characteristics of the new patient and at least one patient of the patients included in a particular cohort. When a pattern is matched, the machine learning models may assign the new patient to the particular cohort and select the treatment plan associated with the at least one patient. The artificial intelligence engine may be configured to control, distally and based on the treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan.

[0528] As may be appreciated, the characteristics of the new patient may change as the new patient uses the treatment apparatus to perform the treatment plan. For example, the performance of the patient may improve quicker than expected for people in the cohort to which the new patient is currently assigned. Accordingly, the machine learning models may be trained to dynamically reassign, based on the changed characteristics, the new patient to a different cohort that includes people having characteristics similar to the now -changed characteristics as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, result in the patient’ s being reassigned to a different cohort with a different weight criterion. A different treatment plan may be selected for the new patient, and the treatment apparatus may be controlled, distally and based on the different treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan. Such techniques may provide the technical solution of distally controlling a treatment apparatus. Further, the techniques may lead to faster recovery times and/or better results for the patients because the treatment plan that most accurately fits their characteristics is selected and implemented, in real-time, at any given moment. Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds but greater than 2 seconds. As described herein, the term “results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions.

[0529] Depending on what result is desired, the artificial intelligence engine may be trained to output several treatment plans. For example, one result may include recovering to a threshold level (e.g., 75% range of motion) in a fastest amount of time, while another result may include fully recovering (e.g., 100% range of motion) regardless of the amount of time. The data obtained from the patients and sorted into cohorts may indicate that a first treatment plan provides the first result for people with characteristics similar to the patient’s, and that a second treatment plan provides the second result for people with characteristics similar to the patient.

[0530] Further, the artificial intelligence engine may also be trained to output treatment plans that are not optimal or sub-optimal or even inappropriate (all referred to, without limitation, as “excluded treatment plans”) for the patient. For example, if a patient has high blood pressure, a particular exercise may not be approved or suitable for the patient as it may put the patient at unnecessary risk or even induce a hypertensive crisis and, accordingly, that exercise may be flagged in the excluded treatment plan for the patient.

[0531] In some embodiments, the treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a medical professional. The medical professional may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment apparatus. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive and/or operate distally from the patient and the treatment apparatus. In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a medical professional. The video may also be accompanied by audio, text and other multimedia information. Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds but greater than 2 seconds.

[0532] Presenting the treatment plans generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the medical professional may continue to visually and/or otherwise communicate with the patient while also reviewing the treatment plans on the same user interface. The enhanced user interface may improve the medical professional’s experience using the computing device and may encourage the medical professional to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network) because the medical professional does not have to switch to another user interface screen to enter a query for a treatment plan to recommend based on the characteristics of the patient. The artificial intelligence engine provides, dynamically on the fly, the treatment plans and excluded treatment plans.

[0533] Additionally, some embodiments of the present disclosure may relate to analytically optimizing telehealth practice-based billing processes and revenue while enabling regulatory compliance. Information of a patient’s condition may be received and the information may be used to determine the procedures (e.g., the procedures may include one or more office visits, bloodwork tests, other medical tests, surgeries, biopsies, performances of exercise or exercises, therapy sessions, physical therapy sessions, lab studies, consultations, or the like) to perform on the patient. Based on the information, a treatment plan may be generated for the patient. The treatment plan may include various instructions pertaining at least to the procedures to perform for the patient’s condition. There may be an optimal way to bill the procedures and costs associated with the billing. However, there may be a set of billing procedures associated with the set of instructions. The set of billing procedures may include a set of rules pertaining to billing codes, timing, constraints, or some combination thereof that govern the order in which the procedures are allowed to be billed and, further, which procedures are allowed to be billed or which portions of a given procedure are allowed to be billed. For example, regarding timing, a test may be allowed to be conducted before surgery but not after the surgery. In his example, it may be best for the patient to conduct the test before the surgery. Accordingly, the billing sequence may include a billing code for the test before a billing code for the surgery. The constraints may pertain to an insurance regime, a medical order, laws, regulations, or the like. Regarding the order, an example may include: if procedure A is performed, then procedure B may be billed, but procedure A cannot be billed if procedure B was billed first. It may not be a trivial task to optimize a billing sequence for a treatment plan while complying with the set of rules.

[0534] It is desirable to generate a billing sequence for the patient’s treatment plan that complies with the set of rules. In addition, there are multiples of parameters to consider for a desired billing sequence. The parameters may pertain to a monetary value amount generated by the billing sequence, a patient outcome that results from the treatment plan associated with the billing sequence, a fee paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof.

[0535] The artificial intelligence engine may be trained to generate, based on the set of billing procedures, one or more billing sequences for at least a portion of or all of the instructions, where the billing sequence is tailored according to one or more of the parameters. As such, the disclosed techniques may enable medical professionals to provide, improve or come closer to achieving best practices for ethical patient care. By complying with the set of billing procedures, the disclosed techniques provide for ethical consideration of the patient’s care, while also benefiting the practice of the medical professional and benefiting the interests of insurance providers. In other words, one key goal of the disclosed techniques is to maximize both patient care quality and the degree of reimbursement for the use of ethical medical practices related thereto.

[0536] The artificial intelligence engine may pattern match to generate billing sequences and/or treatment plans tailored for a selected parameter (e.g., best outcome for the patient, maximize monetary value amount generated, etc.). Different machine learning models may be trained to generate billing sequences and/or treatment plans for different parameters. In some embodiments, one trained machine learning model that generates a first billing sequence for a first parameter (e.g., monetary value amount generated) may be linked to and feed its output to another trained machine learning model that generates a second billing sequence for a second parameter (e.g., a plan of reimbursement). Thus, the second billing sequence may be tuned for both the first parameter and the second parameter. It should be understood that any suitable combination of trained machine learning models may be used to provide billing sequences and/or treatment plans tailored to any combination of the parameters described herein, as well as other parameters contemplated and/or used in billing sequences and/or treatment plans, whether or not specifically expressed or enumerated herein.

[0537] In some embodiments, a medical professional and an insurance company may participate to provide requests pertaining to the billing sequence. For example, the medical professional and the insurance company may request to receive immediate reimbursement for the treatment plan. Accordingly, the artificial intelligence engine may be trained to generate, based on the immediate reimbursement requests, a modified billing sequence that complies with the set of billing procedures and provides for immediate reimbursement to the medical professional and the insurance company.

[0538] In some embodiments, the treatment plan may be modified by a medical professional. For example, certain procedures may be added, modified or removed. In the telehealth scenario, there are certain procedures that may not be performed due to the distal nature of a medical professional using a computing device in a different physical location than a patient.

[0539] In some embodiments, the treatment plan and the billing sequence may be transmitted to a computing device of a medical professional, insurance provider, any lawfully designated or appointed entity and/or patient. It should be noted that there may be other entities that receive the treatment plan and the billing sequence for the insurance provider and/or the patient. Such entities may include any lawfully designated or appointed entity (e.g., assignees, legally predicated designees, attomeys-in-fact, legal proxies, etc.), Thus, as used herein, it should be understood that these entities may receive information in lieu of, in addition to the insurance provider and/or the patient, or as an intermediary or interlocutor between another such lawfully designated or appointed entity and the insurance provider and/or the patient. The treatment plan and the billing sequence may be presented in a first portion of a user interface on the computing device. A video of the patient or the medical professional may be optionally presented in a second portion of the user interface on the computing device. The first portion (including the treatment plan and the billing sequence) and the second portion (including the video) may be presented concurrently on the user interface to enable to the medical professional and/or the patient to view the video and the treatment plan and the billing sequence at the same time. Such a technique may be beneficial and reduce computing resources because the user (medical professional and/or patient) does not have to minimize the user interface (including the video) in order to open another user interface which includes the treatment plan and the billing sequence.

[0540] In some embodiments, the medical professional and/or the patient may select a certain treatment plan and/or billing sequence from the user interface. Based on the selection, the treatment apparatus may be electronically controlled, either via the computing device of the patient transmitting a control signal to a controller of the treatment apparatus, or via the computing device of the medical professional transmitting a control signal to the controller of the treatment apparatus. As such, the treatment apparatus may initialize the treatment plan and configure various settings (e.g., position of pedals, speed of pedaling, amount of force required on pedals, etc.) defined by the treatment plan.

[0541] A potential technical problem may relate to the information pertaining to the patient’s medical condition being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). That is, some sources used by various medical professional entities may be installed on their local computing devices and, additionally and/or alternatively, may use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain, via interfaces exposed by APIs used by the sources, the formats used by the sources. In some embodiments, when information is received from the sources, the API may map and convert the format used by the sources to a standardized (i.e., canonical) format, language and/or encoding (“format” as used herein will be inclusive of all of these terms) used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when the artificial intelligence engine is performing any of the techniques disclosed herein. Using the information converted to a standardized format may enable a more accurate determination of the procedures to perform for the patient and/or a billing sequence to use for the patient.

[0542] To that end, the standardized information may enable generating treatment plans and/or billing sequences having a particular format that can be processed by various applications (e.g., telehealth). For example, applications, such as telehealth applications, may be executing on various computing devices of medical professionals and/or patients. The applications (e.g., standalone or web-based) may be provided by a server and may be configured to process data according to a format in which the treatment plans and the billing sequences are implemented. Accordingly, the disclosed embodiments may provide a technical solution by (i) receiving, from various sources (e.g., EMR systems), information in non-standardized and/or different formats; (ii) standardizing the information (i.e., representing the information in a canonical format); and (iii) generating, based on the standardized information, treatment plans and billing sequences having standardized formats capable of being processed by applications (e.g., telehealth applications) executing on computing devices of medical professionals and/or patients and/or their lawfully authorized designees.

[0543] Additionally, some embodiments of the present disclosure may use artificial intelligence and machine learning to create optimal patient treatment plans based on one or more of monetary value amount and patient outcomes. Optimizing for one or more of patient outcome and monetary value amount generated, while complying with a set of constraints, may be a computationally and technically challenging issue. [0544] Accordingly, the disclosed techniques provide numerous technical solutions in embodiments that enable dynamically determining one or more optimal treatment plans optimized for various parameters (e.g., monetary value amount generated, patient outcome, risk, etc.). In some embodiments, while complying with the set of constraints, an artificial intelligence engine may use one or more trained machine learning models to generate the optimal treatment plans for various parameters. The set of constraints may pertain to billing codes associated with various treatment plans, laws, regulations, timings of billing, orders of billing, and the like. As described herein, one or more of the optimal treatment plans may be selected to control, based on the selected one or more treatment plans, the treatment apparatus in real-time or near real-time while a patient uses the treatment apparatus in a telehealth or telemedicine session.

[0545] One of the parameters may include maximizing an amount of monetary value amount generated. Accordingly, in one embodiment, the artificial intelligence engine may receive information pertaining to a medical condition of the patient. Based on the information, the artificial intelligence engine may receive a set of treatment plans that, when applied to other patients having similar medical condition information, cause outcomes to be achieved by the patients. The artificial intelligence engine may receive a set of monetary value amounts associated with the set of treatment plans. A respective monetary value amount may be associated with a respective treatment plan. The artificial intelligence engine may receive the set of constraints. The artificial intelligence engine may generate optimal treatment plans for a patient, where the generating is based on one or more of the set of treatment plans, the set of monetary value amounts, and the set of constraints. Each of the optimal treatment plans complies completely or to the maximum extent possible or to a prescribed extent with the set of constraints and represents a patient outcome and an associated monetary value amount generated. The optimal treatment plans may be transmitted, in real-time or near real-time, during a telehealth or telemedicine session, to be presented on one or more computing devices of one or more medical professionals and/or one or more patients. It should be noted that the term “telehealth” as used herein will be inclusive of all of the following terms : telemedicine, teletherapeutic, telerehab, etc. It should be noted that the term “telemedicine” as used herein will be inclusive of all of the following terms: telehealth, teletherapeutic, telerehab, etc.

[0546] A user may select different monetary value amounts, and the artificial intelligence engine may generate different optimal treatment plans for those monetary value amounts. The different optimal treatment plans may represent different patient outcomes and may also comply with the set of constraints. The different optimal treatment plans may be transmitted, in real-time or near real-time, during a telehealth or telemedicine session, to be presented on a computing device of a medical professional and/or a patient.

[0547] The disclosed techniques may use one or more equations having certain parameters on a left side of the equation and certain parameters on a right side of the equation. For example, the parameters on the left side of the equation may represent a treatment plan, patient outcome, risk, and/or monetary value amount generated. The parameters on the right side of the equation may represent the set of constraints that must be complied with to ethically and/or legally bill for the treatment plan. Such an equation or equations and/or one or more parameters therein may also, without limitation, incorporate or implement appropriate mathematical, statistical and/or probabilistic algorithms as well as use computer-based subroutines, methods, operations, function calls, scripts, services, applications or programs to receive certain values and to return other values and/or results. The various parameters may be considered levers that may be adjusted to provide a desired treatment plan and/or monetary value amount generated. In some instances, it may be desirable to select an optimal treatment plan that is tailored for a desired patient outcome (e.g., best recovery, fastest recovery rate, etc.), which may effect the monetary value amount generated and the risk associated with the treatment plan. In other instances, it may be desirable to select an optimal treatment plan tailored for a desired monetary value amount generated, which may effect the treatment plan and/or the risk associated with the treatment plan.

[0548] For example, a first treatment plan may result in a first patient outcome having a low risk and resulting in a low monetary value amount generated, whereas a second treatment plan may result in a second patient outcome (better than the first patient outcome) having a higher risk and resulting in a higher monetary value amount generated than the first treatment plan. Both the first treatment plan and the second treatment plan are generated based on the set of constraints. Also, both the first treatment plan and the second treatment plan may be simultaneously presented, in real-time or near real-time, on a user interface of one or more computing devices engaged in a telehealth or telemedicine session. A user (e.g., medical professional or patient) may select either the first or second treatment plan to cause the selected treatment plan to be implemented on the treatment apparatus. In other words, the treatment apparatus may be electronically controlled based on the selected treatment plan.

[0549] Accordingly, the artificial intelligence engine may use various machine learning models, each trained to generate one or more optimal treatment plans for a different parameter, as described further below. Each of the one or more optimal treatment plans complies with the set of constraints.

[0550] The various embodiments disclosed herein may provide a technical solution to the technical problem pertaining to the patient’s medical condition information being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). The information may be converted from the format used by the sources to the standardized format used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when performing any of the techniques disclosed herein. The standardized information may enable generating optimal treatment plans, where the generating is based on treatment plans associated with the standardized information, monetary value amounts, and the set of constraints. The optimal treatment plans may be provided in a standardized format that can be processed by various applications (e.g., telehealth) executing on various computing devices of medical professionals and/or patients.

[0551] In some embodiments, the treatment apparatus may be adaptive and/or personalized because its properties, configurations, and positions may be adapted to the needs of a particular patient. For example, the pedals may be dynamically adjusted on the fly (e.g., via a telemedicine session or based on programmed configurations in response to certain measurements being detected) to increase or decrease a range of motion to comply with a treatment plan designed for the user. In some embodiments, a medical professional may adapt, remotely during a telemedicine session, the treatment apparatus to the needs of the patient by causing a control instruction to be transmitted from a server to treatment apparatus. Such adaptive nature may improve the results of recovery for a patient, furthering the goals of personalized medicine, and enabling personalization of the treatment plan on a per-individual basis. [0552] FIG. 27 shows a block diagram of a computer-implemented system 4010, hereinafter called “the system” for managing a treatment plan. Managing the treatment plan may include using an artificial intelligence engine to recommend treatment plans and/or provide excluded treatment plans that should not be recommended to a patient.

[0553] The system 4010 also includes a server 4030 configured to store and to provide data related to managing the treatment plan. The server 4030 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers. The server 4030 also includes a first communication interface 4032 configured to communicate with the clinician interface 4020 via a first network 4034.1n some embodiments, the first network 4034 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. The server 4030 includes a first processor 4036 and a first machine-readable storage memory 4038, which may be called a “memory” for short, holding first instructions 4040 for performing the various actions of the server 4030 for execution by the first processor 4036. The server 4030 is configured to store data regarding the treatment plan. For example, the memory 4038 includes a system data store 4042 configured to hold system data, such as data pertaining to treatment plans for treating one or more patients.

[0554] The system data store 4042 may be configured to hold data relating to billing procedures, including rules and constraints pertaining to billing codes, order, timing, insurance regimes, laws, regulations, or some combination thereof. The system data store 4042 may be configured to store various billing sequences generated based on billing procedures and various parameters (e.g., monetary value amount generated, patient outcome, plan of reimbursement, fees, a payment plan for patients to pay of an amount of money owed, an amount of revenue to be paid to an insurance provider, etc.). The system data store 4042 may be configured to store optimal treatment plans generated based on various treatment plans for users having similar medical conditions, monetary value amounts generated by the treatment plans, and the constraints. Any of the data stored in the system data store 4042 may be accessed by an artificial intelligence engine 4011 when performing any of the techniques described herein.

[0555] The server 4030 is also configured to store data regarding performance by a patient in following a treatment plan. For example, the memory 4038 includes a patient data store 4044 configured to hold patient data, such as data pertaining to the one or more patients, including data representing each patient’ s performance within the treatment plan.

[0556] In addition, the characteristics (e.g., personal, performance, measurement, etc.) of the people, the treatment plans followed by the people, the level of compliance with the treatment plans, and the results of the treatment plans may use correlations and other statistical or probabilistic measures to enable the partitioning of or to partition the treatment plans into different patient cohort-equivalent databases in the patient data store 4044. For example, the data for a first cohort of first patients having a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment plan followed by the first patient, and a first result of the treatment plan may be stored in a first patient database. The data for a second cohort of second patients having a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, and a second result of the treatment plan may be stored in a second patient database. Any single characteristic or any combination of characteristics may be used to separate the cohorts of patients. In some embodiments, the different cohorts of patients may be stored in different partitions or volumes of the same database. There is no specific limit to the number of different cohorts of patients allowed, other than as limited by mathematical combinatoric and/or partition theory.

[0557] This characteristic data, treatment plan data, and results data may be obtained from numerous treatment apparatuses and/or computing devices over time and stored in the database 4044. The characteristic data, treatment plan data, and results data may be correlated in the patient-cohort databases in the patient data store 4044. The characteristics of the people may include personal information, performance information, and/or measurement information.

[0558] In addition to the historical information about other people stored in the patient cohort-equivalent databases, real-time or near-real-time information based on the current patient’s characteristics about a current patient being treated may be stored in an appropriate patient cohort-equivalent database. The characteristics of the patient may be determined to match or be similar to the characteristics of another person in a particular cohort (e.g., cohort A) and the patient may be assigned to that cohort.

[0559] In some embodiments, the server 4030 may execute the artificial intelligence (AI) engine 4011 that uses one or more machine learning models 4013 to perform at least one of the embodiments disclosed herein. The server 4030 may include a training engine 4009 capable of generating the one or more machine learning models 4013. The machine learning models 4013 may be trained to assign people to certain cohorts based on their characteristics, select treatment plans using real-time and historical data correlations involving patient cohort-equivalents, and control a treatment apparatus 4070, among other things. The machine learning models 4013 may be trained to generate, based on billing procedures, billing sequences and/or treatment plans tailored for various parameters (e.g., a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof). The machine learning models 4013 may be trained to generate, based on constraints, optimal treatment plans tailored for various parameters (e.g., monetary value amount generated, patient outcome, risk, etc.). The one or more machine learning models 4013 may be generated by the training engine 4009 and may be implemented in computer instructions executable by one or more processing devices of the training engine 4009 and/or the servers 4030. To generate the one or more machine learning models 4013, the training engine 4009 may train the one or more machine learning models 4013. The one or more machine learning models 4013 may be used by the artificial intelligence engine 4011.

[0560] The training engine 4009 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, an Internet of Things (IoT) device, any other desired computing device, or any combination of the above. The training engine 4009 may be cloud-based or a real-time software platform, and it may include privacy software or protocols, and/or security software or protocols.

[0561] To train the one or more machine learning models 4013, the training engine 4009 may use a training data set of a corpus of the information (e.g., characteristics, medical diagnosis codes, etc.) pertaining to medical conditions of the people who used the treatment apparatus 4070 to perform treatment plans, the details (e.g., treatment protocol including exercises, amount of time to perform the exercises, instructions for the patient to follow, how often to perform the exercises, a schedule of exercises, parameters/configurations/settings of the treatment apparatus 4070 throughout each step of the treatment plan, etc.) of the treatment plans performed by the people using the treatment apparatus 4070, the results of the treatment plans performed by the people, a set of monetaiy value amounts associated with the treatment plans, a set of constraints (e.g., rules pertaining to billing codes associated with the set of treatment plans, laws, regulations, etc.), a set of billing procedures (e.g., rules pertaining to billing codes, order, timing and constraints) associated with treatment plan instructions, a set of parameters (e.g., a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof, a treatment plan, a monetaiy value amount generated, a risk, etc.), insurance regimens, etc. [0562] The one or more machine learning models 4013 may be trained to match patterns of characteristics of a patient with characteristics of other people in assigned to a particular cohort. The term “match” may refer to an exact match, a correlative match, a substantial match, etc. The one or more machine learning models 4013 may be trained to receive the characteristics of a patient as input, map the characteristics to characteristics of people assigned to a cohort, and select a treatment plan from that cohort. The one or more machine learning models 4013 may also be trained to control, based on the treatment plan, the machine learning apparatus 4070. [0563] The one or more machine learning models 4013 may be trained to match patterns of a first set of parameters (e.g., treatment plans for patients having a medical condition, a set of monetaiy value amounts associated with the treatment plans, patient outcome, and/or a set of constraints) with a second set of parameters associated with an optimal treatment plan. The one or more machine learning models 4013 may be trained to receive the first set of parameters as input, map the characteristics to the second set of parameters associated with the optimal treatment plan, and select the optimal treatment plan a treatment plan. The one or more machine learning models 4013 may also be trained to control, based on the treatment plan, the machine learning apparatus 4070.

[0564] The one or more machine learning models 4013 may be trained to match patterns of a first set of parameters (e.g., information pertaining to a medical condition, treatment plans for patients having a medical condition, a set of monetaiy value amounts associated with the treatment plans, patient outcomes, instructions for the patient to follow in a treatment plan, a set of billing procedures associated with the instructions, and/or a set of constraints) with a second set of parameters associated with a billing sequence and/or optimal treatment plan. The one or more machine learning models 4013 may be trained to receive the first set of parameters as input, map or otherwise associate or algorithmically associate the first set of parameters to the second set of parameters associated with the billing sequence and/or optimal treatment plan, and select the billing sequence and/or optimal treatment plan for the patient. In some embodiments, one or more optimal treatment plans may be selected to be provided to a computing device of the medical professional and/or the patient. The one or more machine learning models 4013 may also be trained to control, based on the treatment plan, the machine learning apparatus 4070.

[0565] Different machine learning models 4013 may be trained to recommend different treatment plans tailored for different parameters. For example, one machine learning model may be trained to recommend treatment plans for a maximum monetaiy value amount generated, while another machine learning model may be trained to recommend treatment plans based on patient outcome, or based on any combination of monetaiy value amount and patient outcome, or based on those and/or additional goals. Also, different machine learning models 4013 may be trained to recommend different billing sequences tailored for different parameters. For example, one machine learning model may be trained to recommend billing sequences for a maximum fee to be paid to a medical professional, while another machine learning model may be trained to recommend billing sequences based on a plan of reimbursement.

[0566] Using training data that includes training inputs and corresponding target outputs, the one or more machine learning models 4013 may refer to model artifacts created by the training engine 9. The training engine 4009 may find patterns in the training data wherein such patterns map the training input to the target output, and generate the machine learning models 4013 that capture these patterns. In some embodiments, the artificial intelligence engine 4011, the database 4033, and/or the training engine 4009 may reside on another component (e.g., assistant interface 4094, clinician interface 4020, etc.) depicted in FIG. 27.

[0567] The one or more machine learning models 4013 may comprise, e.g., a single level of linear or non linear operations (e.g., a support vector machine [SVM]) or the machine learning models 4013 may be a deep network, i.e., a machine learning model comprising multiple levels of non-linear operations. Examples of deep networks are neural networks including generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (e.g., each neuron may transmit its output signal to the input of the remaining neurons, as well as to itself). For example, the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

[0568] The system 4010 also includes a patient interface 4050 configured to communicate information to a patient and to receive feedback from the patient. Specifically, the patient interface includes an input device 4052 and an output device 4054, which may be collectively called a patient user interface 4052, 4054. The input device 4052 may include one or more devices, such as a keyboard, a mouse, a touch screen input, a gesture sensor, and/or a microphone and processor configured for voice recognition. The output device 4054 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, smartphone, or a smart watch. The output device 4054 may include other hardware and/or software components such as a projector, virtual reality capability, augmented reality capability, etc. The output device 4054 may incorporate various different visual, audio, or other presentation technologies. For example, the output device 4054 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, and/or melodies, which may signal different conditions and/or directions. The output device 4054 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the patient. The output device 4054 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

[0569] In some embodiments, the output device 4054 may present a user interface that may present a recommended treatment plan, billing sequence, or the like to the patient. The user interface may include one or more graphical elements that enable the user to select which treatment plan to perform. Responsive to receiving a selection of a graphical element (e.g., “Start” button) associated with a treatment plan via the input device 4054, the patient interface 4050 may communicate a control signal to the controller 4072 of the treatment apparatus, wherein the control signal causes the treatment apparatus 4070 to begin execution of the selected treatment plan. As described below, the control signal may control, based on the selected treatment plan, the treatment apparatus 4070 by causing actuation of the actuator 4078 (e.g., cause a motor to drive rotation of pedals of the treatment apparatus at a certain speed), causing measurements to be obtained via the sensor 4076, or the like. The patient interface 4050 may communicate, via a local communication interface 4068, the control signal to the treatment apparatus 4070.

[0570] As shown in FIG. 27, the patient interface 4050 includes a second communication interface 4056, which may also be called a remote communication interface configured to communicate with the server 4030 and/or the clinician interface 4020 via a second network 4058. In some embodiments, the second network 4058 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 58 may include the Internet, and communications between the patient interface 4050 and the server 4030 and/or the clinician interface 4020 may be secured via encryption, such as, for example, by using a virtual private network (VPN). In some embodiments, the second network 4058 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. In some embodiments, the second network 4058 may be the same as and/or operationally coupled to the first network 4034.

[0571] The patient interface 4050 includes a second processor 4060 and a second machine -readable storage memory 4062 holding second instructions 4064 for execution by the second processor 4060 for performing various actions of patient interface 4050. The second machine-readable storage memory 4062 also includes a local data store 4066 configured to hold data, such as data pertaining to a treatment plan and/or patient data, such as data representing a patient’s performance within a treatment plan. The patient interface 4050 also includes a local communication interface 4068 configured to communicate with various devices for use by the patient in the vicinity of the patient interface 4050. The local communication interface 4068 may include wired and/or wireless communications. In some embodiments, the local communication interface 4068 may include a local wireless network such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc.

[0572] The system 4010 also includes a treatment apparatus 4070 configured to be manipulated by the patient and/or to manipulate a body part of the patient for performing activities according to the treatment plan. In some embodiments, the treatment apparatus 4070 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a body part of the patient, such as a joint or a bone or a muscle group. The treatment apparatus 4070 may be any suitable medical, rehabilitative, therapeutic, etc. apparatus configured to be controlled distally via another computing device to treat a patient and/or exercise the patient. The treatment apparatus 4070 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, or the like. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, a bone, or a muscle group, such as one or more vertebrae, a tendon, or a ligament. As shown in FIG. 27, the treatment apparatus 4070 includes a controller 4072, which may include one or more processors, computer memory, and/or other components. The treatment apparatus 4070 also includes a fourth communication interface 4074 configured to communicate with the patient interface 4050 via the local communication interface 4068. The treatment apparatus 4070 also includes one or more internal sensors 4076 and an actuator 4078, such as a motor. The actuator 4078 may be used, for example, for moving the patient’s body part and/or for resisting forces by the patient. [0573] The internal sensors 4076 may measure one or more operating characteristics of the treatment apparatus 4070 such as, for example, a force a position, a speed, and /or a velocity. In some embodiments, the internal sensors 4076 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a body part of the patient. For example, an internal sensor 4076 in the form of a position sensor may measure a distance that the patient is able to move a part of the treatment apparatus 4070, where such distance may correspond to a range of motion that the patient’s body part is able to achieve. In some embodiments, the internal sensors 4076 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 4076 in the form of a force sensor may measure a force or weight the patient is able to apply, using a particular body part, to the treatment apparatus 4070.

[0574] The system 4010 shown in FIG. 27 also includes an ambulation sensor 4082, which communicates with the server 4030 via the local communication interface 4068 of the patient interface 4050. The ambulation sensor 4082 may track and store a number of steps taken by the patient. In some embodiments, the ambulation sensor 4082 may take the form of a wristband, wristwatch, or smart watch. In some embodiments, the ambulation sensor 4082 may be integrated within a phone, such as a smartphone.

[0575] The system 4010 shown in FIG. 27 also includes a goniometer 4084, which communicates with the server 4030 via the local communication interface 4068 of the patient interface 4050. The goniometer 4084 measures an angle of the patient’s body part. For example, the goniometer 4084 may measure the angle of flex of a patient’s knee or elbow or shoulder.

[0576] The system 4010 shown in FIG. 27 also includes a pressure sensor 4086, which communicates with the server 4030 via the local communication interface 4068 of the patient interface 4050. The pressure sensor 4086 measures an amount of pressure or weight applied by a body part of the patient. For example, pressure sensor 4086 may measure an amount of force applied by a patient’s foot when pedaling a stationary bike. [0577] The system 4010 shown in FIG. 27 also includes a supervisory interface 4090 which may be similar or identical to the clinician interface 4020. In some embodiments, the supervisory interface 90 may have enhanced functionality beyond what is provided on the clinician interface 4020. The supervisory interface 4090 may be configured for use by a person having responsibility for the treatment plan, such as an orthopedic surgeon. [0578] The system 4010 shown in FIG. 27 also includes a reporting interface 4092 which may be similar or identical to the clinician interface 4020. In some embodiments, the reporting interface 4092 may have less functionality from what is provided on the clinician interface 4020. For example, the reporting interface 4092 may not have the ability to modify a treatment plan. Such a reporting interface 4092 may be used, for example, by a biller to determine the use of the system 4010 for billing purposes. In another example, the reporting interface 4092 may not have the ability to display patient identifiable information, presenting only pseudonymized data and/or anonymized data for certain data fields concerning a data subject and/or for certain data fields concerning a quasi-identifier of the data subject. Such a reporting interface 4092 may be used, for example, by a researcher to determine various effects of a treatment plan on different patients.

[0579] The system 4010 includes an assistant interface 4094 for an assistant, such as a doctor, a nurse, a physical therapist, or a technician, to remotely communicate with the patient interface 4050 and/or the treatment apparatus 4070. Such remote communications may enable the assistant to provide assistance or guidance to a patient using the system 4010. More specifically, the assistant interface 4094 is configured to communicate a telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b with the patient interface 4050 via a network comiection such as, for example, via the first network 4034 and/or the second network 4058. The telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b comprises one of an audio signal 4096, an audiovisual signal 4097, an interface control signal 4098a for controlling a function of the patient interface 4050, an interface monitor signal 98b for monitoring a status of the patient interface 4050, an apparatus control signal 4099a for changing an operating parameter of the treatment apparatus 4070, and/or an apparatus monitor signal 4099b for monitoring a status of the treatment apparatus 4070. In some embodiments, each of the control signals 4098a, 4099a may be unidirectional, conveying commands from the assistant interface 4094 to the patient interface 4050. In some embodiments, in response to successfully receiving a control signal 4098a, 4099a and/or to communicate successful and/or unsuccessful implementation of the requested control action, an acknowledgement message may be sent from the patient interface 4050 to the assistant interface 4094. In some embodiments, each of the monitor signals 4098b, 4099b may be unidirectional, status-information commands from the patient interface 4050 to the assistant interface 4094. In some embodiments, an acknowledgement message may be sent from the assistant interface 4094 to the patient interface 4050 in response to successfully receiving one of the monitor signals 4098b, 4099b.

