JP7298053B2 - Methods and systems for using sensor data from rehabilitation or exercise equipment to treat patients via telemedicine - Google Patents

Description

CROSS REFERENCES TO RELATED APPLICATIONS This application is based on U.S. Provisional Patent Application No. 6, entitled "Method and System for Using Sensor Data from Rehabilitation or Exercise Equipment to Treat Patients via Telemedicine," filed July 6, 2020. 3/ 048,456, and priority to and benefit from U.S. Provisional Patent Application No. 62/910,232, entitled "Telemedicine for Orthopedic Treatment," filed October 3, 2019; Alleged, Continuing Patent Application claiming priority to and benefit from U.S. Patent Application Serial No. 17/021,895, entitled "Telemedicine for Orthopedic Treatment," filed September 15, 2020, January 2021 PRIORITY AND INTEREST IN U.S. patent application Ser. with the claim that , the entire disclosures of which are incorporated herein by reference.

This application is part of U.S. patent application Ser. priority and interest of , the entire disclosure of which is incorporated herein by reference.

This application supersedes U.S. Provisional Patent Application No. 63/088,657, entitled "Method and System Using Artificial Intelligence to Monitor User Characteristics During a Telemedicine Session," filed October 7, 2020. rights and interests claiming and claiming priority to and benefit from U.S. Provisional Patent Application No. 62/910,232, entitled "Telemedicine for Orthopedic Treatment," filed October 3, 2019, on September 15, 2020 A continuation-in-part patent application claiming priority to and benefit from U.S. patent application Ser. No. 17/147,439 entitled "Methods and Systems Using Artificial Intelligence to Monitor User Characters During a Telemedicine Session", the entire disclosure of which is hereby incorporated by reference. statement incorporated into.

This application is part of U.S. Provisional Patent Application Serial No. 63/104,716, entitled "Method and System for Using Virtual Avatars Associated with Medical Professionals During Exercise Sessions," filed October 23, 2020. priority and interest claiming and claiming priority to and benefit from U.S. Provisional Patent Application No. 62/910,232, entitled "Telemedicine for Orthopedic Treatment," filed October 3, 2019, on September 15, 2020 A continuation-in-part patent application claiming priority to and benefit from U.S. patent application Ser. No. 17/147,211 entitled "Methods and Systems for Using Virtual Avatars Associated with Medical Professionals During Exercise Sessions", the entire disclosure of which is hereby incorporated by reference. statement incorporated into.

Telemedicine assistance, or telemedicine, can assist patients in carrying out various aspects of a rehabilitation regimen for a part of the body. A patient can use the patient interface in communication with the assistant interface to receive telemedicine assistance via voice and/or audiovisual communication.

Aspects of the disclosed embodiment include a method that includes receiving treatment data regarding a user implementing a treatment plan using a treatment device. The therapy data includes at least one of characteristics of the user, measurement information about the user while the user is using the therapy device, characteristics of the therapy device, and a treatment plan. The method also includes using the therapy data to generate therapy information and writing the therapy information to an associated memory for access at the healthcare provider's computing device. The method also includes communicating with an interface at the healthcare provider's computing device, the interface configured to receive treatment plan input, communicating and at least one modification of the treatment plan. and modifying the treatment plan in response to receiving a treatment plan input comprising:

Aspects of the disclosed embodiment provide a computer-implemented system including a therapy device configured to be operated by a patient while performing an exercise session and a patient interface configured to receive a virtual avatar. include. The patient interface comprises an output device configured to present a virtual avatar. A virtual avatar guides the patient through an exercise session using a virtual representation of the therapy device. A virtual avatar is associated with a medical worker. The computer-implemented system provides a virtual avatar of the patient to the patient interface; receives a message from the patient interface regarding the triggering event, the message including a severity level of the triggering event; Presenting a virtual avatar on the patient interface in response to determining whether the severity level of the event exceeds the threshold severity level and determining that the severity level of the triggering event exceeds the threshold severity level. with the presentation of the multimedia feed from the healthcare worker's computing device.