[0580] In some embodiments, the patient interface 4050 may be configured as a pass-through for the apparatus control signals 4099a and the apparatus monitor signals 4099b between the treatment apparatus 4070 and one or more other devices, such as the assistant interface 4094 and/or the server 4030. For example, the patient interface 4050 may be configured to transmit an apparatus control signal 99a in response to an apparatus control signal 4099a within the telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b from the assistant interface 4094.

[0581] In some embodiments, the assistant interface 4094 may be presented on a shared physical device as the clinician interface 4020. For example, the clinician interface 4020 may include one or more screens that implement the assistant interface 4094. Alternatively or additionally, the clinician interface 4020 may include additional hardware components, such as a video camera, a speaker, and/or a microphone, to implement aspects of the assistant interface 4094.

[0582] In some embodiments, one or more portions of the telemedicine signal 4096, 4097, 4098a, 4098b, 4099a, 4099b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation) for presentation by the output device 4054 of the patient interface 4050. For example, a tutorial video may be streamed from the server 4030 and presented upon the patient interface 4050. Content from the prerecorded source may be requested by the patient via the patient interface 4050. Alternatively, via a control on the assistant interface 4094, the assistant may cause content from the prerecorded source to be played on the patient interface 4050.

[0583] The assistant interface 4094 includes an assistant input device 4022 and an assistant display 4024, which may be collectively called an assistant user interface 4022, 4024. The assistant input device 4022 may include one or more of a telephone, a keyboard, a mouse, a trackpad, or a touch screen, for example. Alternatively or additionally, the assistant input device 4022 may include one or more microphones. In some embodiments, the one or more microphones may take the form of a telephone handset, headset, or wide-area microphone or microphones configured for the assistant to speak to a patient via the patient interface 4050. In some embodiments, assistant input device 4022 may be configured to provide voice-based functionalities, with hardware and/or software configured to interpret spoken instructions by the assistant by using the one or more microphones. The assistant input device 4022 may include functionality provided by or similar to existing voice- based assistants such as Siii by Apple, Alexaby Amazon, Google Assistant, or Bixby by Samsung. The assistant input device 4022 may include other hardware and/or software components. The assistant input device 4022 may include one or more general purpose devices and/or special-purpose devices.

[0584] The assistant display 4024 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, a smartphone, or a smart watch. The assistant display 4024 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities, etc. The assistant display 4024 may incorporate various different visual, audio, or other presentation technologies. For example, the assistant display 4024 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, melodies, and/or compositions, which may signal different conditions and/or directions. The assistant display 4024 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the assistant. The assistant display 4024 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

[0585] In some embodiments, the system 4010 may provide computer translation of language from the assistant interface 4094 to the patient interface 4050 and/or vice-versa. The computer translation of language may include computer translation of spoken language and/or computer translation of text. Additionally or alternatively, the system 4010 may provide voice recognition and/or spoken pronunciation of text. For example, the system 4010 may convert spoken words to printed text and/or the system 4010 may audibly speak language from printed text. The system 4010 may be configured to recognize spoken words by any or all of the patient, the clinician, and/or the assistant. In some embodiments, the system 4010 may be configured to recognize and react to spoken requests or commands by the patient. For example, the system 4010 may automatically initiate a telemedicine session in response to a verbal command by the patient (which may be given in any one of several different languages).

[0586] In some embodiments, the server 4030 may generate aspects of the assistant display 4024 for presentation by the assistant interface 4094. For example, the server 4030 may include a web server configured to generate the display screens for presentation upon the assistant display 4024. For example, the artificial intelligence engine 4011 may generate treatment plans, billing sequences, and/or excluded treatment plans for patients and generate the display screens including those treatment plans, billing sequences, and/or excluded treatment plans for presentation on the assistant display 4024 of the assistant interface 4094. In some embodiments, the assistant display 4024 may be configured to present a virtualized desktop hosted by the server 4030. In some embodiments, the server 4030 may be configured to communicate with the assistant interface 4094 via the first network 4034. In some embodiments, the first network 4034 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the first network 4034 may include the Internet, and communications between the server 4030 and the assistant interface 4094 may be seemed via privacy enhancing technologies, such as, for example, by using encryption over a virtual private network (VPN). Alternatively or additionally, the server 4030 may be configured to communicate with the assistant interface 4094 via one or more networks independent of the first network 4034 and/or other communication means, such as a direct wired or wireless communication channel. In some embodiments, the patient interface 4050 and the treatment apparatus 4070 may each operate from a patient location geographically separate from a location of the assistant interface 4094. For example, the patient interface 4050 and the treatment apparatus 4070 may be used as part of an in-home rehabilitation system, which may be aided remotely by using the assistant interface 4094 at a centralized location, such as a clinic or a call center.

[0587] In some embodiments, the assistant interface 4094 may be one of several different terminals (e.g., computing devices) that may be grouped together, for example, in one or more call centers or at one or more clinicians’ offices. In some embodiments, a plurality of assistant interfaces 4094 may be distributed geographically. In some embodiments, a person may work as an assistant remotely from any conventional office infrastructure. Such remote work may be performed, for example, where the assistant interface 4094 takes the form of a computer and/or telephone. This remote work functionality may allow for work-from-home arrangements that may include part time and/or flexible work hours for an assistant.

[0588] FIGS. 28-29 show an embodiment of a treatment apparatus 4070. More specifically, FIG. 28 shows a treatment apparatus 4070 in the form of a stationary cycling machine 4100, which may be called a stationary bike, for short. The stationary cycling machine 4100 includes a set of pedals 4102 each attached to a pedal arm 4104 for rotation about an axle 4106. In some embodiments, and as shown in FIG. 28, the pedals 4102 are movable on the pedal arms 4104 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 4106 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 4106. A pressure sensor 4086 is attached to or embedded within one of the pedals 4102 for measuring an amount of force applied by the patient on the pedal 4102. The pressure sensor 4086 may communicate wirelessly to the treatment apparatus 4070 and/or to the patient interface 4050. [0589] FIG. 30 shows a person (a patient) using the treatment apparatus of FIG. 28, and showing sensors and various data parameters connected to a patient interface 4050. The example patient interface 4050 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 4050 may be embedded within or attached to the treatment apparatus 4070. FIG. 30 shows the patient wearing the ambulation sensor 4082 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 4082 has recorded and transmitted that step count to the patient interface 4050. FIG. 30 also shows the patient wearing the goniometer 4084 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 4084 is measuring and transmitting that knee angle to the patient interface 4050. FIG. 30 also shows a right side of one of the pedals 4102 with a pressure sensor 4086 showing “FORCE 12.5 lbs.,” indicating that the right pedal pressure sensor 4086 is measuring and transmitting that force measurement to the patient interface 4050. FIG. 30 also shows a left side of one of the pedals 4102 with a pressure sensor 4086 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 4086 is measuring and transmitting that force measurement to the patient interface 4050. FIG. 30 also shows other patient data, such as an indicator of “SESSION TIME 0:04: 13”, indicating that the patient has been using the treatment apparatus 4070 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 4050 based on information received from the treatment apparatus 4070. FIG. 30 also shows an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patent in response to a solicitation, such as a question, presented upon the patient interface 4050. [0590] FIG. 31 is an example embodiment of an overview display 4120 of the assistant interface 4094. Specifically, the overview display 4120 presents several different controls and interfaces for the assistant to remotely assist a patient with using the patient interface 4050 and/or the treatment apparatus 4070. This remote assistance functionality may also be called telemedicine or telehealth.

[0591] Specifically, the overview display 4120 includes a patient profile display 4130 presenting biographical information regarding a patient using the treatment apparatus 4070. The patient profile display 4130 may take the form of a portion or region of the overview display 4120, as shown in FIG. 31, although the patient profile display 4130 may take other forms, such as a separate screen or a popup window. In some embodiments, the patient profile display 4130 may include a limited subset of the patient’s biographical information. More specifically, the data presented upon the patient profile display 4130 may depend upon the assistant’s need for that information. For example, a medical professional that is assisting the patient with a medical issue may be provided with medical history information regarding the patient, whereas a technician troubleshooting an issue with the treatment apparatus 4070 may be provided with a much more limited set of information regarding the patient. The technician, for example, may be given only the patient’s name. The patient profile display 4130 may include pseudonymized data and/or anonymized data or use any privacy enhancing technology to prevent confidential patient data from being communicated in a way that could violate patient confidentiality requirements. Such privacy enhancing technologies may enable compliance with laws, regulations, or other rules of governance such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA), or the General Data Protection Regulation (GDPR), wherein the patient may be deemed a “data subject”.

[0592] In some embodiments, the patient profile display 4130 may present information regarding the treatment plan for the patient to follow in using the treatment apparatus 4070. Such treatment plan information may be limited to an assistant who is a medical professional, such as a doctor or physical therapist. For example, a medical professional assisting the patient with an issue regarding the treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting an issue with the treatment apparatus 4070 may not be provided with any information regarding the patient’s treatment plan.

[0593] In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented in the patient profile display 4130 to the assistant. The one or more recommended treatment plans and/or excluded treatment plans may be generated by the artificial intelligence engine 4011 of the server 4030 and received from the server 4030 in real-time during, inter alia, a telemedicine or telehealth session. An example of presenting the one or more recommended treatment plans and/or ruled-out treatment plans is described below with reference to FIG. 33.

[0594] In some embodiments, one or more treatment plans and/or billing sequences associated with the treatment plans may be presented in the patient profile display 4130 to the assistant. The one or more treatment plans and/or billing sequences associated with the treatment plans may be generated by the artificial intelligence engine 4011 of the server 4030 and received from the server 4030 in real-time during, inter alia, a telehealth session. An example of presenting the one or more treatment plans and/or billing sequences associated with the treatment plans is described below with reference to FIG. 35.

[0595] In some embodiments, one or more treatment plans and associated monetary value amounts generated, patient outcomes, and risks associated with the treatment plans may be presented in the patient profile display 4130 to the assistant. The one or more treatment plans and associated monetary value amounts generated, patient outcomes, and risks associated with the treatment plans may be generated by the artificial intelligence engine 4011 of the server 4030 and received from the server 4030 in real-time during, inter alia, a telehealth session. An example of presenting the one or more treatment plans and associated monetary value amounts generated, patient outcomes, and risks associated with the treatment plans is described below with reference to FIG. 38. [0596] The example overview display 4120 shown in FIG. 31 also includes a patient status display 4134 presenting status information regarding a patient using the treatment apparatus. The patient status display 4134 may take the form of a portion or region of the overview display 4120, as shown in FIG. 31, although the patient status display 4134 may take other forms, such as a separate screen or a popup window. The patient status display 4134 includes sensor data 4136 from one ormore of the external sensors 4082, 4084, 4086, and/orfrom one or more internal sensors 4076 of the treatment apparatus 4070. In some embodiments, the patient status display 4134 may present other data 4138 regarding the patient, such as last reported pain level, or progress within a treatment plan.

[0597] User access controls may be used to limit access, including what data is available to be viewed and/or modified, on any or all of the user interfaces 4020, 4050, 4090, 4092, 4094 of the system 4010. In some embodiments, user access controls may be employed to control what information is available to any given person using the system 4010. For example, data presented on the assistant interface 4094 may be controlled by user access controls, with permissions set depending on the assistant/user’s need for and/or qualifications to view that information.

[0598] The example overview display 4120 shown in FIG. 31 also includes a help data display 4140 presenting information for the assistant to use in assisting the patient. The help data display 4140 may take the form of a portion or region of the overview display 4120, as shown in FIG. 31. The help data display 4140 may take other forms, such as a separate screen or a popup window. The help data display 4140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 4050 and/or the treatment apparatus 4070. The help data display 4140 may also include research data or best practices. In some embodiments, the help data display 4140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 4140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient’s problem. In some embodiments, the assistant interface 4094 may present two or more help data displays 4140, which may be the same or different, for simultaneous presentation of help data for use by the assistant for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient’s problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information.

[0599] The example overview display 4120 shown in FIG. 31 also includes a patient interface control 4150 presenting information regarding the patient interface 4050, and/or to modify one or more settings of the patient interface 4050. The patient interface control 4150 may take the form of a portion or region of the overview display 4120, as shown in FIG. 31. The patient interface control 4150 may take other forms, such as a separate screen or a popup window. The patient interface control 4150 may present information communicated to the assistant interface 4094 via one or more of the interface monitor signals 4098b. As shown in FIG. 31, the patient interface control 4150 includes a display feed 4152 of the display presented by the patient interface 4050. In some embodiments, the display feed 4152 may include a live copy of the display screen currently being presented to the patient by the patient interface 4050. In other words, the display feed 4152 may present an image of what is presented on a display screen of the patient interface 4050. In some embodiments, the display feed 4152 may include abbreviated information regarding the display screen currently being presented by the patient interface 4050, such as a screen name or a screen number. The patient interface control 4150 may include a patient interface setting control 4154 for the assistant to adjust or to control one or more settings or aspects of the patient interface 4050. In some embodiments, the patient interface setting control 4154 may cause the assistant interface 4094 to generate and/or to transmit an interface control signal 4098 for controlling a function or a setting of the patient interface 4050.

[0600] In some embodiments, the patient interface setting control 4154 may include collaborative browsing or co-browsing capability for the assistant to remotely view and/or control the patient interface 4050. For example, the patient interface setting control 4154 may enable the assistant to remotely enter text to one or more text entry fields on the patient interface 4050 and/or to remotely control a cursor on the patient interface 4050 using a mouse or touchscreen of the assistant interface 4094.

[0601] In some embodiments, using the patient interface 4050, the patient interface setting control 4154 may allow the assistant to change a setting that cannot be changed by the patient. For example, the patient interface 4050 may be precluded from accessing a language setting to prevent a patient from inadvertently switching, on the patient interface 4050, the language used for the displays, whereas the patient interface setting control 4154 may enable the assistant to change the language setting of the patient interface 4050. In another example, the patient interface 4050 may not be able to change a font size setting to a smaller size in order to prevent a patient from inadvertently switching the font size used for the displays on the patient interface 4050 such that the display would become illegible to the patient, whereas the patient interface setting control 4154 may provide for the assistant to change the font size setting of the patient interface 4050.

[0602] The example overview display 4120 shown in FIG. 31 also includes an interface communications display 4156 showing the status of communications between the patient interface 4050 and one or more other devices 4070, 4082, 4084, such as the treatment apparatus 4070, the ambulation sensor 4082, and/or the goniometer 4084. The interface communications display 4156 may take the form of a portion or region of the overview display 4120, as shown in FIG. 31. The interface communications display 4156 may take other forms, such as a separate screen or a popup window. The interface communications display 4156 may include controls for the assistant to remotely modify communications with one or more of the other devices 4070, 4082, 4084. For example, the assistant may remotely command the patient interface 4050 to reset communications with one of the other devices 4070, 4082, 4084, or to establish communications with a new one of the other devices 4070, 4082, 4084. This functionality may be used, for example, where the patient has a problem with one of the other devices 4070, 4082, 4084, or where the patient receives a new or a replacement one of the other devices 4070, 4082, 4084.

[0603] The example overview display 4120 shown in FIG. 31 also includes an apparatus control 4160 for the assistant to view and/or to control information regarding the treatment apparatus 4070. The apparatus control 4160 may take the form of a portion or region of the overview display 4120, as shown in FIG. 31. The apparatus control 4160 may take other forms, such as a separate screen or a popup window. The apparatus control 4160 may include an apparatus status display 4162 with information regarding the current status of the apparatus. The apparatus status display 4162 may present information communicated to the assistant interface 4094 via one or more of the apparatus monitor signals 4099b. The apparatus status display 4162 may indicate whether the treatment apparatus 4070 is currently communicating with the patient interface 4050. The apparatus status display 4162 may present other current and/or historical information regarding the status of the treatment apparatus 4070.

[0604] The apparatus control 4160 may include an apparatus setting control 4164 for the assistant to adjust or control one or more aspects of the treatment apparatus 4070. The apparatus setting control 4164 may cause the assistant interface 4094 to generate and/or to transmit an apparatus control signal 4099 for changing an operating parameter of the treatment apparatus 4070, (e.g., a pedal radius setting, a resistance setting, a target RPM, etc.). The apparatus setting control 4164 may include a mode button 4166 and a position control 4168, which may be used in conjunction for the assistant to place an actuator 4078 of the treatment apparatus 4070 in a manual mode, after which a setting, such as a position or a speed of the actuator 4078, canbe changed using the position control 4168. The mode button 4166 may provide fora setting, such as a position, to be toggled between automatic and manual modes. In some embodiments, one or more settings may be adjustable at any time, and without having an associated auto/manual mode. In some embodiments, the assistant may change an operating parameter of the treatment apparatus 4070, such as a pedal radius setting, while the patient is actively using the treatment apparatus 4070. Such “on the fly” adjustment may or may not be available to the patient using the patient interface 4050. In some embodiments, the apparatus setting control 4164 may allow the assistant to change a setting that cannot be changed by the patient using the patient interface 4050. For example, the patient interface 4050 may be precluded from changing a preconfigured setting, such as a height or a tilt setting of the treatment apparatus 4070, whereas the apparatus setting control 4164 may provide for the assistant to change the height or tilt setting of the treatment apparatus 4070.

[0605] The example overview display 4120 shown in FIG. 31 also includes a patient communications control 4170 for controlling an audio or an audiovisual communications session with the patient interface 4050. The communications session with the patient interface 4050 may comprise a live feed from the assistant interface 4094 for presentation by the output device of the patient interface 4050. The live feed may take the form of an audio feed and/or a video feed. In some embodiments, the patient interface 4050 may be configured to provide two-way audio or audiovisual communications with a person using the assistant interface 4094. Specifically, the communications session with the patient interface 4050 may include bidirectional (two-way) video or audiovisual feeds, with each of the patient interface 4050 and the assistant interface 4094 presenting video of the other one. In some embodiments, the patient interface 4050 may present video from the assistant interface 4094, while the assistant interface 4094 presents only audio or the assistant interface 4094 presents no live audio or visual signal from the patient interface 4050. In some embodiments, the assistant interface 4094 may present video from the patient interface 4050, while the patient interface 4050 presents only audio or the patient interface 4050 presents no live audio or visual signal from the assistant interface 4094.

[0606] In some embodiments, the audio or an audiovisual communications session with the patient interface 4050 may take place, at least in part, while the patient is performing the rehabilitation regimen upon the body part. The patient communications control 4170 may take the form of a portion or region of the overview display 4120, as shown in FIG. 31. The patient communications control 4170 may take other forms, such as a separate screen or a popup window. The audio and/or audiovisual communications may be processed and/or directed by the assistant interface 4094 and/or by another device or devices, such as a telephone system, or a videoconferencing system used by the assistant while the assistant uses the assistant interface 4094. Alternatively or additionally, the audio and/or audiovisual communications may include communications with a third party. For example, the system 4010 may enable the assistant to initiate a 3-way conversation regarding use of a particular piece of hardware or software, with the patient and a subject matter expert, such as a medical professional or a specialist. The example patient communications control 4170 shown in FIG. 31 includes call controls 4172 for the assistant to use in managing various aspects of the audio or audiovisual communications with the patient. The call controls 4172 include a disconnect button 4174 for the assistant to end the audio or audiovisual communications session. The call controls 4172 also include a mute button 4176 to temporarily silence an audio or audiovisual signal from the assistant interface 4094. In some embodiments, the call controls 4172 may include other features, such as a hold button (not shown). The call controls 4172 also include one or more record/playback controls 4178, such as record, play, and pause buttons to control, with the patient interface 4050, recording and/or playback of audio and/or video from the teleconference session. The call controls 4172 also include a video feed display 4180 for presenting still and/or video images from the patient interface 4050, and a self-video display 4182 showing the current image of the assistant using the assistant interface. The self video display 4182 may be presented as a picture-in-picture format, within a section of the video feed display 4180, as shown in FIG. 31. Alternatively or additionally, the self-video display 4182 may be presented separately and/or independently from the video feed display 4180.

[0607] The example overview display 4120 shown in FIG. 31 also includes a third party communications control 4190 for use in conducting audio and/or audiovisual communications with a third party. The third party communications control 4190 may take the form of a portion or region of the overview display 4120, as shown in FIG. 31. The third party communications control 4190 may take other forms, such as a display on a separate screen or a popup window. The third party communications control 4190 may include one or more controls, such as a contact list and/or buttons or controls to contact a third party regarding use of a particular piece of hardware or software, e.g., a subject matter expert, such as a medical professional or a specialist. The third party communications control 4190 may include conference calling capability for the third party to simultaneously communicate with both the assistant via the assistant interface 4094, and with the patient via the patient interface 4050. For example, the system 4010 may provide for the assistant to initiate a 3-way conversation with the patient and the third party.

[0608] FIG. 32 shows an example block diagram of training a machine learning model 4013 to output, based on data 4600 pertaining to the patient, a treatment plan 4602 for the patient according to the present disclosure. Data pertaining to other patients may be received by the server 4030. The other patients may have used various treatment apparatuses to perform treatment plans. The data may include characteristics of the other patients, the details of the treatment plans performed by the other patients, and/or the results of performing the treatment plans (e.g., a percent of recovery of a portion of the patients’ bodies, an amount of recovery of a portion of the patients ’ bodies, an amount of increase or decrease in muscle strength of a portion of patients ’ bodies, an amount of increase or decrease in range of motion of a portion of patients’ bodies, etc.).

[0609] As depicted, the data has been assigned to different cohorts. Cohort A includes data for patients having similar first characteristics, first treatment plans, and first results. Cohort B includes data for patients having similar second characteristics, second treatment plans, and second results. For example, cohort A may include first characteristics of patients in their twenties without any medical conditions who underwent surgery for a broken limb; their treatment plans may include a certain treatment protocol (e.g., use the treatment apparatus 4070 for 30 minutes 5 times a week for 3 weeks, wherein values for the properties, configurations, and/or settings of the treatment apparatus 4070 are set to X (where X is a numerical value) for the first two weeks and to Y (where Y is a numerical value) for the last week).

[0610] Cohort A and cohort B may be included in a training dataset used to train the machine learning model 4013. The machine learning model 4013 may be trained to match a pattern between characteristics for each cohort and output the treatment plan that provides the result. Accordingly, when the data 4600 for a new patient is input into the trained machine learning model 4013, the trained machine learning model 4013 may match the characteristics included in the data 4600 with characteristics in either cohort A or cohort B and output the appropriate treatment plan 4602. In some embodiments, the machine learning model 4013 may be trained to output one or more excluded treatment plans that should not be performed by the new patient.

[0611] FIG. 33 shows an embodiment of an overview display 4120 of the assistant interface 4094 presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure. As depicted, the overview display 4120 just includes sections for the patient profile 4130 and the video feed display 4180, including the self-video display 4182. Any suitable configuration of controls and interfaces of the overview display 4120 described with reference to FIG. 31 may be presented in addition to or instead of the patient profile 4130, the video feed display 4180, and the self-video display 4182. [0612] The assistant (e.g., medical professional) using the assistant interface 4094 (e.g., computing device) during the telemedicine session may be presented in the self-video 4182 in a portion of the overview display 4120 (e.g., user interface presented on a display screen 4024 of the assistant interface 4094) that also presents a video from the patient in the video feed display 4180. Further, the video feed display 4180 may also include a graphical user interface (GUI) object 4700 (e.g., a button) that enables the medical professional to share, in real time or near real-time during the telemedicine session, the recommended treatment plans and/or the excluded treatment plans with the patient on the patient interface 4050. The medical professional may select the GUI object 4700 to share the recommended treatment plans and/or the excluded treatment plans. As depicted, another portion of the overview display 4120 includes the patient profile display 4130.

[0613] The patient profile display 4130 is presenting two example recommended treatment plans 4600 and one example excluded treatment plan 4602. As described herein, the treatment plans may be recommended in view of characteristics of the patient being treated. To generate the recommended treatment plans 4600 the patient should follow to achieve a desired result, a pattern between the characteristics of the patient being treated and a cohort of other people who have used the treatment apparatus 4070 to perform a treatment plan may be matched by one or more machine learning models 4013 of the artificial intelligence engine 4011. Each of the recommended treatment plans may be generated based on different desired results.

[0614] For example, as depicted, the patient profile display 4130 presents “The characteristics of the patient match characteristics of users in Cohort A. The following treatment plans are recommended for the patient based on his characteristics and desired results.” Then, the patient profile display 4130 presents recommended treatment plans from cohort A, and each treatment plan provides different results.

[0615] As depicted, treatment plan “A” indicates “Patient X should use treatment apparatus for 4030 minutes a day for 4 days to achieve an increased range of motion of Y%; Patient X has Type 2 Diabetes; and Patient X should be prescribed medication Z for pain management during the treatment plan (medication Z is approved for people having Type 2 Diabetes).” Accordingly, the treatment plan generated achieves increasing the range of motion of Y%. As may be appreciated, the treatment plan also includes a recommended medication (e.g., medication Z) to prescribe to the patient to manage pain in view of a known medical disease (e.g., Type 2 Diabetes) of the patient. That is, the recommended patient medication not only does not conflict with the medical condition of the patient but thereby improves the probability of a superior patient outcome. This specific example and all such examples elsewhere herein are not intended to limit in any way the generated treatment plan from recommending multiple medications, or from handling the acknowledgement, view, diagnosis and/or treatment of comorbid conditions or diseases.

[0616] Recommended treatment plan “B” may specify, based on a different desired result of the treatment plan, a different treatment plan including a different treatment protocol for a treatment apparatus, a different medication regimen, etc.

[0617] As depicted, the patient profile display 4130 may also present the excluded treatment plans 4602. These types of treatment plans are shown to the assistant using the assistant interface 4094 to alert the assistant not to recommend certain portions of a treatment plan to the patient. For example, the excluded treatment plan could specify the following: “Patient X should not use treatment apparatus for longer than 30 minutes a day due to a heart condition; Patient X has Type 2 Diabetes; and Patient X should not be prescribed medication M for pain management during the treatment plan (in this scenario, medication M can cause complications for people having Type 2 Diabetes) . Specifically, the excluded treatment plan points out a limitation of a treatment protocol where, due to a heart condition, Patient X should not exercise for more than 30 minutes a day. The ruled-out treatment plan also points out that Patient X should not be prescribed medication M because it conflicts with the medical condition Type 2 Diabetes.

[0618] The assistant may select the treatment plan for the patient on the overview display 4120. For example, the assistant may use an input peripheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) to select from the treatment plans 4600 for the patient. In some embodiments, during the telemedicine session, the assistant may discuss the pros and cons of the recommended treatment plans 4600 with the patient.

[0619] In any event, the assistant may select the treatment plan for the patient to follow to achieve the desired result. The selected treatment plan may be transmitted to the patient interface 4050 for presentation. The patient may view the selected treatment plan on the patient interface 4050. In some embodiments, the assistant and the patient may discuss during the telemedicine session the details (e.g., treatment protocol using treatment apparatus 4070, diet regimen, medication regimen, etc.) in real-time or in near real-time. In some embodiments, the server 4030 may control, based on the selected treatment plan and during the telemedicine session, the treatment apparatus 4070 as the user uses the treatment apparatus 4070.

[0620] FIG. 34 shows an embodiment of the overview display 4120 of the assistant interface 4094 presenting, in real-time during a telemedicine session, recommended treatment plans that have changed as a result of patient data changing according to the present disclosure. As may be appreciated, the treatment apparatus 4070 and/or any computing device (e.g., patient interface 4050) may transmit data while the patient uses the treatment apparatus 4070 to perform a treatment plan. The data may include updated characteristics of the patient. For example, the updated characteristics may include new performance information and/or measurement information. The performance information may include a speed of a portion of the treatment apparatus 4070, a range of motion achieved by the patient, a force exerted on a portion of the treatment apparatus 4070, a heartrate of the patient, a blood pressure of the patient, a respiratory rate of the patient, and so forth.

[0621] In one embodiment, the data received at the server 4030 may be input into the trained machine learning model 4013, which may determine that the characteristics indicate the patient is on track for the current treatment plan. Determining the patient is on track for the current treatment plan may cause the trained machine learning model 4013 to adjust a parameter of the treatment apparatus 4070. The adjustment may be based on a next step of the treatment plan to further improve the performance of the patient.

[0622] In one embodiment, the data received at the server 4030 may be input into the trained machine learning model 4013, which may determine that the characteristics indicate the patient is not on track (e.g., behind schedule, not able to maintain a speed, not able to achieve a certain range of motion, is in too much pain, etc.) for the current treatment plan or is ahead of schedule (e.g., exceeding a certain speed, exercising longer than specified with no pain, exerting more than a specified force, etc.) for the current treatment plan. The trained machine learning model 4013 may determine that the characteristics of the patient no longer match the characteristics of the patients in the cohort to which the patient is assigned. Accordingly, the trained machine learning model 4013 may reassign the patient to another cohort that includes qualifying characteristics the patient’s characteristics. As such, the trained machine learning model 4013 may select a new treatment plan from the new cohort and control, based on the new treatment plan, the treatment apparatus 4070.

[0623] In some embodiments, prior to controlling the treatment apparatus 4070, the server 4030 may provide the new treatment plan 4800 to the assistant interface 4094 for presentation in the patient profile 4130. As depicted, the patient profile 4130 indicates “The characteristics of the patient have changed and now match characteristics of users in Cohort B. The following treatment plan is recommended for the patient based on his characteristics and desired results.” Then, the patient profile 4130 presents the new treatment plan 4800 (“Patient X should use treatment apparatus for 10 minutes a day for 3 days to achieve an increased range of motion of L%” The assistant (medical professional) may select the new treatment plan 4800, and the server 4030 may receive the selection. The server 4030 may control the treatment apparatus 4070 based on the new treatment plan 4800. In some embodiments, the new treatment plan 4800 may be transmitted to the patient interface 4050 such that the patient may view the details of the new treatment plan 4800.

[0624] FIG. 35 shows an embodiment of the overview display 4120 of the assistant interface 4094 presenting, in real-time during a telemedicine session, treatment plans and billing sequences tailored for certain parameters according to the present disclosure. As depicted, the overview display 4120 just includes sections for the patient profile 4130 and the video feed display 4180, including the self-video display 4182. Any suitable configuration of controls and interfaces of the overview display 4120 described with reference to FIG. 31 may be presented in addition to or instead of the patient profile 4130, the video feed display 4180, and the self-video display 4182. In some embodiments, the same treatment plans and billing sequences may be presented in a display screen 4054 of the patient interface 4050. In some embodiments, the treatment plans and billing sequences may be presented simultaneously, in real-time or near real-time, during a telemedicine or telehealth session, on both the display screen 4054 of the patient interface 4050 and the display screen 4024 of the assistant interface 4094. [0625] The assistant (e.g., medical professional) using the assistant interface 4094 (e.g., computing device) during the telemedicine session may be presented in the self-video 4182 in a portion of the overview display 4120 (e.g., user interface presented on a display screen 4024 of the assistant interface 4094) that also presents a video from the patient in the video feed display 4180. Further, the video feed display 4180 may also include a graphical user interface (GUI) object 4700 (e.g., a button) that enables the medical professional to share, in real time or near real-time during the telemedicine session, the treatment plans and/or the billing sequences with the patient on the patient interface 4050. The medical professional may select the GUI object 4700 to share the treatment plans and/orthe billing sequences. As depicted, another portion of the overview display 4120 includes the patient profile display 4130.

[0626] The patient profile display 4130 is presenting two example treatment plans and two example billing sequences. Treatment plans 4900 and 4902 may be generated based on information (e.g., medical diagnosis code) pertaining to a condition of the patient. Treatment plan 4900 corresponds to billing sequence 4904, and treatment plan 4902 corresponds to billing sequence 4906. The generated billing sequences 4904 and 4906 and the treatment plans 4900 and 4902 comply with a set of billing procedures including rules pertaining to billing codes, order, timing, and constraints (e.g., laws, regulations, etc.). As described herein, each of the respective the billing sequences 4904 and4 906 may be generated based on a set of billing procedures associated with at least a portion of instructions included in each of the respective treatment plans 4900 and 4902. Further, each of the billing sequences 4904 and 4906 and/or treatment plans 4900 and 4902 may be tailored according to a certain parameter (e.g., a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof). In some embodiments, the monetary value amount “to be paid” may be inclusive to any means of settling an account with an insurance provider (e.g., payment of monetary, issuance of credit).

[0627] Each of the respective treatment plans 4900 and 4902 may include one or more procedures to be performed on the patient based on the information pertaining to the medical condition of the patient. Further, each of the respective billing sequences 4904 and 4906 may include an order for how the procedures are to be billed based on the billing procedures and one or more parameters.

[0628] For example, as depicted, the patient profile display 4130 presents “Patient has Condition Z”, where condition Z may be associated with information of the patient including a particular medical diagnosis code received from an EMR system. Based on the information, the treatment plans 4900 and 4906 each include procedures relevant to be performed for the Condition Z. The patient profile 4130 presents “Treatment Plan 1: 1. Procedure A; 2. Procedure B”. Each of the procedures may specify one or more instructions for performing the procedures, and each of the one or more instructions may be associated with a particular billing code or codes. Then, the patient profile display 4130 presents the billing sequence 4904 generated, based on the billing procedures and one or more parameters, for at least a portion of the one or more instructions included in the treatment plan 4900. The patient profile display 4130 presents “Billing Sequence 1 Tailored for [Parameter X] : 1. Bill for code 123 associated with Procedure A; 2. Bill for code 234 associated with Procedure B”. It should be noted that [Parameter X] may be any suitable parameter, such as a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof.

[0629] Further, the patient profile 4130 also presents the treatment plan 4902 and presents “Treatment Plan 2: 1. Procedure C; 2. Procedure A”. Each of the procedures may specify one or more instructions for performing the procedures, and each of the one or more instructions may be associated with a particular billing code. Then, the patient profile display 4130 presents the billing sequence 4906 generated, based on the billing procedures and one or more parameters, for at least a portion of the one or more instructions included in the treatment plan 4902. The patient profile display 4130 presents “Billing Sequence 2 Tailored for [Parameter Y] : 1. Bill for code 345 associated with Procedure C; 2. Bill for code 123 associated with Procedure A”. It should be noted that [Parameter Y] may be any suitable parameter, such as a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof. It should also be noted that in the depicted example [Parameter X] and [Parameter Y] are different parameters.

[0630] As should be appreciated, the billing sequence 4904 and 4906 includes a different order for billing the procedures included in the respective treatment plans 4900 and 4902, and each of the billing sequences 4904 and 4906 complies with the billing procedures. The billing sequence 4904 may have been tailored for [Parameter X] (e.g., a fee to be paid to a medical professional) and the billing sequence 4906 may have been tailored for [Parameter Y] (e.g., a plan of reimbursement).

[0631] The order of performing the procedures for the treatment plan 4902 specifies performing Procedure C first and then Procedure A. However, the billing sequence 4906 specifies billing for the code 123 associated with Procedure A first and then billing for the code 345 associated with Procedure C. Such a billing sequence 4906 may have been dictated by the billing procedures. For example, although Procedure A is performed second, a law, regulation, or the like may dictate that Procedure A be billed before any other procedure.

[0632] Further, as depicted, a graphical element (e.g., button for “SELECT”) may be presented in the patient profile display 4130. Although just one graphical element is presented, any suitable number of graphical elements for selecting a treatment plan and/or billing sequence may be presented in the patient profile display 4130. As depicted, a user (e.g., medical professional or patient) uses an input peripheral (e.g., mouse, keyboard, microphone, touchscreen) to select (as represented by circle 4950) the graphical element associated with the treatment plan 4900 and billing sequence4904. The medical professional may prefer to receive a certain fee and the billing sequence 4904 is optimized based on [Parameter X] (e.g., a fee to be paid to the medical professional, as previously discussed). Accordingly, the assistant interface 4094 may transmit a control signal to the treatment apparatus 4070 to control, based on the treatment plan 4900, operation of the treatment apparatus 4070. In some embodiments, the patient may select the treatment plan from the display screen 4054 and the patient interface 50 may transmit a control signal to the treatment apparatus 4070 to control, based on the selected treatment plan, operation of the treatment apparatus 4070.

[0633] FIG. 36 shows an example embodiment of a method 41000 for generating, based on a set of billing procedures, a billing sequence tailored for a particular parameter, where the billing sequence pertains to a treatment plan according to the present disclosure. The method 41000 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as is ran on a general-purpose computer system or a dedicated machine), or a combination of both. The method 41000 and/or each of its individual functions, routines, other methods, scripts, subroutines, or operations may be performed by one or more processors of a computing device (e.g., any component of FIGURE 27, such as server 4030 executing the artificial intelligence engine 4011). In certain implementations, the method 1000 may be performed by a single processing thread. Alternatively, the method 41000 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, other methods, scripts, subroutines, or operations of the methods. [0634] For simplicity of explanation, the method 41000 is depicted and described as a series of operations. However, operations in accordance with this disclosure can occur in various orders and/or concurrently, and/or with other operations not presented and described herein. For example, the operations depicted in the method 41000 may occur in combination with any other operation of any other method disclosed herein. Furthermore, not all illustrated operations may be required to implement the method 41000 in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the method 1000 could alternatively be represented as a series of interrelated states via a state diagram, a directed graph, a deterministic finite state automaton, a non-deterministic finite state automaton, a Markov diagram, or events.

[0635] At 41002, the processing device may receive information pertaining to a patient. The information may include a medical diagnosis code (DRG, ICD-9, ICD-10, etc.) associated with the patient. The information may also include characteristics of the patient, such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment apparatus, an amount of force exerted on a portion of the treatment apparatus, the body part used to exert the amount of force, the tendons, ligaments, muscles and other body parts associated with or connected to the body part, a range of motion achieved on the treatment apparatus, a movement speed of a portion of the treatment apparatus, an indication of a plurality of pain levels using the treatment apparatus, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, or some combination thereof. It may be desirable to process the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[0636] At 41004, the processing device may generate, based on the information, a treatment plan for the patient. The treatment plan may include a set of instructions for the patient to follow (e.g., for rehabilitation, prehabilitation, post-habilitation, etc.). In some embodiments, the treatment plan may be generated by comparing and matching the information of the patient with information of other patients. In some embodiments, the treatment plan may pertain to habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength training, endurance training, weight loss, weight gain, flexibility, pliability, or some combination thereof. In some embodiments, the set of instructions may include a set of exercises for the patient to perform, an order for the set of exercises, a frequency for performing the set of exercises, a diet program, a sleep regimen, a set of procedures to perform on the patient, an order for the set of procedures, a medication regimen, a set of sessions for the patient, or some combination thereof.

[0637] At 41006, the processing device may receive a set of billing procedures associated with the set of instructions. The set of billing procedures may include rules pertaining to billing codes, timing, order, insurance regimens, constraints, or some combination thereof. In some embodiments, the constraints may include constraints set forth in regulations, laws, or some combination thereof. The rules pertaining to the billing codes may specify exact billing codes for procedures. The billing codes may be standardized and mandated by certain regulatory agencies and/or systems. A certain billing code may be unique to a certain procedure.

[0638] The rules pertaining to the timing information may specify when certain procedures and/or associated billing codes may be billed. The timing information may also specify a length of time from when a procedure is performed until the procedure can be billed, a periodicity that certain procedures may be billed, a frequency that certain procedures may be billed, and so forth.

[0639] The rules pertaining to the order information may specify an order in which certain procedures and/or billing codes may be billed to the patient. For example, the rules may specify that a certain procedure cannot be billed until another procedure is billed.

[0640] The rules pertaining to the insurance regimens may specify what amount and/or percentage the insurance provider pays based on the insurance benefits of the patient, when the insurance provider distributes payments, and the like.

[0641] The rules pertaining to the constraints may include laws and regulations of medical billing. For example, the Health Insurance Portability and Accountability Act (HIPAA) includes numerous medical billing laws and regulations. In the European Union, the General Protection Data Regulation (GDPR) would impose certain constraints. One of the laws and regulations is patient confidentiality, which makes it necessary for each and every medical practice to create safeguards against the leaking of confidential patient information. Another of the laws and regulations is the use of ICD-10 codes, which allow for more specificity in reporting of patient diagnoses. Other laws and regulations, in certain jurisdictions, may include requirements to pseudonymize, pseudonymise, anonymize or anonymise (the terms can have different meanings in different countries and jurisidictions) data subject (i.e., patient) personally identifying information (PII) or personal health identifying information (PHI).

[0642] Another law and regulation pertains to balance billing. When a healthcare provider signs a contract with an insurance company, the healthcare provider agrees to take a certain percentage or payment amount for specific services. The amount the healthcare provider bills over the agreed upon amount with the insurance provider must be written off by the healthcare provider’s office. That is, the healthcare provider cannot bill the patient for any amount over the negotiated rate. If, nevertheless, a healthcare provider does this, it is referred to as balance billing, which is illegal per the contract with the insurance company.

[0643] Further, medical billing fraud is also specified as being illegal by HIPAA. Medical billing fraud may refer to a healthcare provider’s office knowingly billing for services that were not performed, or that are inaccurately represented or described.

[0644] At 41008, the processing device may generate, based on the set of billing procedures, a billing sequence for at least a portion of the set of instructions included in the treatment plan. Just a portion of the total number of instructions may be accounted for in the billing sequence because some of the instructions may not yet have been completed or may still be completed in the future. However, if all the instructions included in the treatment plan are completed, then the billing sequence may be generated for all of the instructions. The billing sequence may be tailored according to a certain parameter. The parameter may be a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof.

[0645] At 41010, the processing device may transmit the treatment plan and the billing sequence to a computing device. The computing device may be any of the interfaces described with reference to FIG. 27. For example, the treatment plan and the billing sequence may be transmitted to an assistant interface 4094 and/or a patient interface 4050. [0646] In some embodiments, the processing device may cause presentation, in real-time or near real-time during a telemedicine session with a computing device of the patient, of the treatment plan and the billing sequence on a computing device of the medical professional. Further, the processing device may cause presentation, in real-time or near real-time during a telemedicine session with the computing device of the medical professional, of the treatment plan and the billing sequence on the computing device of the medical professional.

[0647] In some embodiments, the processing device may control, based on the treatment plan, the treatment apparatus 4070 used by the patient to perform the treatment plan. For example, the processing device may transmit a control signal to cause a range of motion of the pedals 4102 to adjust (e.g., by electromechanically adjusting the pedals 4102 attached to the pedal arms 4104 inwardly or outwardly on the axle 4106) to a setting specified in the treatment plan. In some embodiments, and as further described herein, a patient may view the treatment plan and/or the billing sequence and select to initiate the treatment plan using the patient interface 4050. In some embodiments, and as further described herein, an assistant (e.g., medical professional) may view the treatment plan and/or the billing sequence and, using the assistant interface 4094, select to initiate the treatment plan. In such an embodiment, the treatment apparatus 4070 may be distally controlled via a remote computing device (e.g., server 4030, assistant interface 4094, etc.). For example, the remote computing device may transmit one or more control signals to the controller 4072 of the treatment apparatus 4070 to cause the controller 4072 to execute instructions based on the control signals. By executing the instructions, the controller 4072 may control various parts (e.g., pedals, motor, etc.) of the treatment apparatus 4070 in real-time or near real-time while the patient uses the treatment apparatus 4070.

[0648] In some embodiments, the treatment plan, including the configurations, settings, range of motion settings, pain level, force settings, speed settings, etc. of the treatment apparatus 4070 for various exercises, may be transmitted to the controller of the treatment apparatus 4070. In one example, if the user provides an indication, via the patient interface 4050, that he is experiencing a high level of pain at a particular range of motion, the controller may receive the indication. Based on the indication, the controller may electronically adjust the range of motion of the pedal 4102 by adjusting the pedal inwardly or outwardly via one or more actuators, hydraulics, springs, electric, mechanical, optical, opticoelectric or electromechanical motors, or the like. When the user indicates certain pain levels during an exercise, the treatment plan may define alternative range of motion settings for the pedal 4102. Accordingly, once the treatment plan is uploaded to the controller of the treatment apparatus 4070, the treatment apparatus may be self-functioning. It should be noted that the patient (via the patient interface 50) and/or the assistant (via the assistant interface 4094) may override any of the configurations or settings of the treatment apparatus 4070 at any time. For example, the patient may use the patient interface 4050 to cause the treatment apparatus 4070 to immediately stop, if so desired.

[0649] FIG. 37 shows an example embodiment of a method 41100 for receiving requests from computing devices and modifying the billing sequence based on the requests according to the present disclosure. Method 1100 includes operations performed by processors of a computing device (e.g., any component of FIG. 27, such as server 4030 executing the artificial intelligence engine 4011). In some embodiments, one or more operations of the method 41100 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 41100 may be performed in the same or in a similar manner as described above in regard to method 41000. The operations of the method 41100 may be performed in some combination with any of the operations of any of the methods described herein.

[0650] At 41102, the processing device may receive, from a computing device, a first request pertaining to the billing sequence. The request may be received from a computing device of a medical professional. The request may specify that the medical professional desires instant payment of his or her portion of the bills included in the billing sequence, funds to be received sooner than had the original billing sequence been implemented, an optimized total amount of the funds to be received, an optimized number of payments to be received, an optimized schedule for the funds to be received, or some combination thereof.

[0651] At 41104, the processing device may receive, from another computing device of an insurance provider, a second request pertaining to the billing sequence. The second request may specify the insurance provider desires instant payment of their portion of the bills in the billing sequence, to be received sooner than had the original billing sequence been implemented, an optimized total amount of the funds to be received, an optimized number of payments to be received, an optimized schedule for the funds to be received, or some combination thereof.

[0652] At 41106, the processing device may modify, based on the first request and the second request, the billing sequence to generate a modified billing sequence, such that the modified billing sequence results in funds being received sooner than had the original billing sequence been implemented, an optimized total amount of the funds to be received, an optimized number of payments to be received, an optimized schedule for the funds to be received, or some combination thereof. The modified billing sequence may be generated to comply with the billing procedures. For example, the modified billing sequence may be generated to ensure that the modified billing sequence is free of medical billing fraud and/or balance billing.

[0653] FIG. 38 shows an embodiment of the overview display 4120 of the assistant interface 4094 presenting, in real-time during a telemedicine session, optimal treatment plans that generate certain monetary value amounts and result in certain patient outcomes according to the present disclosure. As depicted, the overview display 4120 just includes sections for the patient profile 4130 and the video feed display 4180, including the self-video display 4182. Any suitable configuration of controls and interfaces of the overview display 4120 described with reference to FIG. 31 may be presented in addition to or instead of the patient profile 4130, the video feed display 4180, and the self-video display 4182. In some embodiments, the same optimal treatment plans, including monetary value amounts generated, patient outcomes, and/or risks, may be presented in a display screen 4054 of the patient interface 4050. In some embodiments, the optimal treatment plans including monetary value amounts generated, patient outcomes, and/or risks may be presented simultaneously, in real-time or near real time, during a telehealth session, on both the display screen 4054 of the patient interface 4050 and the display screen 4024 of the assistant interface 4094.

[0654] The assistant (e.g., medical professional) using the assistant interface 4094 (e.g., computing device) during the telemedicine session may be presented in the self-video 4182 in a portion of the overview display 4120 (e.g., user interface presented on a display screen 4024 of the assistant interface 4094) that also presents a video from the patient in the video feed display 4180. Further, the video feed display 4180 may also include a graphical user interface (GUI) object 4700 (e.g., a button) that enables the medical professional to share, in real time or near real-time during the telemedicine session, the optimal treatment plans including the monetary value amounts generated, patient outcomes, risks, etc. with the patient on the patient interface 4050. The medical professional may select the GUI object 4700 to share the treatment plans. As depicted, another portion of the overview display 4120 includes the patient profile display 4130.

[0655] The patient profile display 4130 is presenting two example optimal treatment plans 41200 and 41202. The optimal treatment plan 41200 includes a monetary value amount generated 41204 by the optimal treatment plan 41200, a patient outcome 41206 associated with performing the optimal treatment plan 41200, and a risk 41208 associated with performing the optimal treatment plan 41200. The optimal treatment plan 41202 includes a monetary value amount generated 41210 by the optimal treatment plan 41202, a patient outcome 41212 associated with performing the optimal treatment plan 41202, and a risk 41214 associated with performing the optimal treatment plan 41202. The risks may be determined using an algorithm that accounts for a difficulty of a procedure (e.g., open heart surgery versus an endoscopy), a skill level of a medical professional based on years of experience, malpractice judgments, and/or peer reviews, and various other factors.

[0656] To generate the optimal treatment plans 41200 and 41202, the artificial intelligence engine 4011 may receive (i) information pertaining to a medical condition of the patient; (ii) a set of treatment plans that, when applied to patients having a similar medical condition as the patient, cause outcomes to be achieved by the patients; (ii) a set of monetary value amounts associated with the set of treatment plans; and/or (iii) a set of constraints including laws, regulations, and/or rules pertaining to billing codes associated with the set of treatment plans (e.g., more particularly, laws, regulations, and/or rules pertaining to billing codes associated with procedures and/or instructions included in the treatment plans).

[0657] Based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, the artificial intelligence engine 4011 may use one or more trained machine learning models 4013 to generate the optimal treatment plans 41200 and 41202 for the patient. Each of the optimal treatment plans 41200 and 41202 complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated. It should be noted that the optimal treatment plans may be generated and tailored based on one or more parameters (e.g., monetary value amount generated, patient outcome, and/or risk). The one or more parameters may be selected electronically by the artificial intelligence engine 4011 or by a user (e.g., medical professional) using a user interface (e.g., patient profile display 4130) to tailor how the treatment plans are optimized. For example, the user may specify she wants to see optimal treatment plans tailored based on the best patient outcome or, alternatively, based on the maximum monetary value amount generated.

[0658] Each of the respective treatment plans 41200 and 41202 may include one or more procedures to be performed on the patient based on the information pertaining to the medical condition of the patient. Further, each of the respective treatment plans 41200 and 41202 may include one or more billing codes associated with the one or more procedures.

[0659] For example, as depicted, the patient profile display 4130 presents “Patient has Condition Z”, where condition Z may be associated with information of the patient including a particular medical diagnosis code received from an EMR system. The patient profile display 4130 also presents the optimal treatment plan 41200, “Optimal Treatment plan 1 Tailored for [Parameter X] : 1. Procedure A; billing code 123; 2. Procedure B; billing code 234”. The [Parameter X] may be any suitable parameter, such as a monetary value amount generated by the optimal treatment plan, a patient outcome associated with performing the optimal treatment plan, and/or a risk associated with performing the optimal treatment plan. [0660] The patient profile display 4130 presents “Monetary Value Amount Generated for Treatment Plan 1: $monetary ValueX”. monetary ValueX may be any suitable monetary value amount associated with the optimal treatment plan 41200. In some embodiments, monetary ValueX may be a configurable parameter that enables the user to set a desired monetary value amount to be generated.

[0661] The patient profile display 4130 presents “Patient Outcome: patientOutcomel”. patientOutcomel may be any suitable patient outcome (e.g., full recovery or partial recovery, achievement of full or partial: desired range of motion, flexibility, strength, or pliability, etc.) associated with the optimal treatment plan 41200. In some embodiments, patientOutcomel may be a configurable parameter that enables the user to set a desired patient outcome that results from performing the optimal treatment plan.

[0662] The patient profile display 4130 presents “Risk: riskl”. riskl may be any suitable risk (e.g., low, medium, or high; or an absolute or relative number or magnitude on a scale; etc.) associated with the optimal treatment plan 41200. In some embodiments, riskl may be a configurable parameter that enables the user to set a desired risk associated with performing the optimal treatment plan.

[0663] Further, the patient profile display 4130 also presents the optimal treatment plan 41202, “Optimal Treatment plan 2 Tailored for [Parameter Y]: 1. Procedure A; billing code 123; 2. Procedure C; billing code 345”. The [Parameter Y] may be any suitable parameter, such as a monetary value amount generated by the optimal treatment plan, a patient outcome associated with performing the optimal treatment plan, and/or a risk associated with performing the optimal treatment plan.

[0664] The patient profile display 4130 presents “Monetary Value Amount Generated for Treatment Plan 1: $monetary Value Y”. monetary ValueX may be any suitable monetary value amount associated with the optimal treatment plan 41202. In some embodiments, monetary ValueX may be a configurable parameter that enables the user to set a desired monetary value amount to be generated.

[0665] The patient profile display 4130 presents “Patient Outcome: patientOutcome2”. patientOutcome2 may be any suitable patient outcome (e.g., full recovery or partial recovery, achievement of full or partial: desired range of motion, flexibility, strength, or pliability, etc.) associated with the optimal treatment plan 41202. In some embodiments, patientOutcome2 may be a configurable parameter that enables the user to set a desired patient outcome that results from performing the optimal treatment plan.

[0666] The patient profile display 4130 presents “Risk: risk2”. Risk2 may be any suitable risk (e.g., low, medium, or high; or an absolute or relative number or magnitude on a scale; etc..) associated with the optimal treatment plan 41200. In some embodiments, risk2 may be a configurable parameter that enables the user to set a desired risk associated with performing the optimal treatment plan.

[0667] In the depicted example, the [Parameter X] and the [Parameter Y] both correspond to the parameter pertaining to the monetary value amount generated. The monetary value amount generated for [Parameter X] may be set higher than the monetary value amount generated for [Parameter Y] Accordingly, the optimal treatment plan 41200 may include different procedures (e.g., Procedure A and Procedure B) that result in the higher monetary amount generated ([Parameter X]), a better outcome (e.g., patientOutcome 1), and a higher risk (e.g., riskl) than the optimal treatment plan 41202, which may result in a lesser monetary value amount generated ([Parameter y]), less desirable outcome (e.g., patientOutcome2), and a lower risk (e.g., risk2).

[0668] Further, as depicted, a graphical element (e.g., button for “SELECT”) may be presented in the patient profile display 4130. Although just one graphical element is presented, any suitable number of graphical elements for selecting an optimal treatment may be presented in the patient profile display 4130. As depicted, a user (e.g., medical professional or patient) uses an input peripheral (e.g., mouse, keyboard, microphone, touchscreen) to select (as represented by circle 41250) the graphical element associated with the optimal treatment plan 41200. The medical professional may prefer to receive a higher monetary value amount generated (e.g., [Parameter X]) from the optimal treatment plan and/or the patient may have requested the best patient outcome possible. Accordingly, the assistant interface 4094 may transmit a control signal to the treatment apparatus 4070 to control, based on the treatment plan 41200, operation of the treatment apparatus 4070. In some embodiments, the patient may select the treatment plan from the display screen 4054 and the patient interface 4050 may transmit a control signal to the treatment apparatus 4070 to control, based on the selected treatment plan 41200, operation of the treatment apparatus 4070.

[0669] It should be noted that, in some embodiments, just treatment plans that pass muster with respect to standard of care, regulations, laws, and the like may be presented as viable options on a computing device of the patient and/or the medical professional. Accordingly, non-viable treatment plans that fail to meet a standard of care, violate a regulation and/or law, etc. may not be presented as options for selection. For example, the non- viable treatment plan options may be filtered from a result set presented on the computing device. In some embodiments, any treatment plan (e.g., both viable and non-viable options) may be presented on the computing device of the patient and/or medical professional.

[0670] FIG. 39 shows an example embodiment of a method 41300 for generating optimal treatment plans for a patient, where the generating is based on a set of treatment plans, a set of monetary value amounts, and a set of constraints according to the present disclosure. Method 41300 includes operations performed by processors of a computing device (e.g., any component of FIG. 27, such as server 4030 executing the artificial intelligence engine 4011). In some embodiments, one or more operations of the method 41300 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 41300 may be performed in the same or in a similar manner as described above in regard to method 41300. The operations of the method 41300 may be performed in some combination with any of the operations of any of the methods described herein.

[0671] Prior to the method 41300 beginning, the processing device may receive information pertaining to the patient. The information may include a medical diagnosis code and/or the various characteristics (e.g., personal information, performance information, and measurement information, etc.) described herein. The processing device may match the information of the patient with similar information from other patients. Based upon the matching, the processing device may select a set of treatment plans that cause certain outcomes (e.g., desired results) to be achieved by the patients.

[0672] At 41302, the processing device may receive the set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients. In some embodiments, the set of treatment plans may specify procedures to perform for the condition of the patient, a set of exercises to be performed by the patient using the treatment apparatus 4070, a periodicity to perform the set of exercises using the treatment apparatus 4070, a frequency to perform the set of exercises using the treatment apparatus 4070, settings and/or configurations for portions (e.g., pedals, seat, etc.) of the treatment apparatus 4070, and the like.

[0673] At 41304, the processing device may receive a set of monetary value amounts associated with the set of treatment plans. A respective monetary value amount of the set of monetary value amounts may be associated

- I l l - with a respective treatment plan of the set of treatment plans. For example, one respective monetary value amount may indicate $5,000 may be generated if the patient performs the respective treatment plan (e.g., including a consultation with a medical professional during a telemedicine session, rental fee for the treatment apparatus 4070, follow-up in-person visit with the medical professional, etc.).

[0674] At 41306, the processing device may receive a set of constraints. The set of constraints may include rules pertaining to billing codes associated with the set of treatment plans. In some embodiments, the processing device may receive a set of billing codes associated with the procedures to be performed for the patient, the set of exercises, etc. and apply the set of billing codes to the treatment plans in view of the rules. In some embodiments, the set of constraints may further include constraints set forth in regulations, laws, or some combination thereof. For example, the laws and/or regulations may specify that certain billing codes (e.g., DRG or ICD-10) be used for certain procedures and/or exercises.

[0675] At 41308, the processing device may generate, by the artificial intelligence engine 4011, optimal treatment plans for a patient. Generating the optimal treatment plans may be based on the set of treatment plans, the set of monetary value amounts, and the set of constraints. In some embodiments, generating the optimal treatment plans may include optimizing the optimal treatment plans for fees, revenue, profit (e.g., gross, net, etc.), earnings before interest (EBIT), earnings before interest, depreciation and amortization (EBITDA), cash flow, free cash flow, working capital, gross revenue, a value of warrants, options, equity, debt, derivatives or any other financial instrument, any generally acceptable financial measure or metric in corporate finance or according to Generally Accepted Accounting Principles (GAAP) or foreign counterparts, or some combination thereof.

[0676] Each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated. To ensure the procedure is allowed, the set of constraints may be enforced by comparing each procedure included in the optimal treatment plan with the set of constraints. If the procedure is allowed, based on the set of constraints, the procedure is included in the optimal treatment plan. If the procedure is not allowed, based on the set of constraints, the procedure is excluded from the optimal treatment plan. The optimal treatment plans may pertain to habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength, pliability, flexibility, weight stability, weight gain, weight loss, cardiovascular fitness, performance or metrics, endurance, respiratory fitness, performance or metrics, or some combination thereof.

[0677] In some embodiments, a first optimal treatment plan of the optimal treatment plans may result in a first patient outcome and a first monetary value amount generated, and a second optimal treatment plan of the optimal treatment plans may result in a second patient outcome and a second monetary value amount generating. The second patient outcome may be better than the first patient outcome and the second monetary value amount generated may be greater than the first monetary value amount generated. Based on certain criteria (e.g., whether the patient desires the best patient outcome or has limited funds), either the first or second optimal treatment plan may be selected and implemented to control the treatment apparatus 4070. In this and other scenarios herein, both patient outcomes, even the inferior one, are at or above the standard of care dictated by ethical medical practices for individual medical professionals, hospitals, etc., as the case may be, and such standard of care shall further be consistent with applicable governing regulations and laws, whether de facto or de jure. [0678] At 41310, the processing device may transmit, in real-time or near real-time, the optimal treatment plans to be presented on a computing device of a medical professional. The optimal treatment plans may be presented on the computing device of the medical professional during a telemedicine or telehealth session in which a computing device of the patient is engaged. In some embodiments, the processing device may transmit the optimal treatment plans to be presented, in real-time or near real-time, on a computing device of the patient during a telemedicine session in which the computing device of the medical professional is engaged.

[0679] In some embodiments, the processing device may receive levels of risk associated with the set of treatment plans. In some embodiments, the levels of risk may be preconfigured for each of the set of treatment plans. In some embodiments, the levels of risk may be dynamically determined based on a number of factors (e.g., condition of the patient, difficulty of procedures included in the treatment plan, etc.). In some embodiments, generating the optimal treatment plans may also be based on the levels of risk. Further, in some embodiments, the processing device may transmit the optimal treatment plans and the levels of risk to be presented on the computing device of the medical professional. As used herein, “levels of risk” includes levels of risk for each of one or more risks.

[0680] FIG. 40 shows an example embodiment of a method 41400 for receiving a selection of a monetary value amount and generating an optimal treatment plan based on a set of treatment plans, the monetary value amount, and a set of constraints according to the present disclosure. Method 41400 includes operations performed by processors of a computing device (e.g., any component of FIG. 27, such as server 4030 executing the artificial intelligence engine 4011). In some embodiments, one or more operations of the method 41400 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 41400 may be performed in the same or a similar manner as described above in regard to method 41000. The operations of the method 41400 may be performed in some combination with any of the operations of any of the methods described herein.

[0681] At 41402, the processing device may receive a selection of a certain monetary value amount of the set of monetary value amounts. For example, a graphical element included on a user interface of a computing device may enable a user to select (e.g., enter a monetary value amount in a textbox or select from a drop-down list, radio button, scrollbar, etc.) the certain monetary value amount to be generated by an optimal treatment plan. The certain monetary value amount may be transmitted to the artificial intelligence engine 4011, which uses the certain monetary value amount to generate an optimal treatment plan tailored for the desired monetary value amount.

[0682] At 41404, the processing device may generate, by the artificial intelligence engine 4011, an optimal treatment plan based on the set of treatment plans, the certain monetary value amount, and the set of constraints. The optimal treatment plan complies with the set of constraints and represents another patient outcome and the certain monetary value amount.

[0683] FIG. 41 shows an example embodiment of a method 41500 for receiving a selection of an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus according to the present disclosure. Method 41500 includes operations performed by processors of a computing device (e.g., any component of FIG. 27, such as server 4030 executing the artificial intelligence engine 4011). In some embodiments, one or more operations of the method 41500 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 41500 may be performed in the same or a similar manner as described above in regard to method 1000. The operations of the method 41500 may be performed in some combination with any of the operations of any of the methods described herein.