Aspects of the disclosed embodiments include methods for providing optimal treatment plans for use with treatment devices by artificial intelligence engines. The method is receiving from a data source clinical information about the results of performing a particular treatment regimen for people with particular characteristics using a therapy device, the clinical information having a first data format. converting a portion of clinical information from a first data format into a medical description language for use by an artificial intelligence engine; a portion of the clinical information described by the medical description language; and a patient Determining the optimal treatment plan for a patient to follow in order to achieve a desired outcome using the therapy device, based on multiple characteristics of the patient and the optimal treatment plan to be presented on the healthcare provider's computing device. and providing a suitable treatment plan.

Aspects of the disclosed embodiments include methods for providing optimal treatment plans for use with treatment devices by artificial intelligence engines. The method is receiving from a data source clinical information about the results of performing a particular treatment regimen for people with particular characteristics using a therapy device, the clinical information having a first data format. converting a portion of clinical information from a first data format into a medical description language for use by an artificial intelligence engine; a portion of the clinical information described by the medical description language; and a patient Determining the optimal treatment plan for a patient to follow in order to achieve a desired outcome using the therapy device, based on multiple characteristics of the patient and the optimal treatment plan to be presented on the healthcare provider's computing device. and providing a suitable treatment plan.

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 instructions to perform any of the methods, acts, or steps described herein.

Another aspect of the disclosed embodiments is a tangible, non-transitory instruction that, when executed, causes a processing device to perform any of the methods, acts, or steps described herein. computer readable medium.

The present disclosure is best understood from the following detailed description when read in conjunction with the accompanying drawings. It is noted that, according to common practice, the various features of the drawings are not to scale. Conversely, the dimensions of various features are arbitrarily exaggerated or reduced for clarity.