[0684] Prior to the method 41500 being executed, various optimal treatment plans may be generated by one or more trained machine learning models 4013 of the artificial intelligence engine 4011. For example, based on a set of treatment plans pertaining to a medical condition of a patient, a set of monetary value amounts associated with the set of treatment plans, and a set of constraints, the one or more trained machine learning models 4013 may generate the optimal treatment plans. In some embodiments, the one or more trained machine learning models 4013 may generate a billing sequence that is tailored based on a parameter (e.g., a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof). The various treatment plans and/or billing sequences may be transmitted to one or computing devices of a patient and/or medical professional.

[0685] At 41502 of the method 41500, the processing device may receive a selection of an optimal treatment plan from the optimal treatment plans. The selection may have been entered on a user interface presenting the optimal treatment plans on the patient interface 4050 and/or the assistant interface 4094. In some embodiments, the processing device may receive a selection of a billing sequence associated with at least a portion of a treatment plan. The selection may have been entered on a user interface presenting the billing sequence on the patient interface 4050 and/or the assistant interface 4094. If the user selects a particular billing sequence, the treatment plan associated with the selected billing sequence may be selected.

[0686] At 41504, the processing device may control, based on the selected optimal treatment plan, the treatment apparatus 4070 while the patient uses the treatment apparatus. In some embodiments, the controlling is performed distally by the server 4030. For example, if the selection is made using the patient interface 4050, one or more control signals may be transmitted from the patient interface 4050 to the treatment apparatus 4070 to configure, according to the selected treatment plan, a setting of the treatment apparatus 4070 to control operation of the treatment apparatus 4070. Further, if the selection is made using the assistant interface 4094, one or more control signals may be transmitted from the assistant interface 4094 to the treatment apparatus 4070 to configure, according to the selected treatment plan, a setting of the treatment apparatus 4070 to control operation of the treatment apparatus 4070.

[0687] It should be noted that, as the patient uses the treatment apparatus 4070, the sensors 4076 may transmit measurement data to a processing device. The processing device may dynamically control, according to the treatment plan, the treatment apparatus 4070 by modifying, based on the sensor measurements, a setting of the treatment apparatus 4070. For example, if the force measured by the sensor 4076 indicates the user is not applying enough force to a pedal 4102, the treatment plan may indicate to reduce the required amount of force for an exercise.

[0688] It should be noted that, as the patient uses the treatment apparatus 4070, the user may use the patient interface 4050 to enter input pertaining to a pain level experienced by the patient as the patient performs the treatment plan. For example, the user may enter a high degree of pain while pedaling with the pedals 4102 set to a certain range of motion on the treatment apparatus 4070. The pain level may cause the range of motion to be dynamically adjusted based on the treatment plan. For example, the treatment plan may specify alternative range of motion setings if a certain pain level is indicated when the user is performing an exercise at a certain range of motion.

[0689] Different people have different tolerances for pain. In some embodiments, a person may indicate a pain level they are willing to tolerate to achieve a certain result (e.g., a certain range of motion within a certain time period). A high degree of pain may be acceptable to a person if that degree of pain is associated with achieving the certain result. The treatment plan may be tailored based on the indicated pain level. For example, the treatment plan may include certain exercises, frequencies of exercises, and/or periodicities of exercises that are associated with the indicated pain level and desired result for people having characteristics similar to characteristics of the person.

[0690] FIG. 42 shows an example computer system 41600 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 41600 may include a computing device and correspond to the assistance interface 4094, reporting interface 4092, supervisory interface 4090, clinician interface 4020, server 4030 (including the AI engine 4011), patient interface 4050, ambulatory sensor 4082, goniometer 4084, treatment apparatus 4070, pressure sensorfO 86, or any suitable component of FIG. 27. The computer system 41600 may be capable of executing instructions implementing the one or more machine learning models 4013 of the artificial intelligence engine 4011 of FIG. 27. The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet, including via the cloud or a peer-to-peer network. The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a wearable (e.g., wristband), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[0691] The computer system 41600 includes a processing device 41602, a main memory 41604 (e.g., read only memory (ROM), flash memory, solid state drives (SSDs), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 41606 (e.g., flash memory, solid state drives (SSDs), static random access memory (SRAM)), and a data storage device 41608, which communicate with each other via a bus 41610.

[0692] Processing device 41602 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 41602 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 41602 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 41602 is configured to execute instructions for performing any of the operations and steps discussed herein. [0693] The computer system 41600 may further include a network interface device 41612. The computer system 41600 also may include a video display 41614 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), one or more input devices 41616 (e.g., a keyboard and/or a mouse or a gaming-like control), and one or more speakers 41618 (e.g., a speaker). In one illustrative example, the video display 41614 and the input device(s) 41616 may be combined into a single component or device (e.g., an LCD touch screen).

[0694] The data storage device 41616 may include a computer-readable medium 41620 on which the instructions 41622 embodying any one or more of the methods, operations, or functions described herein is stored. The instructions 41622 may also reside, completely or at least partially, within the main memory 41604 and/or within the processing device 41602 during execution thereof by the computer system 41600. As such, the main memory 41604 and the processing device 41602 also constitute computer-readable media. The instructions 41622 may further be transmitted or received over a network via the network interface device 41612. [0695] While the computer-readable storage medium 41620 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer- readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[0696] Clause 1.3. A method for generating, by an artificial intelligence engine, a treatment plan and a billing sequence associated with the treatment plan, the method comprising:

[0697] receiving information pertaining to a patient, wherein the information comprises a medical diagnosis code of the patient;

[0698] generating, based on the information, the treatment plan for the patient, wherein the treatment plan comprises a plurality of instructions for the patient to follow;

[0699] receiving a set of billing procedures associated with the plurality of instructions, wherein the set of billing procedures comprises rules pertaining to billing codes, timing, constraints, or some combination thereof; [0700] generating, based on the set of billing procedures, the billing sequence for at least a portion of the plurality of instructions, wherein the billing sequence is tailored according to a certain parameter; and [0701] transmitting the treatment plan and the billing sequence to a computing device.

[0702] Clause 2.3. The method of any preceding clause, further comprising distally controlling, based on the treatment plan, a treatment apparatus used by the patient to perform the treatment plan.

[0703] Clause 3.3. The method of any preceding clause, wherein the certain parameter is a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof.

[0704] Clause 4.3. The method of any preceding clause, wherein the treatment plan is for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength training, endurance training, weight loss, weight gain, flexibility, pliability, or some combination thereof. [0705] Clause 5.3. The method of any preceding clause, wherein the plurality of instructions comprises: [0706] a plurality of exercises for the patient to perform,

[0707] an order for the plurality of exercises,

[0708] a frequency for performing the plurality of exercises,

[0709] a diet regimen,

[0710] a sleep regimen,

[0711] a plurality of procedures to perform on the patient,

[0712] an order for the plurality of procedures,

[0713] a medication regimen,

[0714] a plurality of sessions for the patient, or [0715] some combination thereof.

[0716] Clause 6.3. The method of any preceding clause, further comprising causing presentation, in real time or near real-time during a telemedicine session with another computing device of the patient, of the treatment plan and the billing sequence on the computing device of a medical professional.

[0717] Clause 7.3. The method of any preceding clause, further comprising:

[0718] receiving, from the computing device, a first request pertaining to the billing sequence;

[0719] receiving, from another computing device of an insurance provider, a second request pertaining to the billing sequence;

[0720] modifying, based on the first request and the second request, the billing sequence to generate a modified billing sequence, such that the modified billing sequence results in funds being received sooner than had the billing sequence been implemented, an optimized total amount of the funds being received, an optimized number of payments being received, an optimized schedule for the funds being received, or some combination thereof. [0721] Clause 8.3. The method of any preceding clause, wherein the constraints further comprise constraints set forth in regulations, laws, or some combination thereof.

[0722] Clause 9.3. The method of any preceding clause, further comprising transmitting the treatment plan and the billing sequence to be presented on a second computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged. [0723] Clause 10.3. A system, comprising:

[0724] a memory device storing instructions;

[0725] a processing device communicatively coupled to the memory device, the processing device executes the instructions to:

[0726] receive information pertaining to a patient, wherein the information comprises a medical diagnosis code of the patient;

[0727] generate, based on the information, a treatment plan for the patient, wherein the treatment plan comprises a plurality of instructions for the patient to follow;

[0728] receive a set of billing procedures associated with the plurality of instructions, wherein the set of billing procedures comprises rules pertaining to billing codes, timing, constraints, or some combination thereof;

[0729] generate, based on the set of billing procedures, a billing sequence for at least a portion of the plurality of instructions, wherein the billing sequence is tailored according to a certain parameter; and [0730] transmit the treatment plan and the billing sequence to a computing device. [0731] Clause 11.3. The system of any preceding clause, wherein the processing device is further to distally control, based on the treatment plan, a treatment apparatus used by the patient to perform the treatment plan.

[0732] Clause 12.3. The system of any preceding clause, wherein the certain parameter is a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof.

[0733] Clause 13.3. The system of any preceding clause, wherein the treatment plan is for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength training, endurance training, weight loss, weight gain, flexibility, pliability, or some combination thereof.

[0734] Clause 14.3. The system of any preceding clause, wherein the plurality of instructions comprises: [0735] a plurality of exercises for the patient to perform,

[0736] an order for the plurality of exercises,

[0737] a frequency for performing the plurality of exercises,

[0738] a diet regimen,

[0739] a sleep regimen,

[0740] a plurality of procedures to perform on the patient,

[0741] an order for the plurality of procedures,

[0742] a medication regimen,

[0743] a plurality of sessions for the patient, or [0744] some combination thereof.

[0745] Clause 15.3. The system of any preceding clause, wherein the processing device is further to cause presentation, in real-time or near real-time during a telemedicine session with another computing device of the patient, of the treatment plan and the billing sequence on the computing device of a medical professional. [0746] Clause 16.3. The system of any preceding clause, wherein the processing device is further to: [0747] receive, from the computing device, a first request pertaining to the billing sequence;

[0748] receive, from another computing device of an insurance provider, a second request pertaining to the billing sequence;

[0749] modify, based on the first request and the second request, the billing sequence to generate a modified billing sequence, such that the modified billing sequence results in funds being received sooner than had the billing sequence been implemented, an optimized total amount of the funds being received, an optimized number of payments being received, an optimized schedule for the funds being received, or some combination thereof. [0750] Clause 17.3. The system of any preceding clause, wherein the constraints further comprise constraints set forth in regulations, laws, or some combination thereof.

[0751] Clause 18.3. The system of any preceding clause, wherein the processing device is further to transmit the treatment plan and the billing sequence to be presented on a second computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged.

[0752] Clause 19.3. A tangible, non-transitoiy computer-readable medium storing instructions that, when executed, cause a processing device to: [0753] receive information pertaining to a patient, wherein the information comprises a medical diagnosis code of the patient;

[0754] generate, based on the information, a treatment plan for the patient, wherein the treatment plan comprises a plurality of instructions for the patient to follow;

[0755] receive a set of billing procedures associated with the plurality of instructions, wherein the set of billing procedures comprises rules pertaining to billing codes, timing, constraints, or some combination thereof;

[0756] generate, based on the set of billing procedures, a billing sequence for at least a portion of the plurality of instructions, wherein the billing sequence is tailored according to a certain parameter; and [0757] transmit the treatment plan and the billing sequence to a computing device.

[0758] Clause 20.3. The computer-readable medium of any preceding clause, wherein the processing device is further to distally control, based on the treatment plan, a treatment apparatus used by the patient to perform the treatment plan.

[0759] Clause 21.3. A method for generating, by an artificial intelligence engine, treatment plans for optimizing patient outcome and monetary value amount generated, the method comprising:

[0760] receiving a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients;

[0761] receiving a set of monetary value amounts associated with the set of treatment plans, wherein a respective monetary value amount of the set of monetary value amounts is associated with a respective treatment plan of the set of treatment plans;

[0762] receiving a set of constraints, wherein the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans;

[0763] generating, by the artificial intelligence engine, optimal treatment plans for a patient, wherein the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, wherein each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated; and

[0764] transmitting the optimal treatment plans to be presented on a computing device.

[0765] Clause 22.3. The method of any preceding clause, wherein the optimal treatment plans are for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength, pliability, flexibility, weight stability, weight gain, weight loss, cardiovascular, endurance, respiratory, or some combination thereof.

[0766] Clause 23.3. The method of any preceding clause, further comprising:

[0767] receiving a selection of a certain monetary value amount of the set of monetary value amounts; and [0768] generating, by the artificial intelligence engine, an optimal treatment plan based on the set of treatment plans, the certain monetary value amount, and the set of constraints, wherein the optimal treatment plan complies with the set of constraints and represents another patient outcome and the certain monetary value amount. [0769] Clause 24.3. The method of any preceding clause, wherein the set of treatment plans specifies a set of exercises to be performed by the patient using a treatment apparatus, and the method further comprises: [0770] receiving a set of billing codes associated with the set of exercises; and [0771] correlating the set of billing codes with the rules.

[0772] Clause 25.3. The method of any preceding clause, further comprising: [0773] receiving levels of risk associated with the set of treatment plans, wherein the generating the optimal treatment plans is also based on the levels of risk; and

[0774] transmitting the optimal treatment plans and the level of risks to be presented on the computing device of the medical professional.

[0775] Clause 26.3. The method of any preceding clause, wherein:

[0776] a first optimal treatment plan of the optimal treatment plans results in a first patient outcome and a first monetary value amount generated; and

[0777] a second optimal treatment plan of the optimal treatment plans results in a second patient outcome and a second monetary value amount generated, wherein the second patient outcome is better than the first patient outcome and the second revenue value generated is greater than the first monetary value amount generated. [0778] Clause 27.3. The method of any preceding clause, wherein the set of constraints further comprises constraints set forth in regulations, laws, or some combination thereof.

[0779] Clause 28.3. The method of any preceding clause, further comprising transmitting the optimal treatment plans to be presented on a computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged.

[0780] Clause 29.3. The method of any preceding clause, further comprising:

[0781] receiving a selection of an optimal treatment plan from the optimal treatment plans; and

[0782] controlling, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus.

[0783] Clause 30.3. The method of any preceding clause, wherein the controlling is performed distally . [0784] Clause 31.3. The method of any preceding clause, wherein:

[0785] the optimal treatment plans are presented on the computing device of a medical professional during a telemedicine session in which a computing device of the patient is engaged.

[0786] Clause 32.3. The method of any preceding clause, wherein:

[0787] the optimal treatment plans are presented on the computing device of patient during a telemedicine session in which a computing device of a medical professional is engaged.

[0788] Clause 33.3. The method of any preceding clause, wherein the generating, based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, the optimal treatment plans further comprises optimizing the optimal treatment plans for revenue generated, profit generated, cash flow generated, free cash flow generated, gross revenue generated, earnings before interest taxes amortization (EBITA) generated, or some combination thereof.

[0789] Clause 34.3. A system, comprising:

[0790] a memory device storing instructions; and

[0791] a processing device communicatively coupled to the memory device, the processing device executes the instructions to:

[0792] receive a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients;

[0793] receive a set of monetary value amounts associated with the set of treatment plans, wherein a respective monetary value amount of the set of monetary value amounts is associated with a respective treatment plan of the set of treatment plans; [0794] receive a set of constraints, wherein the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans;

[0795] generate, by an artificial intelligence engine, optimal treatment plans for a patient, wherein the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, wherein each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated; and

[0796] transmit the optimal treatment plans to be presented on a computing device.

[0797] Clause 35.3. The system of any preceding clause, wherein the optimal treatment plans are for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength, pliability, flexibility, weight stability, weight gain, weight loss, cardiovascular, endurance, respiratory, or some combination thereof.

[0798] Clause 36.3. The system of any preceding clause, wherein the processing device is further to: [0799] receive a selection of a certain monetary value amount of the set of monetary value amounts; and [0800] generate, by the artificial intelligence engine, an optimal treatment plan based on the set of treatment plans, the certain monetary value amount, and the set of constraints, wherein the optimal treatment plan complies with the set of constraints and represents another patient outcome and the certain monetary value amount. [0801] Clause 37.3. The system of any preceding clause, wherein the set of treatment plans specifies a set of exercises to be performed by the patient using a treatment apparatus, and the processing device is further to:

[0802] receive a set of billing codes associated with the set of exercises; and [0803] correlate the set of billing codes with the rules.

[0804] Clause 38.3. The system of any preceding clause, wherein the processing device is further to: [0805] receive levels of risk associated with the set of treatment plans, wherein the generating the optimal treatment plans is also based on the levels of risk; and

[0806] transmit the optimal treatment plans and the level of risks to be presented on the computing device of the medical professional.

[0807] Clause 39.3. The system of any preceding clause, wherein:

[0808] a first optimal treatment plan of the optimal treatment plans results in a first patient outcome and a first monetary value amount generated; and

[0809] a second optimal treatment plan of the optimal treatment plans results in a second patient outcome and a second monetary value amount generated, wherein the second patient outcome is better than the first patient outcome and the second revenue value generated is greater than the first monetary value amount generated. [0810] Clause 40.3. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:

[0811] receive a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients;

[0812] receive a set of monetary value amounts associated with the set of treatment plans, wherein a respective monetary value amount of the set of monetary value amounts is associated with a respective treatment plan of the set of treatment plans;

[0813] receive a set of constraints, wherein the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans; [0814] generate, by an artificial intelligence engine, optimal treatment plans for a patient, wherein the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, wherein each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated; and

[0815] transmit the optimal treatment plans to be presented on a computing device.

[0816] Clause 41.3. A computer-implemented system, comprising:

[0817] a treatment apparatus configured to be manipulated by a patient while performing a treatment plan; [0818] a server computing device configured to execute an artificial intelligence engine to generate the treatment plan and a billing sequence associated with the treatment plan, wherein the server computing device: [0819] receives information pertaining to the patient, wherein the information comprises a medical diagnosis code of the patient;

[0820] generates, based on the information, the treatment plan for the patient, wherein the treatment plan comprises a plurality of instructions for the patient to follow;

[0821] receives a set of billing procedures associated with the plurality of instructions, wherein the set of billing procedures comprises rules pertaining to billing codes, timing, constraints, or some combination thereof; [0822] generates, based on the set of billing procedures, the billing sequence for at least a portion of the plurality of instructions, wherein the billing sequence is tailored according to a certain parameter; and [0823] transmits the treatment plan and the billing sequence to a computing device.

[0824] Clause 42.3. The computer-implemented system of any preceding clause, wherein the server computing device is further to distally control, based on the treatment plan, the treatment apparatus used by the patient to perform the treatment plan.

[0825] Clause 43.3. The computer-implemented system of any preceding clause, wherein the certain parameter is a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof.

[0826] Clause 44.3. The computer-implemented system of any preceding clause, wherein the treatment plan is for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength training, endurance training, weight loss, weight gain, flexibility, pliability, or some combination thereof.

[0827] Clause 45.3. The computer-implemented system of any preceding clause, wherein the plurality of instructions comprises:

[0828] a plurality of exercises for the patient to perform,

[0829] an order for the plurality of exercises,

[0830] a frequency for performing the plurality of exercises,

[0831] a diet regimen,

[0832] a sleep regimen,

[0833] a plurality of procedures to perform on the patient,

[0834] an order for the plurality of procedures,

[0835] a medication regimen,

[0836] a plurality of sessions for the patient, or [0837] some combination thereof. [0838] Clause 46.3. The computer-implemented system of any preceding clause, wherein the server computing device is further to cause presentation, in real-time or near real-time during a telemedicine session with another computing device of the patient, of the treatment plan and the billing sequence on the computing device of a medical professional.

[0839] Clause 47.3. The computer-implemented system of any preceding clause, wherein the server computing device is further to:

[0840] receive, from the computing device, a first request pertaining to the billing sequence;

[0841] receive, from another computing device of an insurance provider, a second request pertaining to the billing sequence;

[0842] modify, based on the first request and the second request, the billing sequence to generate a modified billing sequence, such that the modified billing sequence results in funds being received sooner than had the billing sequence been implemented, an optimized total amount of the funds being received, an optimized number of payments being received, an optimized schedule for the funds being received, or some combination thereof. [0843] Clause 48.3. The computer-implemented system of any preceding clause, wherein the constraints further comprise constraints set forth in regulations, laws, or some combination thereof.

[0844] Clause 49.3. The computer-implemented system of any preceding clause, wherein the server computing device is further to transmit the treatment plan and the billing sequence to be presented on a second computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged.

[0845] Clause 50.3. A computer-implemented system, comprising:

[0846] a treatment apparatus configured to be manipulated by a patient while performing a treatment plan; [0847] a server computing device configured to execute an artificial intelligence engine to generate treatment plans for optimizing patient outcome and monetary amount generated, wherein the server computing device: [0848] receives a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients;

[0849] receives a set of monetary value amounts associated with the set of treatment plans, wherein a respective monetary value amount of the set of monetary value amounts is associated with a respective treatment plan of the set of treatment plans;

[0850] receives a set of constraints, wherein the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans;

[0851] generates, by the artificial intelligence engine, optimal treatment plans for a patient, wherein the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, wherein each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated; and

[0852] transmits the optimal treatment plans to be presented on a computing device.

[0853] Clause 51.3. The computer-implemented system of any preceding clause, wherein the server computing device is further to:

[0854] receive a selection of an optimal treatment plan from the optimal treatment plans; and

[0855] control, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus. [0856] Clause 52.3. The computer-implemented system of any preceding clause, wherein the optimal treatment plans are for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength, pliability, flexibility, weight stability, weight gain, weight loss, cardiovascular, endurance, respiratory, or some combination thereof.

[0857] Clause 53.3. The computer-implemented system of any preceding clause, further comprising:

[0858] receiving a selection of a certain monetary value amount of the set of monetary value amounts; and [0859] generating, by the artificial intelligence engine, an optimal treatment plan based on the set of treatment plans, the certain monetary value amount, and the set of constraints, wherein the optimal treatment plan complies with the set of constraints and represents another patient outcome and the certain monetary value amount. [0860] Clause 54.3. The computer-implemented system of any preceding clause, wherein the set of treatment plans specifies a set of exercises to be performed by the patient using a treatment apparatus, and the method further comprises:

[0861] receiving a set of billing codes associated with the set of exercises; and [0862] correlating the set of billing codes with the rules .

[0863] Clause 55.3. The computer-implemented system of any preceding clause, further comprising:

[0864] receiving levels of risk associated with the set of treatment plans, wherein the generating the optimal treatment plans is also based on the levels of risk; and

[0865] transmitting the optimal treatment plans and the level of risks to be presented on the computing device of the medical professional.

[0866] Clause 56.3. The computer-implemented system of any preceding clause, wherein:

[0867] a first optimal treatment plan of the optimal treatment plans results in a first patient outcome and a first monetary value amount generated; and

[0868] a second optimal treatment plan of the optimal treatment plans results in a second patient outcome and a second monetary value amount generated, wherein the second patient outcome is better than the first patient outcome and the second revenue value generated is greater than the first monetary value amount generated. [0869] Clause 57.3. The computer-implemented system of any preceding clause, wherein the set of constraints further comprises constraints set forth in regulations, laws, or some combination thereof.

[0870] Clause 58.3. The computer-implemented system of any preceding clause, further comprising transmitting the optimal treatment plans to be presented on a computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged.

[0871] The above discussion is meant to be illustrative of the principles and various embodiments of the present disclosure. Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

[0872] The various aspects, embodiments, implementations, or features of the described embodiments can be used separately or in any combination. The embodiments disclosed herein are modular in nature and can be used in conjunction with or coupled to other embodiments.

[0873] Consistent with the above disclosure, the examples of assemblies enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples. METHOD AND SYSTEM FOR USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TO CREATE OPTIMAL TREATMENT PLANS BASED ON MONETARY VALUE AMOUNT GENERATED AND/OR PATIENT OUTCOME

[0874] Determining a treatment plan for a patient having certain characteristics (e.g., vital-sign or other measurements; performance; demographic; geographic; diagnostic; measurement- or test-based; medically historic; etiologic; cohort-associative; differentially diagnostic; surgical, physically therapeutic, pharmacologic and other treatment(s) recommended; etc.) may be a technically challenging problem. For example, a multitude of information may be considered when determining a treatment plan, which may result in inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitative setting, some of the multitude of information considered may include characteristics of the patient such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment apparatus, an amount of force exerted on a portion of the treatment apparatus, a range of motion achieved on the treatment apparatus, a movement speed of a portion of the treatment apparatus, an indication of a plurality of pain levels using the treatment apparatus, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, or some combination thereof. It may be desirable to process the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[0875] Further, another technical problem may involve distally treating, via a computing device during a telemedicine or telehealth session, a patient from a location different than a location at which the patient is located. An additional technical problem is controlling or enabling the control of, from the different location, a treatment apparatus used by the patient at the location at which the patient is located. Oftentimes, when a patient undergoes rehabilitative surgery (e.g., knee surgery), a physical therapist or other medical professional may prescribe a treatment apparatus to the patient to use to perform a treatment protocol at their residence or any mobile location or temporary domicile. A medical professional may refer to a doctor, physician assistant, nurse, chiropractor, dentist, physical therapist, acupuncturist, physical trainer, or the like. A medical professional may refer to any person with a credential, license, degree, or the like in the field of medicine, physical therapy, rehabilitation, or the like.

[0876] Since the physical therapist or other medical professional is located in a different location from the patient and the treatment apparatus, it may be technically challenging for the physical therapist or other medical professional to monitor the patient’s actual progress (as opposed to relying on the patient’s word about their progress) using the treatment apparatus, modify the treatment plan according to the patient’s progress, adapt the treatment apparatus to the personal characteristics of the patient as the patient performs the treatment plan, and the like.

[0877] Accordingly, some embodiments of the present disclosure pertain to using artificial intelligence and/or machine learning to assign patients to cohorts and to dynamically control a treatment apparatus based on the assignment during an adaptive telemedical session. In some embodiments, numerous treatment apparatuses may be provided to patients. The treatment apparatuses may be used by the patients to perform treatment plans in their residences, at a gym, at a rehabilitative center, at a hospital, or any suitable location, including permanent or temporary domiciles. In some embodiments, the treatment apparatuses may be communicatively coupled to a server. Characteristics of the patients may be collected before, during, and/or after the patients perform the treatment plans. For example, the personal information, the performance information, and the measurement information may be collected before, during, and/or after the person performs the treatment plans. The results (e.g., improved performance or decreased performance) of performing each exercise may be collected from the treatment apparatus throughout the treatment plan and after the treatment plan is performed. The parameters, settings, configurations, etc. (e.g., position of pedal, amount of resistance, etc.) of the treatment apparatus may be collected before, during, and/or after the treatment plan is performed.

[0878] Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be timestamped and may be correlated with a particular step in the treatment plan. Such a technique may enable determining which steps in the treatment plan lead to desired results (e.g., improved muscle strength, range of motion, etc.) and which steps lead to diminishing returns (e.g., continuing to exercise after 3 minutes actually delays or harms recovery).

[0879] Data may be collected from the treatment apparatuses and/or any suitable computing device (e.g., computing devices where personal information is entered, such as a clinician interface or patient interface) over time as the patients use the treatment apparatuses to perform the various treatment plans. The data that may be collected may include the characteristics of the patients, the treatment plans performed by the patients, and the results of the treatment plans.

[0880] In some embodiments, the data may be processed to group certain people into cohorts. The people may be grouped by people having certain or selected similar characteristics, treatment plans, and results of performing the treatment plans. For example, athletic people having no medical conditions who perform a treatment plan (e.g., use the treatment apparatus for 30 minutes a day 5 times a week for 3 weeks) and who fully recover may be grouped into a first cohort. Older people who are classified obese and who perform a treatment plan (e.g., use the treatment plan for 10 minutes a day 3 times a week for 4 weeks) and who improve their range of motion by 75 percent may be grouped into a second cohort.

[0881] In some embodiments, an artificial intelligence engine may include one or more machine learning models that are trained using the cohorts. For example, the one or more machine learning models may be trained to receive an input of characteristics of a new patient and to output a treatment plan for the patient that results in a desired result. The machine learning models may match a pattern between the characteristics of the new patient and at least one patient of the patients included in a particular cohort. When a pattern is matched, the machine learning models may assign the new patient to the particular cohort and select the treatment plan associated with the at least one patient. The artificial intelligence engine may be configured to control, distally and based on the treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan.

[0882] As may be appreciated, the characteristics of the new patient may change as the new patient uses the treatment apparatus to perform the treatment plan. For example, the performance of the patient may improve quicker than expected for people in the cohort to which the new patient is currently assigned. Accordingly, the machine learning models may be trained to dynamically reassign, based on the changed characteristics, the new patient to a different cohort that includes people having characteristics similar to the now -changed characteristics as the new patient. For example, a clinically obese patient may lose weight and no longer meet the weight criterion for the initial cohort, result in the patient’ s being reassigned to a different cohort with a different weight criterion. A different treatment plan may be selected for the new patient, and the treatment apparatus may be controlled, distally and based on the different treatment plan, the treatment apparatus while the new patient uses the treatment apparatus to perform the treatment plan. Such techniques may provide the technical solution of distally controlling a treatment apparatus. Further, the techniques may lead to faster recovery times and/or better results for the patients because the treatment plan that most accurately fits their characteristics is selected and implemented, in real-time, at any given moment. Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds but greater than 2 seconds. As described herein, the term “results” may refer to medical results or medical outcomes. Results and outcomes may refer to responses to medical actions. The outcomes may refer to achieving a certain stage or percentage of recovery, rehabilitation, or the like.

[0883] Depending on what result is desired, the artificial intelligence engine may be trained to output several treatment plans. For example, one result may include recovering to a threshold level (e.g., 75% range of motion) in a fastest amount of time, while another result may include fully recovering (e.g., 100% range of motion) regardless of the amount of time. The data obtained from the patients and sorted into cohorts may indicate that a first treatment plan provides the first result for people with characteristics similar to the patient’s, and that a second treatment plan provides the second result for people with characteristics similar to the patient.

[0884] Further, the artificial intelligence engine may also be trained to output treatment plans that are not optimal or sub-optimal or even inappropriate (all referred to, without limitation, as “excluded treatment plans”) for the patient. For example, if a patient has high blood pressure, a particular exercise may not be approved or suitable for the patient as it may put the patient at unnecessary risk or even induce a hypertensive crisis and, accordingly, that exercise may be flagged in the excluded treatment plan for the patient.

[0885] In some embodiments, the treatment plans and/or excluded treatment plans may be presented, during a telemedicine or telehealth session, to a medical professional. The medical professional may select a particular treatment plan for the patient to cause that treatment plan to be transmitted to the patient and/or to control, based on the treatment plan, the treatment apparatus. In some embodiments, to facilitate telehealth or telemedicine applications, including remote diagnoses, determination of treatment plans and rehabilitative and/or pharmacologic prescriptions, the artificial intelligence engine may receive and/or operate distally from the patient and the treatment apparatus. In such cases, the recommended treatment plans and/or excluded treatment plans may be presented simultaneously with a video of the patient in real-time or near real-time during a telemedicine or telehealth session on a user interface of a computing device of a medical professional. The video may also be accompanied by audio, text and other multimedia information. Real-time may refer to less than or equal to 2 seconds. Near real-time may refer to any interaction of a sufficiently short time to enable two individuals to engage in a dialogue via such user interface, and will generally be less than 10 seconds but greater than 2 seconds. [0886] Presenting the treatment plans generated by the artificial intelligence engine concurrently with a presentation of the patient video may provide an enhanced user interface because the medical professional may continue to visually and/or otherwise communicate with the patient while also reviewing the treatment plans on the same user interface. The enhanced user interface may improve the medical professional’s experience using the computing device and may encourage the medical professional to reuse the user interface. Such a technique may also reduce computing resources (e.g., processing, memory, network) because the medical professional does not have to switch to another user interface screen to enter a query for a treatment plan to recommend based on the characteristics of the patient. The artificial intelligence engine provides, dynamically on the fly, the treatment plans and excluded treatment plans.

[0887] Additionally, some embodiments of the present disclosure may relate to analytically optimizing telehealth practice-based billing processes and revenue while enabling regulatory compliance. Information of a patient’s condition may be received and the information may be used to determine the procedures (e.g., the procedures may include one or more office visits, bloodwork tests, other medical tests, surgeries, biopsies, performances of exercise or exercises, therapy sessions, physical therapy sessions, lab studies, consultations, or the like) to perform on the patient. Based on the information, a treatment plan may be generated for the patient. The treatment plan may include various instructions pertaining at least to the procedures to perform for the patient’s condition. There may be an optimal way to bill the procedures and costs associated with the billing. However, there may be a set of billing procedures associated with the set of instructions. The set of billing procedures may include a set of rules pertaining to billing codes, timing, constraints, or some combination thereof that govern the order in which the procedures are allowed to be billed and, further, which procedures are allowed to be billed or which portions of a given procedure are allowed to be billed. For example, regarding timing, a test may be allowed to be conducted before surgery but not after the surgery. In his example, it may be best for the patient to conduct the test before the surgery. Accordingly, the billing sequence may include a billing code for the test before a billing code for the surgery. The constraints may pertain to an insurance regime, a medical order, laws, regulations, or the like. Regarding the order, an example may include: if procedure A is performed, then procedure B may be billed, but procedure A cannot be billed if procedure B was billed first. It may not be a trivial task to optimize a billing sequence for a treatment plan while complying with the set of rules.

[0888] It is desirable to generate a billing sequence for the patient’s treatment plan that complies with the set of rules. In addition, there are multiples of parameters to consider for a desired billing sequence. The parameters may pertain to a monetary value amount generated by the billing sequence, a patient outcome that results from the treatment plan associated with the billing sequence, a fee paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof.

[0889] The artificial intelligence engine may be trained to generate, based on the set of billing procedures, one or more billing sequences for at least a portion of or all of the instructions, where the billing sequence is tailored according to one or more of the parameters. As such, the disclosed techniques may enable medical professionals to provide, improve or come closer to achieving best practices for ethical patient care. By complying with the set of billing procedures, the disclosed techniques provide for ethical consideration of the patient’s care, while also benefiting the practice of the medical professional and benefiting the interests of insurance providers. In other words, one key goal of the disclosed techniques is to maximize both patient care quality and the degree of reimbursement for the use of ethical medical practices related thereto.

[0890] The artificial intelligence engine may pattern match to generate billing sequences and/or treatment plans tailored for a selected parameter (e.g., best outcome for the patient, maximize monetary value amount generated, etc.). Different machine learning models may be trained to generate billing sequences and/or treatment plans for different parameters. In some embodiments, one trained machine learning model that generates a first billing sequence for a first parameter (e.g., monetary value amount generated) may be linked to and feed its output to another trained machine learning model that generates a second billing sequence for a second parameter (e.g., a plan of reimbursement). Thus, the second billing sequence may be tuned for both the first parameter and the second parameter. It should be understood that any suitable combination of trained machine learning models may be used to provide billing sequences and/or treatment plans tailored to any combination of the parameters described herein, as well as other parameters contemplated and/or used in billing sequences and/or treatment plans, whether or not specifically expressed or enumerated herein.

[0891] In some embodiments, a medical professional and an insurance company may participate to provide requests pertaining to the billing sequence. For example, the medical professional and the insurance company may request to receive immediate reimbursement for the treatment plan. Accordingly, the artificial intelligence engine may be trained to generate, based on the immediate reimbursement requests, a modified billing sequence that complies with the set of billing procedures and provides for immediate reimbursement to the medical professional and the insurance company.

[0892] In some embodiments, the treatment plan may be modified by a medical professional. For example, certain procedures may be added, modified or removed. In the telehealth scenario, there are certain procedures that may not be performed due to the distal nature of a medical professional using a computing device in a different physical location than a patient.

[0893] In some embodiments, the treatment plan and the billing sequence may be transmitted to a computing device of a medical professional, insurance provider, any lawfully designated or appointed entity and/or patient. It should be noted that there may be other entities that receive the treatment plan and the billing sequence for the insurance provider and/or the patient. Such entities may include any lawfully designated or appointed entity (e.g., assignees, legally predicated designees, attomeys-in-fact, legal proxies, etc.), Thus, as used herein, it should be understood that these entities may receive information in lieu of, in addition to the insurance provider and/or the patient, or as an intermediary or interlocutor between another such lawfully designated or appointed entity and the insurance provider and/or the patient. The treatment plan and the billing sequence may be presented in a first portion of a user interface on the computing device. A video of the patient or the medical professional may be optionally presented in a second portion of the user interface on the computing device. The first portion (including the treatment plan and the billing sequence) and the second portion (including the video) may be presented concurrently on the user interface to enable to the medical professional and/or the patient to view the video and the treatment plan and the billing sequence at the same time. Such a technique may be beneficial and reduce computing resources because the user (medical professional and/or patient) does not have to minimize the user interface (including the video) in order to open another user interface which includes the treatment plan and the billing sequence. [0894] In some embodiments, the medical professional and/or the patient may select a certain treatment plan and/or billing sequence from the user interface. Based on the selection, the treatment apparatus may be electronically controlled, either via the computing device of the patient transmitting a control signal to a controller of the treatment apparatus, or via the computing device of the medical professional transmitting a control signal to the controller of the treatment apparatus. As such, the treatment apparatus may initialize the treatment plan and configure various settings (e.g., position of pedals, speed of pedaling, amount of force required on pedals, etc.) defined by the treatment plan.

[0895] A potential technical problem may relate to the information pertaining to the patient’s medical condition being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). That is, some sources used by various medical professional entities may be installed on their local computing devices and, additionally and/or alternatively, may use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain, via interfaces exposed by APIs used by the sources, the formats used by the sources. In some embodiments, when information is received from the sources, the API may map and convert the format used by the sources to a standardized (i.e., canonical) format, language and/or encoding (“format” as used herein will be inclusive of all of these terms) used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when the artificial intelligence engine is performing any of the techniques disclosed herein. Using the information converted to a standardized format may enable a more accurate determination of the procedures to perform for the patient and/or a billing sequence to use for the patient.

[0896] To that end, the standardized information may enable generating treatment plans and/or billing sequences having a particular format that can be processed by various applications (e.g., telehealth). For example, applications, such as telehealth applications, may be executing on various computing devices of medical professionals and/or patients. The applications (e.g., standalone or web-based) may be provided by a server and may be configured to process data according to a format in which the treatment plans and the billing sequences are implemented. Accordingly, the disclosed embodiments may provide a technical solution by (i) receiving, from various sources (e.g., EMR systems), information in non-standardized and/or different formats; (ii) standardizing the information (i.e., representing the information in a canonical format); and (iii) generating, based on the standardized information, treatment plans and billing sequences having standardized formats capable of being processed by applications (e.g., telehealth applications) executing on computing devices of medical professionals and/or patients and/or their lawfully authorized designees.

[0897] Additionally, some embodiments of the present disclosure may use artificial intelligence and machine learning to create optimal patient treatment plans based on one or more of monetary value amount and patient outcomes. Optimizing for one or more of patient outcome and monetary value amount generated, while complying with a set of constraints, may be a computationally and technically challenging issue.

[0898] Accordingly, the disclosed techniques provide numerous technical solutions in embodiments that enable dynamically determining one or more optimal treatment plans optimized for various parameters (e.g., monetary value amount generated, patient outcome, risk, etc.). In some embodiments, while complying with the set of constraints, an artificial intelligence engine may use one or more trained machine learning models to generate the optimal treatment plans for various parameters. The set of constraints may pertain to billing codes associated with various treatment plans, laws, regulations, timings of billing, orders of billing, and the like. As described herein, one or more of the optimal treatment plans may be selected to control, based on the selected one or more treatment plans, the treatment apparatus in real-time or near real-time while a patient uses the treatment apparatus in a telehealth or telemedicine session.

[0899] One of the parameters may include maximizing an amount of monetary value amount generated. Accordingly, in one embodiment, the artificial intelligence engine may receive information pertaining to a medical condition of the patient. Based on the information, the artificial intelligence engine may receive a set of treatment plans that, when applied to other patients having similar medical condition information, cause outcomes to be achieved by the patients. The artificial intelligence engine may receive a set of monetary value amounts associated with the set of treatment plans. A respective monetary value amount may be associated with a respective treatment plan. The artificial intelligence engine may receive the set of constraints. The artificial intelligence engine may generate optimal treatment plans for a patient, where the generating is based on one or more of the set of treatment plans, the set of monetary value amounts, and the set of constraints. Each of the optimal treatment plans complies completely or to the maximum extent possible or to a prescribed extent with the set of constraints and represents a patient outcome and an associated monetary value amount generated. The optimal treatment plans may be transmitted, in real-time or near real-time, during a telehealth or telemedicine session, to be presented on one or more computing devices of one or more medical professionals and/or one or more patients. It should be noted that the term “telehealth” as used herein will be inclusive of all of the following terms : telemedicine, teletherapeutic, telerehab, etc. It should be noted that the term “telemedicine” as used herein will be inclusive of all of the following terms: telehealth, teletherapeutic, telerehab, etc.

[0900] A user may select different monetary value amounts, and the artificial intelligence engine may generate different optimal treatment plans for those monetary value amounts. The different optimal treatment plans may represent different patient outcomes and may also comply with the set of constraints. The different optimal treatment plans may be transmitted, in real-time or near real-time, during a telehealth or telemedicine session, to be presented on a computing device of a medical professional and/or a patient.

[0901] The disclosed techniques may use one or more equations having certain parameters on a left side of the equation and certain parameters on a right side of the equation. For example, the parameters on the left side of the equation may represent a treatment plan, patient outcome, risk, and/or monetary value amount generated. The parameters on the right side of the equation may represent the set of constraints that must be complied with to ethically and/or legally bill for the treatment plan. Such an equation or equations and/or one or more parameters therein may also, without limitation, incorporate or implement appropriate mathematical, statistical and/or probabilistic algorithms as well as use computer-based subroutines, methods, operations, function calls, scripts, services, applications or programs to receive certain values and to return other values and/or results. The various parameters may be considered levers that may be adjusted to provide a desired treatment plan and/or monetary value amount generated. In some instances, it may be desirable to select an optimal treatment plan that is tailored for a desired patient outcome (e.g., best recovery, fastest recovery rate, etc.), which may effect the monetary value amount generated and the risk associated with the treatment plan. In other instances, it may be desirable to select an optimal treatment plan tailored for a desired monetary value amount generated, which may effect the treatment plan and/or the risk associated with the treatment plan.

[0902] For example, a first treatment plan may result in a first patient outcome having a low risk and resulting in a low monetary value amount generated, whereas a second treatment plan may result in a second patient outcome (better than the first patient outcome) having a higher risk and resulting in a higher monetary value amount generated than the first treatment plan. Both the first treatment plan and the second treatment plan are generated based on the set of constraints. Also, both the first treatment plan and the second treatment plan may be simultaneously presented, in real-time or near real-time, on a user interface of one or more computing devices engaged in a telehealth or telemedicine session. A user (e.g., medical professional or patient) may select either the first or second treatment plan to cause the selected treatment plan to be implemented on the treatment apparatus. In other words, the treatment apparatus may be electronically controlled based on the selected treatment plan.

[0903] Accordingly, the artificial intelligence engine may use various machine learning models, each trained to generate one or more optimal treatment plans for a different parameter, as described further below. Each of the one or more optimal treatment plans complies with the set of constraints.

[0904] The various embodiments disclosed herein may provide a technical solution to the technical problem pertaining to the patient’s medical condition information being received in disparate formats. For example, a server may receive the information pertaining to a medical condition of the patient from one or more sources (e.g., from an electronic medical record (EMR) system, application programming interface (API), or any suitable system that has information pertaining to the medical condition of the patient). The information may be converted from the format used by the sources to the standardized format used by the artificial intelligence engine. Further, the information converted to the standardized format used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when performing any of the techniques disclosed herein. The standardized information may enable generating optimal treatment plans, where the generating is based on treatment plans associated with the standardized information, monetary value amounts, and the set of constraints. The optimal treatment plans may be provided in a standardized format that can be processed by various applications (e.g., telehealth) executing on various computing devices of medical professionals and/or patients.

[0905] In some embodiments, the treatment apparatus may be adaptive and/or personalized because its properties, configurations, and positions may be adapted to the needs of a particular patient. For example, the pedals may be dynamically adjusted on the fly (e.g., via a telemedicine session or based on programmed configurations in response to certain measurements being detected) to increase or decrease a range of motion to comply with a treatment plan designed for the user. In some embodiments, a medical professional may adapt, remotely during a telemedicine session, the treatment apparatus to the needs of the patient by causing a control instruction to be transmitted from a server to treatment apparatus. Such adaptive nature may improve the results of recovery for a patient, furthering the goals of personalized medicine, and enabling personalization of the treatment plan on a per-individual basis.

[0906] FIG. 43 shows a block diagram of a computer-implemented system 5010, hereinafter called “the system” for managing a treatment plan. Managing the treatment plan may include using an artificial intelligence engine to recommend treatment plans and/or provide excluded treatment plans that should not be recommended to a patient.

[0907] The system 5010 also includes a server 5030 configured to store and to provide data related to managing the treatment plan. The server 5030 may include one or more computers and may take the form of a distributed and/or virtualized computer or computers. The server 5030 also includes a first communication interface 5032 configured to communicate with the clinician interface 5020 via a first network 5034.1n some embodiments, the first network 5034 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. The server 5030 includes a first processor 5036 and a first machine-readable storage memory 5038, which may be called a “memory” for short, holding first instructions 5040 for performing the various actions of the server 5030 for execution by the first processor 5036. The server 5030 is configured to store data regarding the treatment plan. For example, the memory 5038 includes a system data store 5042 configured to hold system data, such as data pertaining to treatment plans for treating one or more patients.

[0908] The system data store 5042 may be configured to hold data relating to billing procedures, including rules and constraints pertaining to billing codes, order, timing, insurance regimes, laws, regulations, or some combination thereof. The system data store 5042 may be configured to store various billing sequences generated based on billing procedures and various parameters (e.g., monetary value amount generated, patient outcome, plan of reimbursement, fees, a payment plan for patients to pay of an amount of money owed, an amount of revenue to be paid to an insurance provider, etc.). The system data store 5042 may be configured to store optimal treatment plans generated based on various treatment plans for users having similar medical conditions, monetary value amounts generated by the treatment plans, and the constraints. Any of the data stored in the system data store 5042 may be accessed by an artificial intelligence engine 5011 when performing any of the techniques described herein.

[0909] The server 5030 is also configured to store data regarding performance by a patient in following a treatment plan. For example, the memory 5038 includes a patient data store 5044 configured to hold patient data, such as data pertaining to the one or more patients, including data representing each patient’ s performance within the treatment plan.

[0910] In addition, the characteristics (e.g., personal, performance, measurement, etc.) of the people, the treatment plans followed by the people, the level of compliance with the treatment plans, and the results of the treatment plans may use correlations and other statistical or probabilistic measures to enable the partitioning of or to partition the treatment plans into different patient cohort-equivalent databases in the patient data store 5044. For example, the data for a first cohort of first patients having a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment plan followed by the first patient, and a first result of the treatment plan may be stored in a first patient database. The data for a second cohort of second patients having a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment plan followed by the second patient, and a second result of the treatment plan may be stored in a second patient database. Any single characteristic or any combination of characteristics may be used to separate the cohorts of patients. In some embodiments, the different cohorts of patients may be stored in different partitions or volumes of the same database. There is no specific limit to the number of different cohorts of patients allowed, other than as limited by mathematical combinatoric and/or partition theory. [0911] This characteristic data, treatment plan data, and results data may be obtained from numerous treatment apparatuses and/or computing devices over time and stored in the database 5044. The characteristic data, treatment plan data, and results data may be correlated in the patient-cohort databases in the patient data store 5044. The characteristics of the people may include personal information, performance information, and/or measurement information.

[0912] In addition to the historical information about other people stored in the patient cohort-equivalent databases, real-time or near-real-time information based on the current patient’s characteristics about a current patient being treated may be stored in an appropriate patient cohort-equivalent database. The characteristics of the patient may be determined to match or be similar to the characteristics of another person in a particular cohort (e.g., cohort A) and the patient may be assigned to that cohort.

[0913] In some embodiments, the server 5030 may execute the artificial intelligence (AI) engine 5011 that uses one or more machine learning models 5013 to perform at least one of the embodiments disclosed herein. The server 5030 may include a training engine 5009 capable of generating the one or more machine learning models 5013. The machine learning models 5013 may be trained to assign people to certain cohorts based on their characteristics, select treatment plans using real-time and historical data correlations involving patient cohort-equivalents, and control a treatment apparatus 5070, among other things. The machine learning models 5013 may be trained to generate, based on billing procedures, billing sequences and/or treatment plans tailored for various parameters (e.g., a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof). The machine learning models 5013 may be trained to generate, based on constraints, optimal treatment plans tailored for various parameters (e.g., monetary value amount generated, patient outcome, risk, etc.). The one or more machine learning models 5013 may be generated by the training engine 5009 and may be implemented in computer instructions executable by one or more processing devices of the training engine 5009 and/or the servers 5030. To generate the one or more machine learning models 5013, the training engine 5009 may train the one or more machine learning models 5013. The one or more machine learning models 5013 may be used by the artificial intelligence engine 5011.

[0914] The training engine 5009 may be a rackmount server, a router computer, a personal computer, a portable digital assistant, a smartphone, a laptop computer, a tablet computer, a netbook, a desktop computer, an Internet of Things (IoT) device, any other desired computing device, or any combination of the above. The training engine 5009 may be cloud-based or a real-time software platform, and it may include privacy software or protocols, and/or security software or protocols.

[0915] To train the one or more machine learning models 5013, the training engine 5009 may use a training data set of a corpus of the information (e.g., characteristics, medical diagnosis codes, etc.) pertaining to medical conditions of the people who used the treatment apparatus 5070 to perform treatment plans, the details (e.g., treatment protocol including exercises, amount of time to perform the exercises, instructions for the patient to follow, how often to perform the exercises, a schedule of exercises, parameters/configurations/settings of the treatment apparatus 5070 throughout each step of the treatment plan, etc.) of the treatment plans performed by the people using the treatment apparatus 5070, the results of the treatment plans performed by the people, a set of monetary value amounts associated with the treatment plans, a set of constraints (e.g., rules pertaining to billing codes associated with the set of treatment plans, laws, regulations, etc.), a set of billing procedures (e.g., rules pertaining to billing codes, order, timing and constraints) associated with treatment plan instructions, a set of parameters (e.g., a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof, a treatment plan, a monetary value amount generated, a risk, etc.), insurance regimens, etc. [0916] The one or more machine learning models 5013 may be trained to match patterns of characteristics of a patient with characteristics of other people in assigned to a particular cohort. The term “match” may refer to an exact match, a correlative match, a substantial match, etc. The one or more machine learning models 5013 may be trained to receive the characteristics of a patient as input, map the characteristics to characteristics of people assigned to a cohort, and select a treatment plan from that cohort. The one or more machine learning models 5013 may also be trained to control, based on the treatment plan, the machine learning apparatus 5070. [0917] The one or more machine learning models 5013 may be trained to match patterns of a first set of parameters (e.g., treatment plans for patients having a medical condition, a set of monetary value amounts associated with the treatment plans, patient outcome, and/or a set of constraints) with a second set of parameters associated with an optimal treatment plan. The one or more machine learning models 5013 may be trained to receive the first set of parameters as input, map the characteristics to the second set of parameters associated with the optimal treatment plan, and select the optimal treatment plan a treatment plan. The one or more machine learning models 5013 may also be trained to control, based on the treatment plan, the machine learning apparatus 5070.

[0918] The one or more machine learning models 5013 may be trained to match patterns of a first set of parameters (e.g., information pertaining to a medical condition, treatment plans for patients having a medical condition, a set of monetary value amounts associated with the treatment plans, patient outcomes, instructions for the patient to follow in a treatment plan, a set of billing procedures associated with the instructions, and/or a set of constraints) with a second set of parameters associated with a billing sequence and/or optimal treatment plan. The one or more machine learning models 5013 may be trained to receive the first set of parameters as input, map or otherwise associate or algorithmically associate the first set of parameters to the second set of parameters associated with the billing sequence and/or optimal treatment plan, and select the billing sequence and/or optimal treatment plan for the patient. In some embodiments, one or more optimal treatment plans may be selected to be provided to a computing device of the medical professional and/or the patient. The one or more machine learning models 5013 may also be trained to control, based on the treatment plan, the machine learning apparatus 5070.

[0919] Different machine learning models 5013 may be trained to recommend different treatment plans tailored for different parameters. For example, one machine learning model may be trained to recommend treatment plans for a maximum monetary value amount generated, while another machine learning model may be trained to recommend treatment plans based on patient outcome, or based on any combination of monetary value amount and patient outcome, or based on those and/or additional goals. Also, different machine learning models 5013 may be trained to recommend different billing sequences tailored for different parameters. For example, one machine learning model may be trained to recommend billing sequences for a maximum fee to be paid to a medical professional, while another machine learning model may be trained to recommend billing sequences based on a plan of reimbursement. [0920] Using training data that includes training inputs and corresponding target outputs, the one or more machine learning models 5013 may refer to model artifacts created by the training engine 5009. The training engine 5009 may find patterns in the training data wherein such patterns map the training input to the target output, and generate the machine learning models 5013 that capture these patterns. In some embodiments, the artificial intelligence engine 5011, the database 5033, and/or the training engine 5009 may reside on another component (e.g., assistant interface 5094, clinician interface 5020, etc.) depicted in FIG. 43.

[0921] The one or more machine learning models 5013 may comprise, e.g., a single level of linear or non linear operations (e.g., a support vector machine [SVM]) or the machine learning models 5013 may be a deep network, i.e., a machine learning model comprising multiple levels of non-linear operations. Examples of deep networks are neural networks including generative adversarial networks, convolutional neural networks, recurrent neural networks with one or more hidden layers, and fully connected neural networks (e.g., each neuron may transmit its output signal to the input of the remaining neurons, as well as to itself). For example, the machine learning model may include numerous layers and/or hidden layers that perform calculations (e.g., dot products) using various neurons.

[0922] The system 5010 also includes a patient interface 5050 configured to communicate information to a patient and to receive feedback from the patient. Specifically, the patient interface includes an input device 5052 and an output device 5054, which may be collectively called a patient user interface 5052, 5054. The input device 5052 may include one or more devices, such as a keyboard, a mouse, a touch screen input, a gesture sensor, and/or a microphone and processor configured for voice recognition. The output device 5054 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, smartphone, or a smart watch. The output device 5054 may include other hardware and/or software components such as a projector, virtual reality capability, augmented reality capability, etc. The output device 5054 may incorporate various different visual, audio, or other presentation technologies. For example, the output device 5054 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, and/or melodies, which may signal different conditions and/or directions. The output device 5054 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the patient. The output device 5054 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

[0923] In some embodiments, the output device 5054 may present a user interface that may present a recommended treatment plan, billing sequence, or the like to the patient. The user interface may include one or more graphical elements that enable the user to select which treatment plan to perform. Responsive to receiving a selection of a graphical element (e.g., “Start” button) associated with a treatment plan via the input device 5054, the patient interface 5050 may communicate a control signal to the controller 5072 of the treatment apparatus, wherein the control signal causes the treatment apparatus 5070 to begin execution of the selected treatment plan. As described below, the control signal may control, based on the selected treatment plan, the treatment apparatus 5070 by causing actuation of the actuator 5078 (e.g., cause a motor to drive rotation of pedals of the treatment apparatus at a certain speed), causing measurements to be obtained via the sensor 5076, or the like. The patient interface 5050 may communicate, via a local communication interface 5068, the control signal to the treatment apparatus 5070. [0924] As shown in FIG. 43, the patient interface 5050 includes a second communication interface 5056, which may also be called a remote communication interface configured to communicate with the server 5030 and/or the clinician interface 5020 via a second network 5058. In some embodiments, the second network 5058 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the second network 58 may include the Internet, and communications between the patient interface 5050 and the server 5030 and/or the clinician interface 5020 may be secured via encryption, such as, for example, by using a virtual private network (VPN). In some embodiments, the second network 5058 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. In some embodiments, the second network 58 may be the same as and/or operationally coupled to the first network 5034.

[0925] The patient interface 5050 includes a second processor 5060 and a second machine -readable storage memory 5062 holding second instructions 64 for execution by the second processor 5060 for performing various actions of patient interface 5050. The second machine-readable storage memory 5062 also includes a local data store 5066 configured to hold data, such as data pertaining to a treatment plan and/or patient data, such as data representing a patient’s performance within a treatment plan. The patient interface 5050 also includes a local communication interface 5068 configured to communicate with various devices for use by the patient in the vicinity of the patient interface 5050. The local communication interface 5068 may include wired and/or wireless communications. In some embodiments, the local communication interface 5068 may include a local wireless network such as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC), cellular data network, etc. [0926] The system 5010 also includes a treatment apparatus 5070 configured to be manipulated by the patient and/or to manipulate a body part of the patient for performing activities according to the treatment plan. In some embodiments, the treatment apparatus 5070 may take the form of an exercise and rehabilitation apparatus configured to perform and/or to aid in the performance of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, and the treatment includes rehabilitation of a body part of the patient, such as a joint or a bone or a muscle group. The treatment apparatus 5070 may be any suitable medical, rehabilitative, therapeutic, etc. apparatus configured to be controlled distally via another computing device to treat a patient and/or exercise the patient. The treatment apparatus 5070 may be an electromechanical machine including one or more weights, an electromechanical bicycle, an electromechanical spin-wheel, a smart-mirror, a treadmill, or the like. The body part may include, for example, a spine, a hand, a foot, a knee, or a shoulder. The body part may include a part of a joint, a bone, or a muscle group, such as one or more vertebrae, a tendon, or a ligament. As shown in FIG. 42, the treatment apparatus 5070 includes a controller 5072, which may include one or more processors, computer memory, and/or other components. The treatment apparatus 5070 also includes a fourth communication interface 5074 configured to communicate with the patient interface 5050 via the local communication interface 5068. The treatment apparatus 5070 also includes one or more internal sensors 5076 and an actuator 5078, such as a motor. The actuator 5078 may be used, for example, for moving the patient’s body part and/or for resisting forces by the patient.

[0927] The internal sensors 5076 may measure one or more operating characteristics of the treatment apparatus 5070 such as, for example, a force a position, a speed, and /or a velocity. In some embodiments, the internal sensors 5076 may include a position sensor configured to measure at least one of a linear motion or an angular motion of a body part of the patient. For example, an internal sensor 5076 in the form of a position sensor may measure a distance that the patient is able to move a part of the treatment apparatus 5070, where such distance may correspond to a range of motion that the patient’s body part is able to achieve. In some embodiments, the internal sensors 5076 may include a force sensor configured to measure a force applied by the patient. For example, an internal sensor 5076 in the form of a force sensor may measure a force or weight the patient is able to apply, using a particular body part, to the treatment apparatus 5070.

[0928] The system 5010 shown in FIG. 43 also includes an ambulation sensor 5082, which communicates with the server 5030 via the local communication interface 5068 of the patient interface 5050. The ambulation sensor 5082 may track and store a number of steps taken by the patient. In some embodiments, the ambulation sensor 5082 may take the form of a wristband, wristwatch, or smart watch. In some embodiments, the ambulation sensor 5082 may be integrated within a phone, such as a smartphone.

[0929] The system 5010 shown in FIG. 43 also includes a goniometer 5084, which communicates with the server 5030 via the local communication interface 5068 of the patient interface 5050. The goniometer 5084 measures an angle of the patient’s body part. For example, the goniometer 5084 may measure the angle of flex of a patient’s knee or elbow or shoulder.

[0930] The system 5010 shown in FIG. 43 also includes a pressure sensor 5086, which communicates with the server 5030 via the local communication interface 5068 of the patient interface 5050. The pressure sensor 5086 measures an amount of pressure or weight applied by a body part of the patient. For example, pressure sensor 5086 may measure an amount of force applied by a patient’s foot when pedaling a stationary bike. [0931] The system 5010 shown in FIG. 43 also includes a supervisory interface 5090 which may be similar or identical to the clinician interface 5020. In some embodiments, the supervisory interface 5090 may have enhanced functionality beyond what is provided on the clinician interface 5020. The supervisory interface 5090 may be configured for use by a person having responsibility for the treatment plan, such as an orthopedic surgeon.

[0932] The system 5010 shown in FIG. 43 also includes a reporting interface 5092 which may be similar or identical to the clinician interface 5020. In some embodiments, the reporting interface 5092 may have less functionality from what is provided on the clinician interface 5020. For example, the reporting interface 5092 may not have the ability to modify a treatment plan. Such a reporting interface 5092 may be used, for example, by a biller to determine the use of the system 5010 for billing purposes. In another example, the reporting interface 5092 may not have the ability to display patient identifiable information, presenting only pseudonymized data and/or anonymized data for certain data fields concerning a data subject and/or for certain data fields concerning a quasi-identifier of the data subject. Such a reporting interface 5092 may be used, for example, by a researcher to determine various effects of a treatment plan on different patients.

[0933] The system 5010 includes an assistant interface 5094 for an assistant, such as a doctor, a nurse, a physical therapist, or a technician, to remotely communicate with the patient interface 5050 and/or the treatment apparatus 5070. Such remote communications may enable the assistant to provide assistance or guidance to a patient using the system 5010. More specifically, the assistant interface 5094 is configured to communicate a telemedicine signal 5096, 5097, 5098a, 5098b, 5099a, 5099b with the patient interface 5050 via a network connection such as, for example, via the first network 5034 and/or the second network 5058. The telemedicine signal 5096, 5097, 5098a, 5098b, 5099a, 5099b comprises one of an audio signal 5096, an audiovisual signal 5097, an interface control signal 5098a for controlling a function of the patient interface 5050, an interface monitor signal 5098b for monitoring a status of the patient interface 5050, an apparatus control signal 5099a for changing an operating parameter of the treatment apparatus 5070, and/or an apparatus monitor signal 5099b for monitoring a status of the treatment apparatus 5070. In some embodiments, each of the control signals 5098a, 5099a may be unidirectional, conveying commands from the assistant interface 5094 to the patient interface 50. In some embodiments, in response to successfully receiving a control signal 5098a, 5099a and/or to communicate successful and/or unsuccessful implementation of the requested control action, an acknowledgement message may be sent from the patient interface 5050 to the assistant interface 5094. In some embodiments, each of the monitor signals 5098b, 5099b may be unidirectional, status-information commands from the patient interface 5050 to the assistant interface 5094. In some embodiments, an acknowledgement message may be sent from the assistant interface 5094 to the patient interface 5050 in response to successfully receiving one of the monitor signals 5098b, 5099b.