1 illustrates generally a block diagram of one embodiment of a computer-implemented system for managing treatment plans, according to the principles of the present disclosure; FIG. 1 illustrates generally a perspective view of one embodiment of a therapeutic device, in accordance with the principles of the present disclosure; FIG. FIG. 3 generally illustrates a perspective view of a pedal of the therapeutic device of FIG. 2, according to the principles of the present disclosure; FIG. 3 generally illustrates a perspective view of a person using the treatment device of FIG. 2, according to the principles of the present disclosure; FIG. 11 illustrates generally one exemplary embodiment of an assistant interface summary display in accordance with the principles of the present disclosure; FIG. FIG. 4 illustrates generally an exemplary block diagram for training a machine learning model to output a treatment plan for a patient based on data regarding the patient, in accordance with the principles of the present disclosure; FIG. 11 illustrates generally one embodiment of an assistant interface summary display presenting recommended and excluded treatment plans in real-time during a telemedicine session, according to the principles of the present disclosure; FIG. FIG. 11 is a general illustration of one embodiment of an assistant interface overview display that presents a recommended treatment plan that has changed as a result of changes in patient data in real-time during a telemedicine session, in accordance with the principles of the present disclosure; FIG. To modify a patient's treatment plan based on treatment data received while a user is using the treatment devices of FIG. 2, and to control at least one treatment device based on the modifications, in accordance with the principles of the present disclosure. 1 is a flow diagram generally illustrating the method of . To modify a patient's treatment plan based on treatment data received while a user is using the treatment devices of FIG. 2, and to control at least one treatment device based on the modifications, in accordance with the principles of the present disclosure. FIG. 4 is a flow diagram generally illustrating an alternative method of . To modify a patient's treatment plan based on treatment data received while a user is using the treatment devices of FIG. 2, and to control at least one treatment device based on the modifications, in accordance with the principles of the present disclosure. FIG. 4 is a flow diagram generally illustrating an alternative method of . A computer system is generally illustrated in accordance with the principles of the present disclosure. 1 shows a block diagram of one embodiment of a computer-implemented system for managing treatment plans according to the present disclosure; FIG. 1 shows a perspective view of one embodiment of a treatment device according to the present disclosure; FIG. 15 shows a perspective view of a pedal of the therapeutic device of FIG. 14 according to the present disclosure; FIG. 15 shows a perspective view of a person using the treatment device of FIG. 14 according to the present disclosure; FIG. 4 illustrates an exemplary embodiment of an overview display of an assistant interface in accordance with the present disclosure; FIG. 11 illustrates an exemplary embodiment of an assistant interface summary display presenting recommended optimal treatment plans and excluded treatment plans in real-time during a telemedicine session in accordance with the present disclosure; FIG. 1 illustrates an exemplary embodiment of a server that converts clinical information into a medical descriptive language for processing by an artificial intelligence engine according to the present disclosure; 1 illustrates an exemplary embodiment of a method for recommending an optimal treatment regimen according to the present disclosure; 1 illustrates an exemplary embodiment of a method for transforming clinical information into a medical descriptive language according to the present disclosure; 1 illustrates an exemplary computer system according to this disclosure; 1 illustrates generally a block diagram of one embodiment of a computer-implemented system for managing treatment plans, according to the principles of the present disclosure; FIG. 1 illustrates generally a perspective view of one embodiment of a therapeutic device, in accordance with the principles of the present disclosure; FIG. 25 generally illustrates a perspective view of a pedal of the therapeutic device of FIG. 24, according to the principles of the present disclosure; FIG. 25 generally illustrates a perspective view of a person using the treatment device of FIG. 24, according to the principles of the present disclosure; FIG. FIG. 11 illustrates generally one exemplary embodiment of an assistant interface summary display in accordance with the principles of the present disclosure; FIG. FIG. 4 illustrates generally an exemplary block diagram for training a machine learning model to output a treatment plan for a patient based on data regarding the patient, in accordance with the principles of the present disclosure; FIG. 11 illustrates generally one embodiment of an assistant interface summary display presenting recommended and excluded treatment plans in real-time during a telemedicine session, according to the principles of the present disclosure; FIG. FIG. 11 is a general illustration of one embodiment of an assistant interface overview display that presents a recommended treatment plan that has changed as a result of changes in patient data in real-time during a telemedicine session, in accordance with the principles of the present disclosure; FIG. 24 generally illustrates a method for monitoring characteristics of a user while the user is using the therapy device based on therapy data received while the user is using the therapy device of FIG. 24, according to principles of the present disclosure; It is a flow diagram to do. Generally, alternative methods for monitoring characteristics of a user while the user is using the therapy device based on therapy data received while the user is using the therapy device of FIG. 24 according to the principles of the present disclosure FIG. 4 is an exemplary flow diagram; Generally, alternative methods for monitoring characteristics of a user while the user is using the therapy device based on therapy data received while the user is using the therapy device of FIG. 24 according to the principles of the present disclosure FIG. 4 is an exemplary flow diagram; FIG. 4 is a flow diagram generally illustrating a method for receiving a selection of an optimal treatment plan and controlling a treatment device during use of the treatment device by a patient based on the optimal treatment plan according to the present disclosure; . 1 illustrates generally a computer system in accordance with the principles of the disclosure; 1 shows a block diagram of one embodiment of a computer-implemented system for managing treatment plans according to the present disclosure; FIG. 1 shows a perspective view of one embodiment of a treatment device according to the present disclosure; FIG. 38 shows a perspective view of a pedal of the therapeutic device of FIG. 37 according to the present disclosure; FIG. 38 shows a perspective view of a person using the treatment device of FIG. 37, according to the present disclosure; FIG. 4 illustrates an exemplary embodiment of an overview display of an assistant interface in accordance with the present disclosure; FIG. 4 illustrates an exemplary block diagram for training a machine learning model to output a treatment plan for a patient based on data about the patient, according to the present disclosure; FIG. 11 illustrates one embodiment of a patient interface overview display presenting a virtual avatar guiding the patient through an exercise session in accordance with the present disclosure; FIG. FIG. 11 illustrates one embodiment of an overview display of an assistant interface that receives patient notifications and enables an assistant to initiate a telemedicine session in real time according to the present disclosure; FIG. FIG. 11 illustrates one embodiment of a summary display of a patient interface presenting a feed of a healthcare professional replacing a virtual avatar in real-time during a telemedicine session in accordance with the present disclosure; FIG. 4 illustrates one exemplary embodiment of a method for replacing a virtual avatar with a healthcare worker's feed based on a triggering event that occurs in accordance with the present disclosure; 1 illustrates an exemplary embodiment of a method for providing virtual avatars according to the present disclosure; 1 illustrates an exemplary computer system according to this disclosure;

Notation and Nomenclature Various terms are used to refer to particular system components. Different companies may refer to components by different names, and this document is not intended to distinguish between components that have different names but do not function. In the following discussion and claims, the terms "including" and "comprising/comprising" are used in an open-ended manner and thus include, but are not limited to, ". ” should be construed to mean ”. Also, the term "couple" or "couples" is intended to mean either an indirect connection or a direct connection. Thus, when a first device couples to a second device, the connection may be through a direct connection or through an indirect connection through other devices and connections.