[0934] In some embodiments, the patient interface 5050 may be configured as a pass-through for the apparatus control signals 5099a and the apparatus monitor signals 5099b between the treatment apparatus 5070 and one or more other devices, such as the assistant interface 5094 and/or the server 5030. For example, the patient interface 5050 may be configured to transmit an apparatus control signal 5099a in response to an apparatus control signal 5099a within the telemedicine signal 5096, 5097, 5098a, 5098b, 5099a, 5099b from the assistant interface 5094.

[0935] In some embodiments, the assistant interface 5094 may be presented on a shared physical device as the clinician interface 5020. For example, the clinician interface 5020 may include one or more screens that implement the assistant interface 5094. Alternatively or additionally, the clinician interface 5020 may include additional hardware components, such as a video camera, a speaker, and/or a microphone, to implement aspects of the assistant interface 5094.

[0936] In some embodiments, one or more portions of the telemedicine signal 5096, 5097, 5098a, 5098b, 5099a, 5099b may be generated from a prerecorded source (e.g., an audio recording, a video recording, or an animation) for presentation by the output device 5054 of the patient interface 5050. For example, a tutorial video may be streamed from the server 5030 and presented upon the patient interface 5050. Content from the prerecorded source may be requested by the patient via the patient interface 5050. Alternatively, via a control on the assistant interface 5094, the assistant may cause content from the prerecorded source to be played on the patient interface 5050.

[0937] The assistant interface 5094 includes an assistant input device 5022 and an assistant display 5024, which may be collectively called an assistant user interface 5022, 5024. The assistant input device 5022 may include one or more of a telephone, a keyboard, a mouse, a trackpad, or a touch screen, for example. Alternatively or additionally, the assistant input device 5022 may include one or more microphones. In some embodiments, the one or more microphones may take the form of a telephone handset, headset, or wide-area microphone or microphones configured for the assistant to speak to a patient via the patient interface 5050. In some embodiments, assistant input device 5022 may be configured to provide voice-based functionalities, with hardware and/or software configured to interpret spoken instructions by the assistant by using the one or more microphones. The assistant input device 5022 may include functionality provided by or similar to existing voice- based assistants such as Siii by Apple, Alexaby Amazon, Google Assistant, or Bixby by Samsung. The assistant input device 5022 may include other hardware and/or software components. The assistant input device 5022 may include one or more general purpose devices and/or special-purpose devices.

[0938] The assistant display 5024 may take one or more different forms including, for example, a computer monitor or display screen on a tablet, a smartphone, or a smart watch. The assistant display 24 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities, etc. The assistant display 5024 may incorporate various different visual, audio, or other presentation technologies. For example, the assistant display 5024 may include a non-visual display, such as an audio signal, which may include spoken language and/or other sounds such as tones, chimes, melodies, and/or compositions, which may signal different conditions and/or directions. The assistant display 5024 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the assistant. The assistant display 5024 may include graphics, which may be presented by a web-based interface and/or by a computer program or application (App.).

[0939] In some embodiments, the system 5010 may provide computer translation of language from the assistant interface 5094 to the patient interface 5050 and/or vice-versa. The computer translation of language may include computer translation of spoken language and/or computer translation of text. Additionally or alternatively, the system 5010 may provide voice recognition and/or spoken pronunciation of text. For example, the system 5010 may convert spoken words to printed text and/or the system 5010 may audibly speak language from printed text. The system 5010 may be configured to recognize spoken words by any or all of the patient, the clinician, and/or the assistant. In some embodiments, the system 5010 may be configured to recognize and react to spoken requests or commands by the patient. For example, the system 5010 may automatically initiate a telemedicine session in response to a verbal command by the patient (which may be given in any one of several different languages).

[0940] In some embodiments, the server 5030 may generate aspects of the assistant display 5024 for presentation by the assistant interface 5094. For example, the server 5030 may include a web server configured to generate the display screens for presentation upon the assistant display 5024. For example, the artificial intelligence engine 5011 may generate treatment plans, billing sequences, and/or excluded treatment plans for patients and generate the display screens including those treatment plans, billing sequences, and/or excluded treatment plans for presentation on the assistant display 5024 of the assistant interface 5094. In some embodiments, the assistant display 5024 may be configured to present a virtualized desktop hosted by the server 5030. In some embodiments, the server 5030 may be configured to communicate with the assistant interface 5094 via the first network 5034. In some embodiments, the first network 5034 may include a local area network (LAN), such as an Ethernet network. In some embodiments, the first network 5034 may include the Internet, and communications between the server 5030 and the assistant interface 5094 may be seemed via privacy enhancing technologies, such as, for example, by using encryption over a virtual private network (VPN). Alternatively or additionally, the server 5030 may be configured to communicate with the assistant interface 5094 via one or more networks independent of the first network 5034 and/or other communication means, such as a direct wired or wireless communication channel. In some embodiments, the patient interface 5050 and the treatment apparatus 5070 may each operate from a patient location geographically separate from a location of the assistant interface 5094. For example, the patient interface 5050 and the treatment apparatus 5070 may be used as part of an in-home rehabilitation system, which may be aided remotely by using the assistant interface 5094 at a centralized location, such as a clinic or a call center.

[0941] In some embodiments, the assistant interface 5094 may be one of several different terminals (e.g., computing devices) that may be grouped together, for example, in one or more call centers or at one or more clinicians’ offices. In some embodiments, a plurality of assistant interfaces 5094 may be distributed geographically. In some embodiments, a person may work as an assistant remotely from any conventional office infrastructure. Such remote work may be performed, for example, where the assistant interface 5094 takes the form of a computer and/or telephone. This remote work functionality may allow for work-from-home arrangements that may include part time and/or flexible work hours for an assistant.

[0942] FIGS. 44-45 show an embodiment of a treatment apparatus 5070. More specifically, FIG. 44 shows a treatment apparatus 5070 in the form of a stationary cycling machine 5100, which may be called a stationary bike, for short. The stationary cycling machine 5100 includes a set of pedals 5102 each attached to a pedal arm 5104 for rotation about an axle 5106. In some embodiments, and as shown in FIG. 44, the pedals 5102 are movable on the pedal arms 5104 in order to adjust a range of motion used by the patient in pedaling. For example, the pedals being located inwardly toward the axle 5106 corresponds to a smaller range of motion than when the pedals are located outwardly away from the axle 5106. A pressure sensor 5086 is attached to or embedded within one of the pedals 5102 for measuring an amount of force applied by the patient on the pedal 5102. The pressure sensor 5086 may communicate wirelessly to the treatment apparatus 5070 and/or to the patient interface 5050. [0943] FIG. 46 shows a person (a patient) using the treatment apparatus of FIG. 44, and showing sensors and various data parameters connected to a patient interface 5050. The example patient interface 5050 is a tablet computer or smartphone, or a phablet, such as an iPad, an iPhone, an Android device, or a Surface tablet, which is held manually by the patient. In some other embodiments, the patient interface 5050 may be embedded within or attached to the treatment apparatus 5070. FIG. 46 shows the patient wearing the ambulation sensor 5082 on his wrist, with a note showing “STEPS TODAY 1355”, indicating that the ambulation sensor 5082 has recorded and transmitted that step count to the patient interface 5050. FIG. 46 also shows the patient wearing the goniometer 5084 on his right knee, with a note showing “KNEE ANGLE 72°”, indicating that the goniometer 5084 is measuring and transmitting that knee angle to the patient interface 5050. FIG. 46 also shows a right side of one of the pedals 5102 with a pressure sensor 5086 showing “FORCE 12.5 lbs.,” indicating that the right pedal pressure sensor 5086 is measuring and transmitting that force measurement to the patient interface 5050. FIG. 46 also shows a left side of one of the pedals 5102 with a pressure sensor 5086 showing “FORCE 27 lbs.”, indicating that the left pedal pressure sensor 5086 is measuring and transmitting that force measurement to the patient interface 5050. FIG. 46 also shows other patient data, such as an indicator of “SESSION TIME 0:04: 13”, indicating that the patient has been using the treatment apparatus 5070 for 4 minutes and 13 seconds. This session time may be determined by the patient interface 5050 based on information received from the treatment apparatus 5070. FIG. 46 also shows an indicator showing “PAIN LEVEL 3”. Such a pain level may be obtained from the patent in response to a solicitation, such as a question, presented upon the patient interface 5050. [0944] FIG. 47 is an example embodiment of an overview display 5120 of the assistant interface 5094. Specifically, the overview display 5120 presents several different controls and interfaces for the assistant to remotely assist a patient with using the patient interface 5050 and/or the treatment apparatus 5070. This remote assistance functionality may also be called telemedicine or telehealth. [0945] Specifically, the overview display 5120 includes a patient profile display 5130 presenting biographical information regarding a patient using the treatment apparatus 5070. The patient profile display 5130 may take the form of a portion or region of the overview display 5120, as shown in FIG. 47, although the patient profile display 5130 may take other forms, such as a separate screen or a popup window. In some embodiments, the patient profile display 5130 may include a limited subset of the patient’s biographical information. More specifically, the data presented upon the patient profile display 5130 may depend upon the assistant’s need for that information. For example, a medical professional that is assisting the patient with a medical issue may be provided with medical history information regarding the patient, whereas a technician troubleshooting an issue with the treatment apparatus 5070 may be provided with a much more limited set of information regarding the patient. The technician, for example, may be given only the patient’s name. The patient profile display 5130 may include pseudonymized data and/or anonymized data or use any privacy enhancing technology to prevent confidential patient data from being communicated in a way that could violate patient confidentiality requirements. Such privacy enhancing technologies may enable compliance with laws, regulations, or other rules of governance such as, but not limited to, the Health Insurance Portability and Accountability Act (HIPAA), or the General Data Protection Regulation (GDPR), wherein the patient may be deemed a “data subject”.

[0946] In some embodiments, the patient profile display 5130 may present information regarding the treatment plan for the patient to follow in using the treatment apparatus 5070. Such treatment plan information may be limited to an assistant who is a medical professional, such as a doctor or physical therapist. For example, a medical professional assisting the patient with an issue regarding the treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting an issue with the treatment apparatus 5070 may not be provided with any information regarding the patient’s treatment plan.

[0947] In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented in the patient profile display 5130 to the assistant. The one or more recommended treatment plans and/or excluded treatment plans may be generated by the artificial intelligence engine 5011 of the server 5030 and received from the server 5030 in real-time during, inter alia, a telemedicine or telehealth session. An example of presenting the one or more recommended treatment plans and/or ruled-out treatment plans is described below with reference to FIG. 49.

[0948] In some embodiments, one or more treatment plans and/or billing sequences associated with the treatment plans may be presented in the patient profile display 5130 to the assistant. The one or more treatment plans and/or billing sequences associated with the treatment plans may be generated by the artificial intelligence engine 5011 of the server 5030 and received from the server 5030 in real-time during, inter alia, a telehealth session. An example of presenting the one or more treatment plans and/or billing sequences associated with the treatment plans is described below with reference to FIG. 51.

[0949] In some embodiments, one or more treatment plans and associated monetary value amounts generated, patient outcomes, and risks associated with the treatment plans may be presented in the patient profile display 5130 to the assistant. The one or more treatment plans and associated monetary value amounts generated, patient outcomes, and risks associated with the treatment plans may be generated by the artificial intelligence engine 5011 of the server 5030 and received from the server 5030 in real-time during, inter alia, a telehealth session. An example of presenting the one or more treatment plans and associated monetary value amounts generated, patient outcomes, and risks associated with the treatment plans is described below with reference to FIG. 53. [0950] The example overview display 5120 shown in FIG. 47 also includes a patient status display 5134 presenting status information regarding a patient using the treatment apparatus. The patient status display 5134 may take the form of a portion or region of the overview display 5120, as shown in FIG. 47, although the patient status display 5134 may take other forms, such as a separate screen or a popup window. The patient status display 5134 includes sensor data 5136 from one or more of the external sensors 5082, 5084, 5086, and/or from one or more internal sensors 5076 of the treatment apparatus 5070. In some embodiments, the patient status display 5134 may present other data 5138 regarding the patient, such as last reported pain level, or progress within a treatment plan.

[0951] User access controls may be used to limit access, including what data is available to be viewed and/or modified, on any or all of the user interfaces 5020, 5050, 5090, 5092, 5094 of the system 5010. In some embodiments, user access controls may be employed to control what information is available to any given person using the system 5010. For example, data presented on the assistant interface 5094 may be controlled by user access controls, with permissions set depending on the assistant/user’s need for and/or qualifications to view that information.

[0952] The example overview display 5120 shown in FIG. 47 also includes a help data display 5140 presenting information for the assistant to use in assisting the patient. The help data display 5140 may take the form of a portion or region of the overview display 5120, as shown in FIG. 47. The help data display 5140 may take other forms, such as a separate screen or a popup window. The help data display 5140 may include, for example, presenting answers to frequently asked questions regarding use of the patient interface 5050 and/or the treatment apparatus 5070. The help data display 5140 may also include research data or best practices. In some embodiments, the help data display 5140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 5140 may present flow charts or walk-throughs for the assistant to use in determining a root cause and/or solution to a patient’s problem. In some embodiments, the assistant interface 5094 may present two or more help data displays 5140, which may be the same or different, for simultaneous presentation of help data for use by the assistant for example, a first help data display may be used to present a troubleshooting flowchart to determine the source of a patient’s problem, and a second help data display may present script information for the assistant to read to the patient, such information to preferably include directions for the patient to perform some action, which may help to narrow down or solve the problem. In some embodiments, based upon inputs to the troubleshooting flowchart in the first help data display, the second help data display may automatically populate with script information.

[0953] The example overview display 5120 shown in FIG. 47 also includes a patient interface control 5150 presenting information regarding the patient interface 5050, and/or to modify one or more settings of the patient interface 5050. The patient interface control 5150 may take the form of a portion or region of the overview display 5120, as shown in FIG. 47. The patient interface control 5150 may take other forms, such as a separate screen or a popup window. The patient interface control 5150 may present information communicated to the assistant interface 5094 via one or more of the interface monitor signals 98b. As shown in FIG. 47, the patient interface control 5150 includes a display feed 5152 of the display presented by the patient interface 5050. In some embodiments, the display feed 5152 may include a live copy of the display screen currently being presented to the patient by the patient interface 5050. In other words, the display feed 5152 may present an image of what is presented on a display screen of the patient interface 5050. In some embodiments, the display feed 5152 may include abbreviated information regarding the display screen currently being presented by the patient interface 5050, such as a screen name or a screen number. The patient interface control 5150 may include a patient interface setting control 5154 for the assistant to adjust or to control one or more settings or aspects of the patient interface 5050. In some embodiments, the patient interface setting control 5154 may cause the assistant interface 5094 to generate and/or to transmit an interface control signal 5098 for controlling a function or a setting of the patient interface 5050.

[0954] In some embodiments, the patient interface setting control 5154 may include collaborative browsing or co-browsing capability for the assistant to remotely view and/or control the patient interface 5050. For example, the patient interface setting control 5154 may enable the assistant to remotely enter text to one or more text entry fields on the patient interface 5050 and/or to remotely control a cursor on the patient interface 5050 using a mouse or touchscreen of the assistant interface 5094.

[0955] In some embodiments, using the patient interface 50, the patient interface setting control 5154 may allow the assistant to change a setting that cannot be changed by the patient. For example, the patient interface 5050 may be precluded from accessing a language setting to prevent a patient from inadvertently switching, on the patient interface 5050, the language used for the displays, whereas the patient interface setting control 5154 may enable the assistant to change the language setting of the patient interface 5050. In another example, the patient interface 5050 may not be able to change a font size setting to a smaller size in order to prevent a patient from inadvertently switching the font size used for the displays on the patient interface 5050 such that the display would become illegible to the patient, whereas the patient interface setting control 5154 may provide for the assistant to change the font size setting of the patient interface 5050.

[0956] The example overview display 5120 shown in FIG. 47 also includes an interface communications display 5156 showing the status of communications between the patient interface 5050 and one or more other devices 5070, 5082, 5084, such as the treatment apparatus 5070, the ambulation sensor 5082, and/or the goniometer 5084. The interface communications display 5156 may take the form of a portion or region of the overview display 5120, as shown in FIG. 47. The interface communications display 5156 may take other forms, such as a separate screen or a popup window. The interface communications display 5156 may include controls for the assistant to remotely modify communications with one or more of the other devices 5070, 5082, 5084. For example, the assistant may remotely command the patient interface 5050 to reset communications with one of the other devices 5070, 5082, 5084, or to establish communications with a new one of the other devices 5070, 5082, 5084. This functionality may be used, for example, where the patient has a problem with one of the other devices 5070, 5082, 5084, or where the patient receives a new or a replacement one of the other devices 5070, 5082, 5084.

[0957] The example overview display 5120 shown in FIG. 47 also includes an apparatus control 5160 for the assistant to view and/or to control information regarding the treatment apparatus 5070. The apparatus control 5160 may take the form of a portion or region of the overview display 5120, as shown in FIG. 47. The apparatus control 5160 may take other forms, such as a separate screen or a popup window. The apparatus control 5160 may include an apparatus status display 5162 with information regarding the current status of the apparatus. The apparatus status display 5162 may present information communicated to the assistant interface 5094 via one or more of the apparatus monitor signals 5099b. The apparatus status display 5162 may indicate whether the treatment apparatus 5070 is currently communicating with the patient interface 5050. The apparatus status display 5162 may present other current and/or historical information regarding the status of the treatment apparatus 5070.

[0958] The apparatus control 5160 may include an apparatus setting control 5164 for the assistant to adjust or control one or more aspects of the treatment apparatus 5070. The apparatus setting control 5164 may cause the assistant interface 5094 to generate and/or to transmit an apparatus control signal 5099 for changing an operating parameter of the treatment apparatus 5070, (e.g., a pedal radius setting, a resistance setting, a target RPM, etc.). The apparatus setting control 5164 may include a mode button 5166 and a position control 5168, which may be used in conjunction for the assistant to place an actuator 5078 of the treatment apparatus 5070 in a manual mode, after which a setting, such as a position or a speed of the actuator 5078, canbe changed using the position control 5168. The mode button 5166 may provide for a setting, such as a position, to be toggled between automatic and manual modes. In some embodiments, one or more settings may be adjustable at any time, and without having an associated auto/manual mode. In some embodiments, the assistant may change an operating parameter of the treatment apparatus 5070, such as a pedal radius setting, while the patient is actively using the treatment apparatus 5070. Such “on the fly” adjustment may or may not be available to the patient using the patient interface 5050. In some embodiments, the apparatus setting control 5164 may allow the assistant to change a setting that cannot be changed by the patient using the patient interface 5050. For example, the patient interface 5050 may be precluded from changing a preconfigured setting, such as a height or a tilt setting of the treatment apparatus 5070, whereas the apparatus setting control 5164 may provide for the assistant to change the height or tilt setting of the treatment apparatus 5070.

[0959] The example overview display 5120 shown in FIG. 47 also includes a patient communications control 5170 for controlling an audio or an audiovisual communications session with the patient interface 5050. The communications session with the patient interface 5050 may comprise a live feed from the assistant interface 5094 for presentation by the output device of the patient interface 5050. The live feed may take the form of an audio feed and/or a video feed. In some embodiments, the patient interface 5050 may be configured to provide two-way audio or audiovisual communications with a person using the assistant interface 5094. Specifically, the communications session with the patient interface 5050 may include bidirectional (two-way) video or audiovisual feeds, with each of the patient interface 5050 and the assistant interface 5094 presenting video of the other one. In some embodiments, the patient interface 5050 may present video from the assistant interface 5094, while the assistant interface 5094 presents only audio or the assistant interface 5094 presents no live audio or visual signal from the patient interface 5050. In some embodiments, the assistant interface 5094 may present video from the patient interface 5050, while the patient interface 5050 presents only audio or the patient interface 5050 presents no live audio or visual signal from the assistant interface 5094.

[0960] In some embodiments, the audio or an audiovisual communications session with the patient interface 5050 may take place, at least in part, while the patient is performing the rehabilitation regimen upon the body part. The patient communications control 5170 may take the form of a portion or region of the overview display 5120, as shown in FIG. 47. The patient communications control 5170 may take other forms, such as a separate screen or a popup window. The audio and/or audiovisual communications may be processed and/or directed by the assistant interface 5094 and/or by another device or devices, such as a telephone system, or a videoconferencing system used by the assistant while the assistant uses the assistant interface 5094. Alternatively or additionally, the audio and/or audiovisual communications may include communications with a third party. For example, the system 5010 may enable the assistant to initiate a 3-way conversation regarding use of a particular piece of hardware or software, with the patient and a subject matter expert, such as a medical professional or a specialist. The example patient communications control 5170 shown in FIG. 47 includes call controls 5172 for the assistant to use in managing various aspects of the audio or audiovisual communications with the patient. The call controls 5172 include a disconnect button 5174 for the assistant to end the audio or audiovisual communications session. The call controls 5172 also include a mute button 5176 to temporarily silence an audio or audiovisual signal from the assistant interface 5094. In some embodiments, the call controls 5172 may include other features, such as a hold button (not shown). The call controls 5172 also include one or more record/playback controls 5178, such as record, play, and pause buttons to control, with the patient interface 5050, recording and/or playback of audio and/or video from the teleconference session. The call controls 5172 also include a video feed display 5180 for presenting still and/or video images from the patient interface 5050, and a self-video display 5182 showing the current image of the assistant using the assistant interface. The self video display 5182 may be presented as a picture-in-picture format, within a section of the video feed display 5180, as shown in FIG. 47. Alternatively or additionally, the self-video display 5182 maybe presented separately and/or independently from the video feed display 5180.

[0961] The example overview display 5120 shown in FIG. 47 also includes a third party communications control 5190 for use in conducting audio and/or audiovisual communications with a third party. The third party communications control 5190 may take the form of a portion or region of the overview display 5120, as shown in FIG. 47. The third party communications control 5190 may take other forms, such as a display on a separate screen or a popup window. The third party communications control 5190 may include one or more controls, such as a contact list and/or buttons or controls to contact a third party regarding use of a particular piece of hardware or software, e.g., a subject matter expert, such as a medical professional or a specialist. The third party communications control 5190 may include conference calling capability for the third party to simultaneously communicate with both the assistant via the assistant interface 5094, and with the patient via the patient interface 5050. For example, the system 5010 may provide for the assistant to initiate a 3-way conversation with the patient and the third party.

[0962] FIG. 48 shows an example block diagram of training a machine learning model 5013 to output, based on data 5600 pertaining to the patient, a treatment plan 5602 for the patient according to the present disclosure. Data pertaining to other patients may be received by the server 5030. The other patients may have used various treatment apparatuses to perform treatment plans. The data may include characteristics of the other patients, the details of the treatment plans performed by the other patients, and/or the results of performing the treatment plans (e.g., a percent of recovery of a portion of the patients’ bodies, an amount of recovery of a portion of the patients ’ bodies, an amount of increase or decrease in muscle strength of a portion of patients ’ bodies, an amount of increase or decrease in range of motion of a portion of patients’ bodies, etc.).

[0963] As depicted, the data has been assigned to different cohorts. Cohort A includes data for patients having similar first characteristics, first treatment plans, and first results. Cohort B includes data for patients having similar second characteristics, second treatment plans, and second results. For example, cohort A may include first characteristics of patients in their twenties without any medical conditions who underwent surgery for a broken limb; their treatment plans may include a certain treatment protocol (e.g., use the treatment apparatus 5070 for 30 minutes 5 times a week for 3 weeks, wherein values for the properties, configurations, and/or settings of the treatment apparatus 5070 are set to X (where X is a numerical value) for the first two weeks and to Y (where Y is a numerical value) for the last week).

[0964] Cohort A and cohort B may be included in a training dataset used to train the machine learning model 5013. The machine learning model 5013 may be trained to match a pattern between characteristics for each cohort and output the treatment plan that provides the result. Accordingly, when the data 5600 for a new patient is input into the trained machine learning model 5013, the trained machine learning model 5013 may match the characteristics included in the data 5600 with characteristics in either cohort A or cohort B and output the appropriate treatment plan 5602. In some embodiments, the machine learning model 5013 may be trained to output one or more excluded treatment plans that should not be performed by the new patient.

[0965] FIG. 49 shows an embodiment of an overview display 5120 of the assistant interface 5094 presenting recommended treatment plans and excluded treatment plans in real-time during a telemedicine session according to the present disclosure. As depicted, the overview display 5120 just includes sections for the patient profile 5130 and the video feed display 5180, including the self-video display 5182. Any suitable configuration of controls and interfaces of the overview display 120 described with reference to FIG. 47 may be presented in addition to or instead of the patient profile 5130, the video feed display 5180, and the self-video display 5182. [0966] The assistant (e.g., medical professional) using the assistant interface 5094 (e.g., computing device) during the telemedicine session may be presented in the self-video 5182 in a portion of the overview display 5120 (e.g., user interface presented on a display screen 5024 of the assistant interface 5094) that also presents a video from the patient in the video feed display 5180. Further, the video feed display 5180 may also include a graphical user interface (GUI) object 5700 (e.g., a button) that enables the medical professional to share, in real time or near real-time during the telemedicine session, the recommended treatment plans and/or the excluded treatment plans with the patient on the patient interface 5050. The medical professional may select the GUI object 5700 to share the recommended treatment plans and/or the excluded treatment plans. As depicted, another portion of the overview display 5120 includes the patient profile display 5130.

[0967] The patient profile display 5130 is presenting two example recommended treatment plans 5600 and one example excluded treatment plan 5602. As described herein, the treatment plans may be recommended in view of characteristics of the patient being treated. To generate the recommended treatment plans 5600 the patient should follow to achieve a desired result, a pattern between the characteristics of the patient being treated and a cohort of other people who have used the treatment apparatus 5070 to perform a treatment plan may be matched by one or more machine learning models 5013 of the artificial intelligence engine 5011. Each of the recommended treatment plans may be generated based on different desired results.

[0968] For example, as depicted, the patient profile display 5130 presents “The characteristics of the patient match characteristics of users in Cohort A. The following treatment plans are recommended for the patient based on his characteristics and desired results.” Then, the patient profile display 5130 presents recommended treatment plans from cohort A, and each treatment plan provides different results.

[0969] As depicted, treatment plan “A” indicates “Patient X should use treatment apparatus for 5030 minutes a day for 4 days to achieve an increased range of motion of Y%; Patient X has Type 2 Diabetes; and Patient X should be prescribed medication Z for pain management during the treatment plan (medication Z is approved for people having Type 2 Diabetes).” Accordingly, the treatment plan generated achieves increasing the range of motion of Y%. As may be appreciated, the treatment plan also includes a recommended medication (e.g., medication Z) to prescribe to the patient to manage pain in view of a known medical disease (e.g., Type 2 Diabetes) of the patient. That is, the recommended patient medication not only does not conflict with the medical condition of the patient but thereby improves the probability of a superior patient outcome. This specific example and all such examples elsewhere herein are not intended to limit in any way the generated treatment plan from recommending multiple medications, or from handling the acknowledgement, view, diagnosis and/or treatment of comorbid conditions or diseases.

[0970] Recommended treatment plan “B” may specify, based on a different desired result of the treatment plan, a different treatment plan including a different treatment protocol for a treatment apparatus, a different medication regimen, etc.

[0971] As depicted, the patient profile display 5130 may also present the excluded treatment plans 5602. These types of treatment plans are shown to the assistant using the assistant interface 5094 to alert the assistant not to recommend certain portions of a treatment plan to the patient. For example, the excluded treatment plan could specify the following: “Patient X should not use treatment apparatus for longer than 30 minutes a day due to a heart condition; Patient X has Type 2 Diabetes; and Patient X should not be prescribed medication M for pain management during the treatment plan (in this scenario, medication M can cause complications for people having Type 2 Diabetes) . Specifically, the excluded treatment plan points out a limitation of a treatment protocol where, due to a heart condition, Patient X should not exercise for more than 30 minutes a day. The ruled-out treatment plan also points out that Patient X should not be prescribed medication M because it conflicts with the medical condition Type 2 Diabetes.

[0972] The assistant may select the treatment plan for the patient on the overview display 5120. For example, the assistant may use an input peripheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) to select from the treatment plans 5600 for the patient. In some embodiments, during the telemedicine session, the assistant may discuss the pros and cons of the recommended treatment plans 5600 with the patient.

[0973] In any event, the assistant may select the treatment plan for the patient to follow to achieve the desired result. The selected treatment plan may be transmitted to the patient interface 5050 for presentation. The patient may view the selected treatment plan on the patient interface 5050. In some embodiments, the assistant and the patient may discuss during the telemedicine session the details (e.g., treatment protocol using treatment apparatus 5070, diet regimen, medication regimen, etc.) in real-time or in near real-time. In some embodiments, the server 5030 may control, based on the selected treatment plan and during the telemedicine session, the treatment apparatus 5070 as the user uses the treatment apparatus 5070.

[0974] FIG. 50 shows an embodiment of the overview display 5120 of the assistant interface 5094 presenting, in real-time during a telemedicine session, recommended treatment plans that have changed as a result of patient data changing according to the present disclosure. As may be appreciated, the treatment apparatus 5070 and/or any computing device (e.g., patient interface 5050) may transmit data while the patient uses the treatment apparatus 5070 to perform a treatment plan. The data may include updated characteristics of the patient. For example, the updated characteristics may include new performance information and/or measurement information. The performance information may include a speed of a portion of the treatment apparatus 5070, a range of motion achieved by the patient, a force exerted on a portion of the treatment apparatus 5070, a heartrate of the patient, a blood pressure of the patient, a respiratory rate of the patient, and so forth.

[0975] In one embodiment, the data received at the server 5030 may be input into the trained machine learning model 5013, which may determine that the characteristics indicate the patient is on track for the current treatment plan. Determining the patient is on track for the current treatment plan may cause the trained machine learning model 5013 to adjust a parameter of the treatment apparatus 5070. The adjustment may be based on a next step of the treatment plan to further improve the performance of the patient.

[0976] In one embodiment, the data received at the server 5030 may be input into the trained machine learning model 5013, which may determine that the characteristics indicate the patient is not on track (e.g., behind schedule, not able to maintain a speed, not able to achieve a certain range of motion, is in too much pain, etc.) for the current treatment plan or is ahead of schedule (e.g., exceeding a certain speed, exercising longer than specified with no pain, exerting more than a specified force, etc.) for the current treatment plan. The trained machine learning model 5013 may determine that the characteristics of the patient no longer match the characteristics of the patients in the cohort to which the patient is assigned. Accordingly, the trained machine learning model 5013 may reassign the patient to another cohort that includes qualifying characteristics the patient’s characteristics. As such, the trained machine learning model 5013 may select a new treatment plan from the new cohort and control, based on the new treatment plan, the treatment apparatus 5070.

[0977] In some embodiments, prior to controlling the treatment apparatus 5070, the server 5030 may provide the new treatment plan 5800 to the assistant interface 5094 for presentation in the patient profile 5130. As depicted, the patient profile 5130 indicates “The characteristics of the patient have changed and now match characteristics of users in Cohort B. The following treatment plan is recommended for the patient based on his characteristics and desired results.” Then, the patient profile 5130 presents the new treatment plan 5800 (“Patient X should use treatment apparatus for 10 minutes a day for 3 days to achieve an increased range of motion of L%” The assistant (medical professional) may select the new treatment plan 5800, and the server 5030 may receive the selection. The server 5030 may control the treatment apparatus 5070 based on the new treatment plan 5800. In some embodiments, the new treatment plan 5800 may be transmitted to the patient interface 5050 such that the patient may view the details of the new treatment plan 5800.

[0978] FIG. 51 shows an embodiment of the overview display 5120 of the assistant interface 5094 presenting, in real-time during a telemedicine session, treatment plans and billing sequences tailored for certain parameters according to the present disclosure. As depicted, the overview display 5120 just includes sections for the patient profile 5130 and the video feed display 5180, including the self-video display 5182. Any suitable configuration of controls and interfaces of the overview display 120 described with reference to FIG. 47 may be presented in addition to or instead of the patient profile 5130, the video feed display 5180, and the self-video display 5182. In some embodiments, the same treatment plans and billing sequences may be presented in a display screen 5054 of the patient interface 5050. In some embodiments, the treatment plans and billing sequences may be presented simultaneously, in real-time or near real-time, during a telemedicine or telehealth session, on both the display screen 5054 of the patient interface 5050 and the display screen 5024 of the assistant interface 5094. [0979] The assistant (e.g., medical professional) using the assistant interface 5094 (e.g., computing device) during the telemedicine session may be presented in the self-video 5182 in a portion of the overview display 5120 (e.g., user interface presented on a display screen 5024 of the assistant interface 5094) that also presents a video from the patient in the video feed display 5180. Further, the video feed display 5180 may also include a graphical user interface (GUI) object 5700 (e.g., a button) that enables the medical professional to share, in real time or near real-time during the telemedicine session, the treatment plans and/or the billing sequences with the patient on the patient interface 5050. The medical professional may select the GUI object 5700 to share the treatment plans and/or the billing sequences. As depicted, another portion of the overview display 5120 includes the patient profile display 5130.

[0980] The patient profile display 5130 is presenting two example treatment plans and two example billing sequences. Treatment plans 5900 and 5902 may be generated based on information (e.g., medical diagnosis code) pertaining to a condition of the patient. Treatment plan 5900 corresponds to billing sequence 5904, and treatment plan 5902 corresponds to billing sequence 5906. The generated billing sequences 5904 and 5906 and the treatment plans 5900 and 5902 comply with a set of billing procedures including rules pertaining to billing codes, order, timing, and constraints (e.g., laws, regulations, etc.). As described herein, each of the respective the billing sequences 5904 and 5906 may be generated based on a set of billing procedures associated with at least a portion of instructions included in each of the respective treatment plans 900 and 902. Further, each of the billing sequences 5904 and 5906 and/or treatment plans 5900 and 5902 may be tailored according to a certain parameter (e.g., a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof). In some embodiments, the monetary value amount “to be paid” may be inclusive to any means of settling an account with an insurance provider (e.g., payment of monetary, issuance of credit).

[0981] Each of the respective treatment plans 5900 and 5902 may include one or more procedures to be performed on the patient based on the information pertaining to the medical condition of the patient. Further, each of the respective billing sequences 5904 and 5906 may include an order for how the procedures are to be billed based on the billing procedures and one or more parameters.

[0982] For example, as depicted, the patient profile display 5130 presents “Patient has Condition Z”, where condition Z may be associated with information of the patient including a particular medical diagnosis code received from an EMR system. Based on the information, the treatment plans 5900 and 5906 each include procedures relevant to be performed for the Condition Z. The patient profile 5130 presents “Treatment Plan 1 : 1. Procedure A; 2. Procedure B”. Each of the procedures may specify one or more instructions for performing the procedures, and each of the one or more instructions may be associated with a particular billing code or codes. Then, the patient profile display 5130 presents the billing sequence 5904 generated, based on the billing procedures and one or more parameters, for at least a portion of the one or more instructions included in the treatment plan 5900. The patient profile display 5130 presents “Billing Sequence 1 Tailored for [Parameter X] : 1. Bill for code 123 associated with Procedure A; 2. Bill for code 234 associated with Procedure B”. It should be noted that [Parameter X] may be any suitable parameter, such as a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof.

[0983] Further, the patient profile 5130 also presents the treatment plan 5902 and presents “Treatment Plan 2: 1. Procedure C; 2. Procedure A”. Each of the procedures may specify one or more instructions for performing the procedures, and each of the one or more instructions may be associated with a particular billing code. Then, the patient profile display 5130 presents the billing sequence 5906 generated, based on the billing procedures and one or more parameters, for at least a portion of the one or more instructions included in the treatment plan 5902. The patient profile display 5130 presents “Billing Sequence 2 Tailored for [Parameter Y] : 1. Bill for code 345 associated with Procedure C; 2. Bill for code 123 associated with Procedure A”. It should be noted that [Parameter Y] may be any suitable parameter, such as a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof. It should also be noted that in the depicted example [Parameter X] and [Parameter Y] are different parameters.

[0984] As should be appreciated, the billing sequence 5904 and 5906 includes a different order for billing the procedures included in the respective treatment plans 5900 and 5902, and each of the billing sequences 5904 and 5906 complies with the billing procedures. The billing sequence 5904 may have been tailored for [Parameter X] (e.g., a fee to be paid to a medical professional) and the billing sequence 5906 may have been tailored for [Parameter Y] (e.g., a plan of reimbursement).

[0985] The order of performing the procedures for the treatment plan 5902 specifies performing Procedure C first and then Procedure A. However, the billing sequence 5906 specifies billing for the code 123 associated with Procedure A first and then billing for the code 345 associated with Procedure C. Such a billing sequence 5906 may have been dictated by the billing procedures. For example, although Procedure A is performed second, a law, regulation, or the like may dictate that Procedure A be billed before any other procedure.

[0986] Further, as depicted, a graphical element (e.g., button for “SELECT”) may be presented in the patient profile display 5130. Although just one graphical element is presented, any suitable number of graphical elements for selecting a treatment plan and/or billing sequence may be presented in the patient profile display 5130. As depicted, a user (e.g., medical professional or patient) uses an input peripheral (e.g., mouse, keyboard, microphone, touchscreen) to select (as represented by circle 5950) the graphical element associated with the treatment plan 5900 and billing sequence 5904. The medical professional may prefer to receive a certain fee and the billing sequence 5904 is optimized based on [Parameter X] (e.g., a fee to be paid to the medical professional, as previously discussed). Accordingly, the assistant interface 5094 may transmit a control signal to the treatment apparatus 5070 to control, based on the treatment plan 5900, operation of the treatment apparatus 5070. In some embodiments, the patient may select the treatment plan from the display screen 5054 and the patient interface 5050 may transmit a control signal to the treatment apparatus 5070 to control, based on the selected treatment plan, operation of the treatment apparatus 5070.

[0987] FIG. 52 shows an example embodiment of a method 51000 for generating, based on a set of billing procedures, a billing sequence tailored for a particular parameter, where the billing sequence pertains to a treatment plan according to the present disclosure. The method 51000 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as is ran on a general-purpose computer system or a dedicated machine), or a combination of both. The method 51000 and/or each of its individual functions, routines, other methods, scripts, subroutines, or operations may be performed by one or more processors of a computing device (e.g., any component of FIGURE 43, such as server 5030 executing the artificial intelligence engine 5011). In certain implementations, the method 51000 may be performed by a single processing thread. Alternatively, the method 51000 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, other methods, scripts, subroutines, or operations of the methods. [0988] For simplicity of explanation, the method 51000 is depicted and described as a series of operations. However, operations in accordance with this disclosure can occur in various orders and/or concurrently, and/or with other operations not presented and described herein. For example, the operations depicted in the method 51000 may occur in combination with any other operation of any other method disclosed herein. Furthermore, not all illustrated operations may be required to implement the method 51000 in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the method 51000 could alternatively be represented as a series of interrelated states via a state diagram, a directed graph, a deterministic finite state automaton, a non-deterministic finite state automaton, a Markov diagram, or events.

[0989] At 51002, the processing device may receive information pertaining to a patient. The information may include a medical diagnosis code (DRG, ICD-9, ICD-10, etc.) associated with the patient. The information may also include characteristics of the patient, such as personal information, performance information, and measurement information. The personal information may include, e.g., demographic, psychographic or other information, such as an age, a weight, a gender, a height, a body mass index, a medical condition, a familial medication history, an injury, a medical procedure, a medication prescribed, or some combination thereof. The performance information may include, e.g., an elapsed time of using a treatment apparatus, an amount of force exerted on a portion of the treatment apparatus, the body part used to exert the amount of force, the tendons, ligaments, muscles and other body parts associated with or connected to the body part, a range of motion achieved on the treatment apparatus, a movement speed of a portion of the treatment apparatus, an indication of a plurality of pain levels using the treatment apparatus, or some combination thereof. The measurement information may include, e.g., a vital sign, a respiration rate, a heartrate, a temperature, a blood pressure, or some combination thereof. It may be desirable to process the characteristics of a multitude of patients, the treatment plans performed for those patients, and the results of the treatment plans for those patients.

[0990] At 51004, the processing device may generate, based on the information, a treatment plan for the patient. The treatment plan may include a set of instructions for the patient to follow (e.g., for rehabilitation, prehabilitation, post-habilitation, etc.). In some embodiments, the treatment plan may be generated by comparing and matching the information of the patient with information of other patients. In some embodiments, the treatment plan may pertain to habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength training, endurance training, weight loss, weight gain, flexibility, pliability, or some combination thereof. In some embodiments, the set of instructions may include a set of exercises for the patient to perform, an order for the set of exercises, a frequency for performing the set of exercises, a diet program, a sleep regimen, a set of procedures to perform on the patient, an order for the set of procedures, a medication regimen, a set of sessions for the patient, or some combination thereof.

[0991] At 51006, the processing device may receive a set of billing procedures associated with the set of instructions. The set of billing procedures may include rules pertaining to billing codes, timing, order, insurance regimens, constraints, or some combination thereof. In some embodiments, the constraints may include constraints set forth in regulations, laws, or some combination thereof. The rules pertaining to the billing codes may specify exact billing codes for procedures. The billing codes may be standardized and mandated by certain regulatory agencies and/or systems. A certain billing code may be unique to a certain procedure.

[0992] The rules pertaining to the timing information may specify when certain procedures and/or associated billing codes may be billed. The timing information may also specify a length of time from when a procedure is performed until the procedure can be billed, a periodicity that certain procedures may be billed, a frequency that certain procedures may be billed, and so forth.

[0993] The rules pertaining to the order information may specify an order in which certain procedures and/or billing codes may be billed to the patient. For example, the rules may specify that a certain procedure cannot be billed until another procedure is billed.

[0994] The rules pertaining to the insurance regimens may specify what amount and/or percentage the insurance provider pays based on the insurance benefits of the patient, when the insurance provider distributes payments, and the like.

[0995] The rules pertaining to the constraints may include laws and regulations of medical billing. For example, the Health Insurance Portability and Accountability Act (HIPAA) includes numerous medical billing laws and regulations. In the European Union, the General Protection Data Regulation (GDPR) would impose certain constraints. One of the laws and regulations is patient confidentiality, which makes it necessary for each and every medical practice to create safeguards against the leaking of confidential patient information. Another of the laws and regulations is the use of ICD-10 codes, which allow for more specificity in reporting of patient diagnoses. Other laws and regulations, in certain jurisdictions, may include requirements to pseudonymize, pseudonymise, anonymize or anonymise (the terms can have different meanings in different countries and jurisidictions) data subject (i.e., patient) personally identifying information (PII) or personal health identifying information (PHI).

[0996] Another law and regulation pertains to balance billing. When a healthcare provider signs a contract with an insurance company, the healthcare provider agrees to take a certain percentage or payment amount for specific services. The amount the healthcare provider bills over the agreed upon amount with the insurance provider must be written off by the healthcare provider’s office. That is, the healthcare provider cannot bill the patient for any amount over the negotiated rate. If, nevertheless, a healthcare provider does this, it is referred to as balance billing, which is illegal per the contract with the insurance company.

[0997] Further, medical billing fraud is also specified as being illegal by HIPAA. Medical billing fraud may refer to a healthcare provider’s office knowingly billing for services that were not performed, or that are inaccurately represented or described.

[0998] At 51008, the processing device may generate, based on the set of billing procedures, a billing sequence for at least a portion of the set of instructions included in the treatment plan. Just a portion of the total number of instructions may be accounted for in the billing sequence because some of the instructions may not yet have been completed or may still be completed in the future. However, if all the instructions included in the treatment plan are completed, then the billing sequence may be generated for all of the instructions. The billing sequence may be tailored according to a certain parameter. The parameter may be a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof.

[0999] At 51010, the processing device may transmit the treatment plan and the billing sequence to a computing device. The computing device may be any of the interfaces described with reference to FIG. 43. For example, the treatment plan and the billing sequence may be transmitted to an assistant interface 5094 and/or a patient interface 5050. [1000] In some embodiments, the processing device may cause presentation, in real-time or near real-time during a telemedicine session with a computing device of the patient, of the treatment plan and the billing sequence on a computing device of the medical professional. Further, the processing device may cause presentation, in real-time or near real-time during a telemedicine session with the computing device of the medical professional, of the treatment plan and the billing sequence on the computing device of the medical professional.

[1001] In some embodiments, the processing device may control, based on the treatment plan, the treatment apparatus 5070 used by the patient to perform the treatment plan. For example, the processing device may transmit a control signal to cause a range of motion of the pedals 5102 to adjust (e.g., by electromechanically adjusting the pedals 5102 attached to the pedal arms 5104 inwardly or outwardly on the axle 5106) to a setting specified in the treatment plan. In some embodiments, and as further described herein, a patient may view the treatment plan and/or the billing sequence and select to initiate the treatment plan using the patient interface 5050. In some embodiments, and as further described herein, an assistant (e.g., medical professional) may view the treatment plan and/or the billing sequence and, using the assistant interface 5094, select to initiate the treatment plan. In such an embodiment, the treatment apparatus 5070 may be distally controlled via a remote computing device (e.g., server 5030, assistant interface 5094, etc.). For example, the remote computing device may transmit one or more control signals to the controller 72 of the treatment apparatus 70 to cause the controller 5072 to execute instructions based on the control signals. By executing the instructions, the controller 5072 may control various parts (e.g., pedals, motor, etc.) of the treatment apparatus 5070 in real-time or near real-time while the patient uses the treatment apparatus 5070.

[1002] In some embodiments, the treatment plan, including the configurations, settings, range of motion settings, pain level, force settings, speed settings, etc. of the treatment apparatus 5070 for various exercises, may be transmitted to the controller of the treatment apparatus 5070. In one example, if the user provides an indication, via the patient interface 5050, that he is experiencing a high level of pain at a particular range of motion, the controller may receive the indication. Based on the indication, the controller may electronically adjust the range of motion of the pedal 5102 by adjusting the pedal inwardly or outwardly via one or more actuators, hydraulics, springs, electric, mechanical, optical, opticoelectric or electromechanical motors, or the like. When the user indicates certain pain levels during an exercise, the treatment plan may define alternative range of motion settings for the pedal 5102. Accordingly, once the treatment plan is uploaded to the controller of the treatment apparatus 5070, the treatment apparatus may be self-functioning. It should be noted that the patient (via the patient interface 5050) and/or the assistant (via the assistant interface 5094) may override any of the configurations or settings of the treatment apparatus 5070 at any time. For example, the patient may use the patient interface 5050 to cause the treatment apparatus 5070 to immediately stop, if so desired.

[1003] FIG. 53 shows an example embodiment of a method 51100 for receiving requests from computing devices and modifying the billing sequence based on the requests according to the present disclosure. Method 51100 includes operations performed by processors of a computing device (e.g., any component of FIG. 43, such as server 5030 executing the artificial intelligence engine 5011). In some embodiments, one or more operations of the method 51100 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 51100 may be performed in the same or in a similar manner as described above in regard to method 51000. The operations of the method 51100 may be performed in some combination with any of the operations of any of the methods described herein.

[1004] At 51102, the processing device may receive, from a computing device, a first request pertaining to the billing sequence. The request may be received from a computing device of a medical professional. The request may specify that the medical professional desires instant payment of his or her portion of the bills included in the billing sequence, funds to be received sooner than had the original billing sequence been implemented, an optimized total amount of the funds to be received, an optimized number of payments to be received, an optimized schedule for the funds to be received, or some combination thereof.

[1005] At 51104, the processing device may receive, from another computing device of an insurance provider, a second request pertaining to the billing sequence. The second request may specify the insurance provider desires instant payment of their portion of the bills in the billing sequence, to be received sooner than had the original billing sequence been implemented, an optimized total amount of the funds to be received, an optimized number of payments to be received, an optimized schedule for the funds to be received, or some combination thereof.

[1006] At 51106, the processing device may modify, based on the first request and the second request, the billing sequence to generate a modified billing sequence, such that the modified billing sequence results in funds being received sooner than had the original billing sequence been implemented, an optimized total amount of the funds to be received, an optimized number of payments to be received, an optimized schedule for the funds to be received, or some combination thereof. The modified billing sequence may be generated to comply with the billing procedures. For example, the modified billing sequence may be generated to ensure that the modified billing sequence is free of medical billing fraud and/or balance billing.

[1007] FIG. 54 shows an embodiment of the overview display 5120 of the assistant interface 5094 presenting, in real-time during a telemedicine session, optimal treatment plans that generate certain monetary value amounts and result in certain patient outcomes according to the present disclosure. As depicted, the overview display 5120 just includes sections for the patient profile 5130 and the video feed display 5180, including the self-video display 5182. Any suitable configuration of controls and interfaces of the overview display 5120 described with reference to FIG. 47 may be presented in addition to or instead of the patient profile 5130, the video feed display 5180, and the self-video display 5182. In some embodiments, the same optimal treatment plans, including monetary value amounts generated, patient outcomes, and/or risks, may be presented in a display screen 5054 of the patient interface 5050. In some embodiments, the optimal treatment plans including monetary value amounts generated, patient outcomes, and/or risks may be presented simultaneously, in real-time or near real time, during a telehealth session, on both the display screen 5054 of the patient interface 5050 and the display screen 5024 of the assistant interface 5094.

[1008] The assistant (e.g., medical professional) using the assistant interface 5094 (e.g., computing device) during the telemedicine session may be presented in the self-video 5182 in a portion of the overview display 5120 (e.g., user interface presented on a display screen 5024 of the assistant interface 5094) that also presents a video from the patient in the video feed display 5180. Further, the video feed display 5180 may also include a graphical user interface (GUI) object 5700 (e.g., a button) that enables the medical professional to share, in real time or near real-time during the telemedicine session, the optimal treatment plans including the monetary value amounts generated, patient outcomes, risks, etc. with the patient on the patient interface 5050. The medical professional may select the GUI object 5700 to share the treatment plans. As depicted, another portion of the overview display 5120 includes the patient profile display 5130.

[1009] The patient profile display 5130 is presenting two example optimal treatment plans 51200 and 51202. The optimal treatment plan 51200 includes a monetary value amount generated 51204 by the optimal treatment plan 51200, a patient outcome 51206 associated with performing the optimal treatment plan 51200, and a risk 51208 associated with performing the optimal treatment plan 51200. The optimal treatment plan 51202 includes a monetary value amount generated 51210 by the optimal treatment plan 51202, a patient outcome 51212 associated with performing the optimal treatment plan 51202, and a risk 51214 associated with performing the optimal treatment plan 51202. The risks may be determined using an algorithm that accounts for a difficulty of a procedure (e.g., open heart surgery versus an endoscopy), a skill level of a medical professional based on years of experience, malpractice judgments, and/or peer reviews, and various other factors.

[1010] To generate the optimal treatment plans 51200 and 51202, the artificial intelligence engine 11 may receive (i) information pertaining to a medical condition of the patient; (ii) a set of treatment plans that, when applied to patients having a similar medical condition as the patient, cause outcomes to be achieved by the patients; (ii) a set of monetary value amounts associated with the set of treatment plans; and/or (iii) a set of constraints including laws, regulations, and/or rules pertaining to billing codes associated with the set of treatment plans (e.g., more particularly, laws, regulations, and/or rules pertaining to billing codes associated with procedures and/or instructions included in the treatment plans).

[1011] Based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, the artificial intelligence engine 5011 may use one or more trained machine learning models 5013 to generate the optimal treatment plans 51200 and 51202 for the patient. Each of the optimal treatment plans 51200 and 51202 complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated. It should be noted that the optimal treatment plans may be generated and tailored based on one or more parameters (e.g., monetary value amount generated, patient outcome, and/or risk). The one or more parameters may be selected electronically by the artificial intelligence engine 5011 or by a user (e.g., medical professional) using a user interface (e.g., patient profile display 5130) to tailor how the treatment plans are optimized. For example, the user may specify she wants to see optimal treatment plans tailored based on the best patient outcome or, alternatively, based on the maximum monetary value amount generated.

[1012] Each of the respective treatment plans 51200 and 51202 may include one or more procedures to be performed on the patient based on the information pertaining to the medical condition of the patient. Further, each of the respective treatment plans 51200 and 51202 may include one or more billing codes associated with the one or more procedures.

[1013] For example, as depicted, the patient profile display 5130 presents “Patient has Condition Z”, where condition Z may be associated with information of the patient including a particular medical diagnosis code received from an EMR system. The patient profile display 5130 also presents the optimal treatment plan 51200, “Optimal Treatment plan 1 Tailored for [Parameter X] : 1. Procedure A; billing code 123; 2. Procedure B; billing code 234”. The [Parameter X] may be any suitable parameter, such as a monetary value amount generated by the optimal treatment plan, a patient outcome associated with performing the optimal treatment plan, and/or a risk associated with performing the optimal treatment plan. [1014] The patient profile display 5130 presents “Monetary Value Amount Generated for Treatment Plan 1: $monetary ValueX”. monetary ValueX may be any suitable monetary value amount associated with the optimal treatment plan 51200. In some embodiments, monetary ValueX may be a configurable parameter that enables the user to set a desired monetary value amount to be generated.

[1015] The patient profile display 5130 presents “Patient Outcome: patientOutcomel”. patientOutcomel may be any suitable patient outcome (e.g., full recovery or partial recovery, achievement of full or partial: desired range of motion, flexibility, strength, or pliability, etc.) associated with the optimal treatment plan 51200. In some embodiments, patientOutcomel may be a configurable parameter that enables the user to set a desired patient outcome that results from performing the optimal treatment plan.

[1016] The patient profile display 5130 presents “Risk: riskl”. riskl may be any suitable risk (e.g., low, medium, or high; or an absolute or relative number or magnitude on a scale; etc.) associated with the optimal treatment plan 51200. In some embodiments, riskl may be a configurable parameter that enables the user to set a desired risk associated with performing the optimal treatment plan.

[1017] Further, the patient profile display 5130 also presents the optimal treatment plan 51202, “Optimal Treatment plan 2 Tailored for [Parameter Y]: 1. Procedure A; billing code 123; 2. Procedure C; billing code 345”. The [Parameter Y] may be any suitable parameter, such as a monetary value amount generated by the optimal treatment plan, a patient outcome associated with performing the optimal treatment plan, and/or a risk associated with performing the optimal treatment plan.

[1018] The patient profile display 5130 presents “Monetary Value Amount Generated for Treatment Plan 1: $monetary Value Y”. monetary ValueX may be any suitable monetary value amount associated with the optimal treatment plan 51202. In some embodiments, monetary ValueX may be a configurable parameter that enables the user to set a desired monetary value amount to be generated.

[1019] The patient profile display 5130 presents “Patient Outcome: patientOutcome2”. patientOutcome2 may be any suitable patient outcome (e.g., full recovery or partial recovery, achievement of full or partial: desired range of motion, flexibility, strength, or pliability, etc.) associated with the optimal treatment plan 51202. In some embodiments, patientOutcome2 may be a configurable parameter that enables the user to set a desired patient outcome that results from performing the optimal treatment plan.

[1020] The patient profile display 5130 presents “Risk: risk2”. Risk2 may be any suitable risk (e.g., low, medium, or high; or an absolute or relative number or magnitude on a scale; etc..) associated with the optimal treatment plan 51200. In some embodiments, risk2 may be a configurable parameter that enables the user to set a desired risk associated with performing the optimal treatment plan.

[1021] In the depicted example, the [Parameter X] and the [Parameter Y] both correspond to the parameter pertaining to the monetary value amount generated. The monetary value amount generated for [Parameter X] may be set higher than the monetary value amount generated for [Parameter Y] Accordingly, the optimal treatment plan 51200 may include different procedures (e.g., Procedure A and Procedure B) that result in the higher monetary amount generated ([Parameter X]), a better outcome (e.g., patientOutcome 1), and a higher risk (e.g., riskl) than the optimal treatment plan 51202, which may result in a lesser monetary value amount generated ([Parameter y]), less desirable outcome (e.g., patientOutcome2), and a lower risk (e.g., risk2).

[1022] Further, as depicted, a graphical element (e.g., button for “SELECT”) may be presented in the patient profile display 5130. Although just one graphical element is presented, any suitable number of graphical elements for selecting an optimal treatment may be presented in the patient profile display 5130. As depicted, a user (e.g., medical professional or patient) uses an input peripheral (e.g., mouse, keyboard, microphone, touchscreen) to select (as represented by circle 51250) the graphical element associated with the optimal treatment plan 51200. The medical professional may prefer to receive a higher monetary value amount generated (e.g., [Parameter X]) from the optimal treatment plan and/or the patient may have requested the best patient outcome possible. Accordingly, the assistant interface 5094 may transmit a control signal to the treatment apparatus 5070 to control, based on the treatment plan 51200, operation of the treatment apparatus 5070. In some embodiments, the patient may select the treatment plan from the display screen 5054 and the patient interface 5050 may transmit a control signal to the treatment apparatus 5070 to control, based on the selected treatment plan 51200, operation of the treatment apparatus 5070.

[1023] It should be noted that, in some embodiments, just treatment plans that pass muster with respect to standard of care, regulations, laws, and the like may be presented as viable options on a computing device of the patient and/or the medical professional. Accordingly, non-viable treatment plans that fail to meet a standard of care, violate a regulation and/or law, etc. may not be presented as options for selection. For example, the non- viable treatment plan options may be filtered from a result set presented on the computing device. In some embodiments, any treatment plan (e.g., both viable and non-viable options) may be presented on the computing device of the patient and/or medical professional.

[1024] FIG. 55 shows an example embodiment of a method 51300 for generating optimal treatment plans for a patient, where the generating is based on a set of treatment plans, a set of monetary value amounts, and a set of constraints according to the present disclosure. Method 51300 includes operations performed by processors of a computing device (e.g., any component of FIG. 43, such as server 5030 executing the artificial intelligence engine 5011). In some embodiments, one or more operations of the method 51300 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 51300 may be performed in the same or in a similar manner as described above in regard to method 51300. The operations of the method 51300 may be performed in some combination with any of the operations of any of the methods described herein.

[1025] Prior to the method 51300 beginning, the processing device may receive information pertaining to the patient. The information may include a medical diagnosis code and/or the various characteristics (e.g., personal information, performance information, and measurement information, etc.) described herein. The processing device may match the information of the patient with similar information from other patients. Based upon the matching, the processing device may select a set of treatment plans that cause certain outcomes (e.g., desired results) to be achieved by the patients.

[1026] At 51302, the processing device may receive the set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients. In some embodiments, the set of treatment plans may specify procedures to perform for the condition of the patient, a set of exercises to be performed by the patient using the treatment apparatus 5070, a periodicity to perform the set of exercises using the treatment apparatus 5070, a frequency to perform the set of exercises using the treatment apparatus 5070, settings and/or configurations for portions (e.g., pedals, seat, etc.) of the treatment apparatus 5070, and the like.

[1027] At 51304, the processing device may receive a set of monetary value amounts associated with the set of treatment plans. A respective monetary value amount of the set of monetary value amounts may be associated with a respective treatment plan of the set of treatment plans. For example, one respective monetary value amount may indicate $5,000 may be generated if the patient performs the respective treatment plan (e.g., including a consultation with a medical professional during a telemedicine session, rental fee for the treatment apparatus 5070, follow-up in-person visit with the medical professional, etc.).

[1028] At 51306, the processing device may receive a set of constraints. The set of constraints may include rules pertaining to billing codes associated with the set of treatment plans. In some embodiments, the processing device may receive a set of billing codes associated with the procedures to be performed for the patient, the set of exercises, etc. and apply the set of billing codes to the treatment plans in view of the rules. In some embodiments, the set of constraints may further include constraints set forth in regulations, laws, or some combination thereof. For example, the laws and/or regulations may specify that certain billing codes (e.g., DRG or ICD-10) be used for certain procedures and/or exercises.

[1029] At 51308, the processing device may generate, by the artificial intelligence engine 5011, optimal treatment plans for a patient. Generating the optimal treatment plans may be based on the set of treatment plans, the set of monetary value amounts, and the set of constraints. In some embodiments, generating the optimal treatment plans may include optimizing the optimal treatment plans for fees, revenue, profit (e.g., gross, net, etc.), earnings before interest (EBIT), earnings before interest, depreciation and amortization (EBITDA), cash flow, free cash flow, working capital, gross revenue, a value of warrants, options, equity, debt, derivatives or any other financial instrument, any generally acceptable financial measure or metric in corporate finance or according to Generally Accepted Accounting Principles (GAAP) or foreign counterparts, or some combination thereof.

[1030] Each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated. To ensure the procedure is allowed, the set of constraints may be enforced by comparing each procedure included in the optimal treatment plan with the set of constraints. If the procedure is allowed, based on the set of constraints, the procedure is included in the optimal treatment plan. If the procedure is not allowed, based on the set of constraints, the procedure is excluded from the optimal treatment plan. The optimal treatment plans may pertain to habilitation, prehabilitation, rehabilitation, post-habilitation, exercise, strength, pliability, flexibility, weight stability, weight gain, weight loss, cardiovascular fitness, performance or metrics, endurance, respiratory fitness, performance or metrics, or some combination thereof.

[1031] In some embodiments, a first optimal treatment plan of the optimal treatment plans may result in a first patient outcome and a first monetary value amount generated, and a second optimal treatment plan of the optimal treatment plans may result in a second patient outcome and a second monetary value amount generating. The second patient outcome may be better than the first patient outcome and the second monetary value amount generated may be greater than the first monetary value amount generated. Based on certain criteria (e.g., whether the patient desires the best patient outcome or has limited funds), either the first or second optimal treatment plan may be selected and implemented to control the treatment apparatus 70. In this and other scenarios herein, both patient outcomes, even the inferior one, are at or above the standard of care dictated by ethical medical practices for individual medical professionals, hospitals, etc., as the case may be, and such standard of care shall further be consistent with applicable governing regulations and laws, whether de facto or de jure. [1032] At 51310, the processing device may transmit, in real-time or near real-time, the optimal treatment plans to be presented on a computing device of a medical professional. The optimal treatment plans may be presented on the computing device of the medical professional during a telemedicine or telehealth session in which a computing device of the patient is engaged. In some embodiments, the processing device may transmit the optimal treatment plans to be presented, in real-time or near real-time, on a computing device of the patient during a telemedicine session in which the computing device of the medical professional is engaged.

[1033] In some embodiments, the processing device may receive levels of risk associated with the set of treatment plans. In some embodiments, the levels of risk may be preconfigured for each of the set of treatment plans. In some embodiments, the levels of risk may be dynamically determined based on a number of factors (e.g., condition of the patient, difficulty of procedures included in the treatment plan, etc.). In some embodiments, generating the optimal treatment plans may also be based on the levels of risk. Further, in some embodiments, the processing device may transmit the optimal treatment plans and the levels of risk to be presented on the computing device of the medical professional. As used herein, “levels of risk” includes levels of risk for each of one or more risks.

[1034] FIG. 56 shows an example embodiment of a method 51400 for receiving a selection of a monetary value amount and generating an optimal treatment plan based on a set of treatment plans, the monetary value amount, and a set of constraints according to the present disclosure. Method 51400 includes operations performed by processors of a computing device (e.g., any component of FIG. 43, such as server 5030 executing the artificial intelligence engine 5011). In some embodiments, one or more operations of the method 51400 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 51400 may be performed in the same or a similar manner as described above in regard to method 51000. The operations of the method 51400 may be performed in some combination with any of the operations of any of the methods described herein.

[1035] At 51402, the processing device may receive a selection of a certain monetary value amount of the set of monetary value amounts. For example, a graphical element included on a user interface of a computing device may enable a user to select (e.g., enter a monetary value amount in a textbox or select from a drop-down list, radio button, scrollbar, etc.) the certain monetary value amount to be generated by an optimal treatment plan. The certain monetary value amount may be transmitted to the artificial intelligence engine 5011, which uses the certain monetary value amount to generate an optimal treatment plan tailored for the desired monetary value amount.

[1036] At 51404, the processing device may generate, by the artificial intelligence engine 5011, an optimal treatment plan based on the set of treatment plans, the certain monetary value amount, and the set of constraints. The optimal treatment plan complies with the set of constraints and represents another patient outcome and the certain monetary value amount.

[1037] In some embodiments, efficiency of the outcome that is associated with the optimal treatment plans may be weighted more heavily in a formulaic manner than the monetary value amount generated. For example, a first optimal treatment plan may generate a first monetary value and take a first amount of time to perform, and a second optimal treatment plan may generate a second monetary value and may take a second amount of time to perform. The second amount of time may be less than the first amount of time and the second outcome may be sufficient for the patient’s goals and/or the medical professional’s goals. In such an instance, the second optimal treatment plan may be selected and implemented.

[1038] FIG. 57 shows an example embodiment of a method 51500 for receiving a selection of an optimal treatment plan and controlling, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus according to the present disclosure. Method 51500 includes operations performed by processors of a computing device (e.g., any component of FIG. 43, such as server 5030 executing the artificial intelligence engine 5011). In some embodiments, one or more operations of the method 51500 are implemented in computer instructions stored on a memory device and executed by a processing device. The method 51500 may be performed in the same or a similar manner as described above in regard to method 51000. The operations of the method 51500 may be performed in some combination with any of the operations of any of the methods described herein.

[1039] Prior to the method 51500 being executed, various optimal treatment plans may be generated by one or more trained machine learning models 5013 of the artificial intelligence engine 5011. For example, based on a set of treatment plans pertaining to a medical condition of a patient, a set of monetary value amounts associated with the set of treatment plans, and a set of constraints, the one or more trained machine learning models 5013 may generate the optimal treatment plans. In some embodiments, the one or more trained machine learning models 5013 may generate a billing sequence that is tailored based on a parameter (e.g., a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, a monetary value amount to be paid to an insurance provider, or some combination thereof). The various treatment plans and/or billing sequences may be transmitted to one or computing devices of a patient and/or medical professional.

[1040] At 51502 of the method 51500, the processing device may receive a selection of an optimal treatment plan from the optimal treatment plans. The selection may have been entered on a user interface presenting the optimal treatment plans on the patient interface 5050 and/or the assistant interface 5094. In some embodiments, the processing device may receive a selection of a billing sequence associated with at least a portion of a treatment plan. The selection may have been entered on a user interface presenting the billing sequence on the patient interface 5050 and/or the assistant interface 5094. If the user selects a particular billing sequence, the treatment plan associated with the selected billing sequence may be selected.

[1041] At 51504, the processing device may control, based on the selected optimal treatment plan, the treatment apparatus 5070 while the patient uses the treatment apparatus. In some embodiments, the controlling is performed distally by the server 5030. For example, if the selection is made using the patient interface 5050, one or more control signals may be transmitted from the patient interface 5050 to the treatment apparatus 5070 to configure, according to the selected treatment plan, a setting of the treatment apparatus 5070 to control operation of the treatment apparatus 5070. Further, if the selection is made using the assistant interface 5094, one or more control signals may be transmitted from the assistant interface 5094 to the treatment apparatus 5070 to configure, according to the selected treatment plan, a setting of the treatment apparatus 5070 to control operation of the treatment apparatus 5070.

[1042] It should be noted that, as the patient uses the treatment apparatus 5070, the sensors 5076 may transmit measurement data to a processing device. The processing device may dynamically control, according to the treatment plan, the treatment apparatus 5070 by modifying, based on the sensor measurements, a setting of the treatment apparatus 5070. For example, if the force measured by the sensor 5076 indicates the user is not applying enough force to a pedal 5102, the treatment plan may indicate to reduce the required amount of force for an exercise.

[1043] It should be noted that, as the patient uses the treatment apparatus 5070, the user may use the patient interface 5050 to enter input pertaining to a pain level experienced by the patient as the patient performs the treatment plan. For example, the user may enter a high degree of pain while pedaling with the pedals 5102 set to a certain range of motion on the treatment apparatus 5070. The pain level may cause the range of motion to be dynamically adjusted based on the treatment plan. For example, the treatment plan may specify alternative range of motion settings if a certain pain level is indicated when the user is performing an exercise at a certain range of motion.

[1044] Different people have different tolerances for pain. In some embodiments, a person may indicate a pain level they are willing to tolerate to achieve a certain result (e.g., a certain range of motion within a certain time period). A high degree of pain may be acceptable to a person if that degree of pain is associated with achieving the certain result. The treatment plan may be tailored based on the indicated pain level. For example, the treatment plan may include certain exercises, frequencies of exercises, and/or periodicities of exercises that are associated with the indicated pain level and desired result for people having characteristics similar to characteristics of the person.

[1045] FIG. 58 shows an example computer system 51600 which can perform any one or more of the methods described herein, in accordance with one or more aspects of the present disclosure. In one example, computer system 51600 may include a computing device and correspond to the assistance interface 5094, reporting interface 5092, supervisory interface 5090, clinician interface 5020, server 5030 (including the AI engine 5011), patient interface 5050, ambulatory sensor 5082, goniometer 5084, treatment apparatus 5070, pressure sensor 5086, or any suitable component of FIG. 43. The computer system 51600 may be capable of executing instructions implementing the one or more machine learning models 5013 of the artificial intelligence engine 5011 of FIG. 43. The computer system may be connected (e.g., networked) to other computer systems in a LAN, an intranet, an extranet, or the Internet, including via the cloud or a peer-to-peer network. The computer system may operate in the capacity of a server in a client-server network environment. The computer system may be a personal computer (PC), a tablet computer, a wearable (e.g., wristband), a set-top box (STB), a personal Digital Assistant (PDA), a mobile phone, a camera, a video camera, an Internet of Things (IoT) device, or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, while only a single computer system is illustrated, the term “computer” shall also be taken to include any collection of computers that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methods discussed herein.

[1046] The computer system 51600 includes a processing device 51602, a main memory 51604 (e.g., read only memory (ROM), flash memory, solid state drives (SSDs), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory 51606 (e.g., flash memory, solid state drives (SSDs), static random access memory (SRAM)), and a data storage device 51608, which communicate with each other via a bus 51610.

[1047] Processing device 51602 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 51602 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 51602 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a system on a chip, a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, or the like. The processing device 51602 is configured to execute instructions for performing any of the operations and steps discussed herein.

[1048] The computer system 51600 may further include a network interface device 51612. The computer system 51600 also may include a video display 51614 (e.g., a liquid crystal display (LCD), a light-emitting diode (LED), an organic light-emitting diode (OLED), a quantum LED, a cathode ray tube (CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), one or more input devices 51616 (e.g., a keyboard and/or a mouse or a gaming-like control), and one or more speakers 51618 (e.g., a speaker). In one illustrative example, the video display 51614 and the input device(s) 51616 may be combined into a single component or device (e.g., an LCD touch screen).

[1049] The data storage device 51616 may include a computer-readable medium 51620 on which the instructions 51622 embodying any one or more of the methods, operations, or functions described herein is stored. The instructions 51622 may also reside, completely or at least partially, within the main memory 51604 and/or within the processing device 51602 during execution thereof by the computer system 51600. As such, the main memory 51604 and the processing device 51602 also constitute computer-readable media. The instructions 51622 may further be transmitted or received over a network via the network interface device 51612. [1050] While the computer-readable storage medium 51620 is shown in the illustrative examples to be a single medium, the term “computer-readable storage medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “computer- readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic media.

[1051] Clause 1.4. A method for generating, by an artificial intelligence engine, a treatment plan and a billing sequence associated with the treatment plan, the method comprising:

[1052] receiving information pertaining to a patient, wherein the information comprises a medical diagnosis code of the patient;

[1053] generating, based on the information, the treatment plan for the patient, wherein the treatment plan comprises a plurality of instructions for the patient to follow;

[1054] receiving a set of billing procedures associated with the plurality of instructions, wherein the set of billing procedures comprises rules pertaining to billing codes, timing, constraints, or some combination thereof; [1055] generating, based on the set of billing procedures, the billing sequence for at least a portion of the plurality of instructions, wherein the billing sequence is tailored according to a certain parameter; and [1056] transmitting the treatment plan and the billing sequence to a computing device. [1057] Clause 2.4. The method of any preceding clause, further comprising distally controlling, based on the treatment plan, a treatment apparatus used by the patient to perform the treatment plan.

[1058] Clause 3.4. The method of any preceding clause, wherein the certain parameter is a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof.

[1059] Clause 4.4. The method of any preceding clause, wherein the treatment plan is for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength training, endurance training, weight loss, weight gain, flexibility, pliability, or some combination thereof.

[1060] Clause 5.4. The method of any preceding clause, wherein the plurality of instructions comprises:

[1061] a plurality of exercises for the patient to perform,

[1062] an order for the plurality of exercises,

[1063] a frequency for performing the plurality of exercises,

[1064] a diet regimen,

[1065] a sleep regimen,

[1066] a plurality of procedures to perform on the patient,

[1067] an order for the plurality of procedures,

[1068] a medication regimen,

[1069] a plurality of sessions for the patient, or [1070] some combination thereof.

[1071] Clause 6.4. The method of any preceding clause, further comprising causing presentation, in real time or near real-time during a telemedicine session with another computing device of the patient, of the treatment plan and the billing sequence on the computing device of a medical professional.

[1072] Clause 7.4. The method of any preceding clause, further comprising:

[1073] receiving, from the computing device, a first request pertaining to the billing sequence;

[1074] receiving, from another computing device of an insurance provider, a second request pertaining to the billing sequence;

[1075] modifying, based on the first request and the second request, the billing sequence to generate a modified billing sequence, such that the modified billing sequence results in funds being received sooner than had the billing sequence been implemented, an optimized total amount of the funds being received, an optimized number of payments being received, an optimized schedule for the funds being received, or some combination thereof. [1076] Clause 8.4. The method of any preceding clause, wherein the constraints further comprise constraints set forth in regulations, laws, or some combination thereof.

[1077] Clause 9.4. The method of any preceding clause, further comprising transmitting the treatment plan and the billing sequence to be presented on a second computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged. [1078] Clause 10.4. A system, comprising:

[1079] a memory device storing instructions;

[1080] a processing device communicatively coupled to the memory device, the processing device executes the instructions to: [1081] receive information pertaining to a patient, wherein the information comprises a medical diagnosis code of the patient;

[1082] generate, based on the information, a treatment plan for the patient, wherein the treatment plan comprises a plurality of instructions for the patient to follow;

[1083] receive a set of billing procedures associated with the plurality of instructions, wherein the set of billing procedures comprises rules pertaining to billing codes, timing, constraints, or some combination thereof;

[1084] generate, based on the set of billing procedures, a billing sequence for at least a portion of the plurality of instructions, wherein the billing sequence is tailored according to a certain parameter; and [1085] transmit the treatment plan and the billing sequence to a computing device.

[1086] Clause 11.4. The system of any preceding clause, wherein the processing device is further to distally control, based on the treatment plan, a treatment apparatus used by the patient to perform the treatment plan.

[1087] Clause 12.4. The system of any preceding clause, wherein the certain parameter is a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof.

[1088] Clause 13.4. The system of any preceding clause, wherein the treatment plan is for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength training, endurance training, weight loss, weight gain, flexibility, pliability, or some combination thereof.

[1089] Clause 14.4. The system of any preceding clause, wherein the plurality of instructions comprises: [1090] a plurality of exercises for the patient to perform,

[1091] an order for the plurality of exercises,

[1092] a frequency for performing the plurality of exercises,

[1093] a diet regimen,

[1094] a sleep regimen,

[1095] a plurality of procedures to perform on the patient,

[1096] an order for the plurality of procedures,

[1097] a medication regimen,

[1098] a plurality of sessions for the patient, or [1099] some combination thereof.

[1100] Clause 15.4. The system of any preceding clause, wherein the processing device is further to cause presentation, in real-time or near real-time during a telemedicine session with another computing device of the patient, of the treatment plan and the billing sequence on the computing device of a medical professional. [1101] Clause 16.4. The system of any preceding clause, wherein the processing device is further to: [1102] receive, from the computing device, a first request pertaining to the billing sequence;

[1103] receive, from another computing device of an insurance provider, a second request pertaining to the billing sequence;

[1104] modify, based on the first request and the second request, the billing sequence to generate a modified billing sequence, such that the modified billing sequence results in funds being received sooner than had the billing sequence been implemented, an optimized total amount of the funds being received, an optimized number of payments being received, an optimized schedule for the funds being received, or some combination thereof. [1105] Clause 17.4. The system of any preceding clause, wherein the constraints further comprise constraints set forth in regulations, laws, or some combination thereof.

[1106] Clause 18.4. The system of any preceding clause, wherein the processing device is further to transmit the treatment plan and the billing sequence to be presented on a second computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged.

[1107] Clause 19.4. A tangible, non-transitoiy computer-readable medium storing instructions that, when executed, cause a processing device to:

[1108] receive information pertaining to a patient, wherein the information comprises a medical diagnosis code of the patient;

[1109] generate, based on the information, a treatment plan for the patient, wherein the treatment plan comprises a plurality of instructions for the patient to follow;

[1110] receive a set of billing procedures associated with the plurality of instructions, wherein the set of billing procedures comprises rules pertaining to billing codes, timing, constraints, or some combination thereof;

[1111] generate, based on the set of billing procedures, a billing sequence for at least a portion of the plurality of instructions, wherein the billing sequence is tailored according to a certain parameter; and [1112] transmit the treatment plan and the billing sequence to a computing device.

[1113] Clause 20.4. The computer-readable medium of any preceding clause, wherein the processing device is further to distally control, based on the treatment plan, a treatment apparatus used by the patient to perform the treatment plan.

[1114] Clause 21.4. A method for generating, by an artificial intelligence engine, treatment plans for optimizing patient outcome and monetary value amount generated, the method comprising:

[1115] receiving a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients;

[1116] receiving a set of monetary value amounts associated with the set of treatment plans, wherein a respective monetary value amount of the set of monetary value amounts is associated with a respective treatment plan of the set of treatment plans;

[1117] receiving a set of constraints, wherein the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans;

[1118] generating, by the artificial intelligence engine, optimal treatment plans for a patient, wherein the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, wherein each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated; and

[1119] transmitting the optimal treatment plans to be presented on a computing device.

[1120] Clause 22.4. The method of any preceding clause, wherein the optimal treatment plans are for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength, pliability, flexibility, weight stability, weight gain, weight loss, cardiovascular, endurance, respiratory, or some combination thereof.

[1121] Clause 23.4. The method of any preceding clause, further comprising:

[1122] receiving a selection of a certain monetary value amount of the set of monetary value amounts; and [1123] generating, by the artificial intelligence engine, an optimal treatment plan based on the set of treatment plans, the certain monetary value amount, and the set of constraints, wherein the optimal treatment plan complies with the set of constraints and represents another patient outcome and the certain monetary value amount. [1124] Clause 24.4. The method of any preceding clause, wherein the set of treatment plans specifies a set of exercises to be performed by the patient using a treatment apparatus, and the method further comprises: [1125] receiving a set of billing codes associated with the set of exercises; and [1126] correlating the set of billing codes with the rules.

[1127] Clause 25.4. The method of any preceding clause, further comprising:

[1128] receiving levels of risk associated with the set of treatment plans, wherein the generating the optimal treatment plans is also based on the levels of risk; and

[1129] transmitting the optimal treatment plans and the level of risks to be presented on the computing device of the medical professional.

[1130] Clause 26.4. The method of any preceding clause, wherein:

[1131] a first optimal treatment plan of the optimal treatment plans results in a first patient outcome and a first monetary value amount generated; and

[1132] a second optimal treatment plan of the optimal treatment plans results in a second patient outcome and a second monetary value amount generated, wherein the second patient outcome is better than the first patient outcome and the second revenue value generated is greater than the first monetary value amount generated. [1133] Clause 27. The method of any preceding clause, wherein the set of constraints further comprises constraints set forth in regulations, laws, or some combination thereof.

[1134] Clause 28. The method of any preceding clause, further comprising transmitting the optimal treatment plans to be presented on a computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged.

[1135] Clause 29. The method of any preceding clause, further comprising:

[1136] receiving a selection of an optimal treatment plan from the optimal treatment plans; and

[1137] controlling, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus.

[1138] Clause 30.4. The method of any preceding clause, wherein the controlling is performed distally. [1139] Clause 31.4. The method of any preceding clause, wherein:

[1140] the optimal treatment plans are presented on the computing device of a medical professional during a telemedicine session in which a computing device of the patient is engaged.

[1141] Clause 32.4. The method of any preceding clause, wherein:

[1142] the optimal treatment plans are presented on the computing device of patient during a telemedicine session in which a computing device of a medical professional is engaged.

[1143] Clause 33.4. The method of any preceding clause, wherein the generating, based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, the optimal treatment plans further comprises optimizing the optimal treatment plans for revenue generated, profit generated, cash flow generated, free cash flow generated, gross revenue generated, earnings before interest taxes amortization (EBITA) generated, or some combination thereof.

[1144] Clause 34.4. A system, comprising: [1145] a memory device storing instructions; and

[1146] a processing device communicatively coupled to the memory device, the processing device executes the instructions to:

[1147] receive a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients;

[1148] receive a set of monetary value amounts associated with the set of treatment plans, wherein a respective monetary value amount of the set of monetary value amounts is associated with a respective treatment plan of the set of treatment plans;

[1149] receive a set of constraints, wherein the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans;

[1150] generate, by an artificial intelligence engine, optimal treatment plans for a patient, wherein the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, wherein each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated; and

[1151] transmit the optimal treatment plans to be presented on a computing device.

[1152] Clause 35.4. The system of any preceding clause, wherein the optimal treatment plans are for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength, pliability, flexibility, weight stability, weight gain, weight loss, cardiovascular, endurance, respiratory, or some combination thereof.

[1153] Clause 36.4. The system of any preceding clause, wherein the processing device is further to: [1154] receive a selection of a certain monetary value amount of the set of monetary value amounts; and [1155] generate, by the artificial intelligence engine, an optimal treatment plan based on the set of treatment plans, the certain monetary value amount, and the set of constraints, wherein the optimal treatment plan complies with the set of constraints and represents another patient outcome and the certain monetary value amount. [1156] Clause 37.4. The system of any preceding clause, wherein the set of treatment plans specifies a set of exercises to be performed by the patient using a treatment apparatus, and the processing device is further to:

[1157] receive a set of billing codes associated with the set of exercises; and [1158] correlate the set of billing codes with the rules.

[1159] Clause 38.4. The system of any preceding clause, wherein the processing device is further to: [1160] receive levels of risk associated with the set of treatment plans, wherein the generating the optimal treatment plans is also based on the levels of risk; and

[1161] transmit the optimal treatment plans and the level of risks to be presented on the computing device of the medical professional.

[1162] Clause 39.4. The system of any preceding clause, wherein:

[1163] a first optimal treatment plan of the optimal treatment plans results in a first patient outcome and a first monetary value amount generated; and

[1164] a second optimal treatment plan of the optimal treatment plans results in a second patient outcome and a second monetary value amount generated, wherein the second patient outcome is better than the first patient outcome and the second revenue value generated is greater than the first monetary value amount generated. [1165] Clause 40. .4 A tangible, non-transitor computer-readable medium storing instructions that, when executed, cause a processing device to:

[1166] receive a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients;

[1167] receive a set of monetary value amounts associated with the set of treatment plans, wherein a respective monetary value amount of the set of monetary value amounts is associated with a respective treatment plan of the set of treatment plans;

[1168] receive a set of constraints, wherein the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans;

[1169] generate, by an artificial intelligence engine, optimal treatment plans for a patient, wherein the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, wherein each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated; and

[1170] transmit the optimal treatment plans to be presented on a computing device.

[1171] Clause 41.4. A computer-implemented system, comprising:

[1172] a treatment apparatus configured to be manipulated by a patient while performing a treatment plan; [1173] a server computing device configured to execute an artificial intelligence engine to generate the treatment plan and a billing sequence associated with the treatment plan, wherein the server computing device: [1174] receives information pertaining to the patient, wherein the information comprises a medical diagnosis code of the patient;

[1175] generates, based on the information, the treatment plan for the patient, wherein the treatment plan comprises a plurality of instructions for the patient to follow;

[1176] receives a set of billing procedures associated with the plurality of instructions, wherein the set of billing procedures comprises rules pertaining to billing codes, timing, constraints, or some combination thereof; [1177] generates, based on the set of billing procedures, the billing sequence for at least a portion of the plurality of instructions, wherein the billing sequence is tailored according to a certain parameter; and [1178] transmits the treatment plan and the billing sequence to a computing device.

[1179] Clause 42.4. The computer-implemented system of any preceding clause, wherein the server computing device is further to distally control, based on the treatment plan, the treatment apparatus used by the patient to perform the treatment plan.

[1180] Clause 43.4. The computer-implemented system of any preceding clause, wherein the certain parameter is a fee to be paid to a medical professional, a payment plan for the patient to pay off an amount of money owed, a plan of reimbursement, an amount of revenue to be paid to an insurance provider, or some combination thereof.

[1181] Clause 44.4. The computer-implemented system of any preceding clause, wherein the treatment plan is for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength training, endurance training, weight loss, weight gain, flexibility, pliability, or some combination thereof.

[1182] Clause 45.4. The computer-implemented system of any preceding clause, wherein the plurality of instructions comprises:

[1183] a plurality of exercises for the patient to perform, [1184] an order for the plurality of exercises,

[1185] a frequency for performing the plurality of exercises,

[1186] a diet regimen,

[1187] a sleep regimen,

[1188] a plurality of procedures to perform on the patient,

[1189] an order for the plurality of procedures,

[1190] a medication regimen,

[1191] a plurality of sessions for the patient, or [1192] some combination thereof.

[1193] Clause 46.4. The computer-implemented system of any preceding clause, wherein the server computing device is further to cause presentation, in real-time or near real-time during a telemedicine session with another computing device of the patient, of the treatment plan and the billing sequence on the computing device of a medical professional.

[1194] Clause 47.4. The computer-implemented system of any preceding clause, wherein the server computing device is further to:

[1195] receive, from the computing device, a first request pertaining to the billing sequence;

[1196] receive, from another computing device of an insurance provider, a second request pertaining to the billing sequence;

[1197] modify, based on the first request and the second request, the billing sequence to generate a modified billing sequence, such that the modified billing sequence results in funds being received sooner than had the billing sequence been implemented, an optimized total amount of the funds being received, an optimized number of payments being received, an optimized schedule for the funds being received, or some combination thereof. [1198] Clause 48.4. The computer-implemented system of any preceding clause, wherein the constraints further comprise constraints set forth in regulations, laws, or some combination thereof.

[1199] Clause 49.4. The computer-implemented system of any preceding clause, wherein the server computing device is further to transmit the treatment plan and the billing sequence to be presented on a second computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged.

[1200] Clause 50.4. A computer-implemented system, comprising:

[1201] a treatment apparatus configured to be manipulated by a patient while performing a treatment plan; [1202] a server computing device configured to execute an artificial intelligence engine to generate treatment plans for optimizing patient outcome and monetary amount generated, wherein the server computing device: [1203] receives a set of treatment plans that, when applied to patients, cause outcomes to be achieved by the patients;

[1204] receives a set of monetary value amounts associated with the set of treatment plans, wherein a respective monetary value amount of the set of monetary value amounts is associated with a respective treatment plan of the set of treatment plans;

[1205] receives a set of constraints, wherein the set of constraints comprises rules pertaining to billing codes associated with the set of treatment plans; [1206] generates, by the artificial intelligence engine, optimal treatment plans for a patient, wherein the generating is based on the set of treatment plans, the set of monetary value amounts, and the set of constraints, wherein each of the optimal treatment plans complies with the set of constraints and represents a patient outcome and an associated monetary value amount generated; and

[1207] transmits the optimal treatment plans to be presented on a computing device.

[1208] Clause 51.4. The computer-implemented system of any preceding clause, wherein the server computing device is further to:

[1209] receive a selection of an optimal treatment plan from the optimal treatment plans; and

[1210] control, based on the optimal treatment plan, a treatment apparatus while the patient uses the treatment apparatus.

[1211] Clause 52.4. The computer-implemented system of any preceding clause, wherein the optimal treatment plans are for habilitation, pre-habilitation, rehabilitation, post-habilitation, exercise, strength, pliability, flexibility, weight stability, weight gain, weight loss, cardiovascular, endurance, respiratory, or some combination thereof.

[1212] Clause 53.4. The computer-implemented system of any preceding clause, further comprising:

[1213] receiving a selection of a certain monetary value amount of the set of monetary value amounts; and [1214] generating, by the artificial intelligence engine, an optimal treatment plan based on the set of treatment plans, the certain monetary value amount, and the set of constraints, wherein the optimal treatment plan complies with the set of constraints and represents another patient outcome and the certain monetary value amount. [1215] Clause 54.4. The computer-implemented system of any preceding clause, wherein the set of treatment plans specifies a set of exercises to be performed by the patient using a treatment apparatus, and the method further comprises:

[1216] receiving a set of billing codes associated with the set of exercises; and [1217] correlating the set of billing codes with the rules.

[1218] Clause 55.4. The computer-implemented system of any preceding clause, further comprising:

[1219] receiving levels of risk associated with the set of treatment plans, wherein the generating the optimal treatment plans is also based on the levels of risk; and

[1220] transmitting the optimal treatment plans and the level of risks to be presented on the computing device of the medical professional.

[1221] Clause 56.4. The computer-implemented system of any preceding clause, wherein:

[1222] a first optimal treatment plan of the optimal treatment plans results in a first patient outcome and a first monetary value amount generated; and

[1223] a second optimal treatment plan of the optimal treatment plans results in a second patient outcome and a second monetary value amount generated, wherein the second patient outcome is better than the first patient outcome and the second revenue value generated is greater than the first monetary value amount generated. [1224] Clause 57.4. The computer-implemented system of any preceding clause, wherein the set of constraints further comprises constraints set forth in regulations, laws, or some combination thereof.

[1225] Clause 58.4. The computer-implemented system of any preceding clause, further comprising transmitting the optimal treatment plans to be presented on a computing device of the patient in real-time or near real-time during a telemedicine session in which the computing device of the medical professional is engaged.

[1226] The above discussion is meant to be illustrative of the principles and various embodiments of the present disclosure. Numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

[1227] The various aspects, embodiments, implementations, or features of the described embodiments can be used separately or in any combination. The embodiments disclosed herein are modular in nature and can be used in conjunction with or coupled to other embodiments.

[1228] Consistent with the above disclosure, the examples of assemblies enumerated in the following clauses are specifically contemplated and are intended as a non-limiting set of examples.

Claims

CLAIMS What is claimed is:

1. A computer-implemented system for processing medical claims, comprising: a medical device configured to be manipulated by a user while the user performs a treatment plan; a patient interface associated with the medical device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session; and a processor configured to: during the telemedicine session, receive device-generated information from the medical device; using the device -generated information, determine device-based medical coding information; and transmit the device-based medical coding information to a claim adjudication server.

2. The computer-implemented system of claim 1, wherein, during the telemedicine session, the device-generated information is generated by the medical device.

3. The computer-implemented system of claim 1, wherein, using the device-generated information, the processor is further configured to determine device-based medical result information.

4. The computer-implemented system of claim 3, wherein the processor is further configured to transmit the device-based medical result information to a patient notes database.

5. The computer-implemented system of claim 1, wherein the processor is further configured to transmit the device-based medical coding information to an electronic medical records database.

6. The computer-implemented system of claim 1 , wherein the processor is further configured to : receive reviewed medical coding information from an electronic medical records database, wherein, using the reviewed medical coding information and the device-based medical coding information, the processor is further configured to determine a match indicator; and transmit the match indicator to the claim adjudication server.

7. A system for processing medical claims, comprising: a processor configured to: receive device-generated information from a medical device; using the device -generated information, determine device-based medical coding information; and transmit the device-based medical coding information to a claim adjudication server.

8. The system of claim 7, wherein the device-generated information is generated by the medical device.

9. The system of claim 7, wherein, using the device-generated information, the processor is further configured to determine device-based medical result information.

10. The system of claim 9, wherein the processor is further configured to transmit the device- based medical result information to a patient notes database.

11. The system of claim 7, wherein the processor is further configured to transmit the device- based medical coding information to an electronic medical records database.

12. The system of claim 7, wherein the processor is further configured to receive reviewed medical coding information from an electronic medical records database.

13. The system of claim 12, wherein, using the reviewed medical coding information and the device-based medical coding information, the processor is further configured to determine a match indicator.

14. The system of claim 13, wherein the processor is further configured to transmit the match indicator to the claim adjudication server.

15. The system of claim 7, further comprising a memory device operatively coupled to the processor, wherein the memory device stores instructions, and wherein the processor is configured to execute the instructions.

16. A method for a clinic server processing medical claims, comprising: receiving device-generated information from a medical device; using the device-generated information, determining device-based medical coding information; and transmitting the device-based medical coding information to a claim adjudication server.

17. The method of claim 16, wherein the device-generated information is generated by the medical device.

18. The method of claim 16, further comprising using the device-generated information to determine device-based medical result information.

19. The method of claim 18, further comprising transmitting the device-based medical result information to a patient notes database.

20. The method of claim 16, further comprising transmitting the device-based medical coding information to an electronic medical records database.

21. The method of claim 16, further comprising receiving reviewed medical coding information from an electronic medical records database.

22. The method of claim 21, further comprising using the reviewed medical coding information and the device-based medical coding information to determine a match indicator.

23. The method of claim 22, further comprising transmitting the match indicator to the claim adjudication server.

24. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processor to: receive device-generated information from a medical device; using the device-generated information, determine device-based medical coding information; and transmit the device-based medical coding information to a claim adjudication server.

25. The tangible, non-transitory computer-readable medium of claim 24, wherein the device generated information is generated by the medical device.

26. The tangible, non-transitory computer-readable medium of claim 24, wherein, using the device-generated information, the instructions further cause the processor to determine device-based medical result information.

27. The tangible, non-transitory computer-readable medium of claim 26, wherein the instructions further cause the processor to transmit the device-based medical result information to a patient notes database.

28. The tangible, non-transitory computer-readable medium of claim 24, wherein the instructions further cause the processor to transmit the device-based medical coding information to an electronic medical records database.

29. The tangible, non-transitory computer-readable medium of claim 24, wherein the processor is further configured to receive reviewed medical coding information from an electronic medical records database.

30. The tangible, non-transitory computer-readable medium of claim 29, wherein, using the reviewed medical coding information and the device-based medical coding information, the instructions further cause the processor to determine a match indicator.