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 plural forms as well, unless the context clearly indicates otherwise. It is to be understood that method steps, processes, and actions described herein require their performance in the particular order necessarily discussed or illustrated, unless specifically identified as an order of performance. shouldn't. It should also be appreciated that additional or alternative steps may be used.

Although the terms first, second, third, etc. may be used herein to describe various elements, components, regions, layers and/or sections, these elements, configurations Elements, regions, layers, and/or sections should not be limited by these terms. These terms may only be 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, as used herein, do not imply any permutation or order unless explicitly indicated by the context. Thus, a first element, component, region, layer or section discussed below could be referred to as a second element, component, region, layer or section without departing from the teachings of the exemplary embodiments. can be called When used with an enumeration of items, the phrase "at least one of" means that different combinations of one or more of the enumerated items may be used, and only one item in the enumeration is required. means to get 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 listing of items, means that there can be one item or any suitable number of items greater than one.

Spatial, such as "inner", "outer", "directly below", "lower", "lower", "upper", "upper", "top", "bottom" Terms relative to may be used herein. These spatially relative terms may be used to facilitate discussion to describe the relationship of one element or feature to another illustrated in the figures. Spatially relative terms may also be intended to encompass different orientations or operations of the device in use in addition to the orientation depicted in the figures. For example, if the device in the figures were turned over, elements described as "below" or "beneath" other elements or features would be oriented "above" the other elements or features. . Thus, the exemplary term "lower" can encompass both upward and downward orientations. The device may be otherwise oriented (rotated 90 degrees or other orientations) and the spatially relative descriptions used herein may be interpreted accordingly.

A "treatment plan" can include one or more treatment protocols, each treatment protocol including one or more treatment sessions. Each treatment session includes several session durations, each session duration including specific exercises for treating a patient's body part. For example, the treatment plan for postoperative rehabilitation after knee surgery was an initial treatment protocol with twice daily stretching sessions for the first three days after surgery and four times daily starting on the fourth day after surgery. and a more intensive treatment protocol with active exercise sessions performed. The treatment plan may also include information regarding medical procedures to be performed on the patient, the patient's treatment protocol using the treatment device, the patient's dietary regimen, the patient's drug regimen, the patient's sleep regimen, additional regimens, or any combination thereof. may include A treatment plan may also include one or more training protocols, such as a strength training protocol, a range of motion training protocol, a cardiovascular training protocol, an endurance training protocol, and the like. Each training protocol can include one or more training sessions comprising several training session durations, each session duration being one of strength training, range of motion training, cardiovascular training, endurance training, etc. Including specific exercises that target:

Terms such as telemedicine, telehealth, telemed, teletherapeutic, telemedicine, remote medicine, etc. may be used interchangeably herein.

The term “augmented reality” includes augmented reality, virtual reality, mixed reality, immersive reality, or combinations of the above (e.g., immersive augmented reality, mixed augmented reality, virtual and augmented immersive reality, etc.). It may include a user experience that includes one or more of:

The term "augmented reality" can refer, but is not limited to, an interactive user experience that combines elements of a real-world environment with computer-generated components perceivable by a user to provide an enhanced environment.

The term "virtual reality" can refer, but is not limited to, an interactive simulated user experience that provides an enhanced environment perceptible by a user, such enhanced environment mimicking a real-world environment. It can be similar or it can be different.

The term "mixed reality" may refer to an interactive user experience that combines aspects of augmented reality with aspects of virtual reality to provide a mixed reality environment that is perceivable by the user.

The term "immersive reality" uses virtual and/or augmented reality images, sounds, and other stimuli to immerse the user in an interactive simulated experience to a certain possible extent (e.g., partial immersion or full immersion). It can refer to an interactive, simulated user experience. For example, in some embodiments, to a certain extent possible, users experience one or more aspects of immersive reality as naturally as they typically experience corresponding aspects of the real world. do. Additionally or alternatively, the immersive reality experience may include actors, narrative elements, themes (eg, entertainment themes or other suitable themes), and/or other suitable characteristics of the elements.

The term "body halo" can refer to one or more hardware components, such one or more components being one or more platforms, one or more body supports or cages, one or more one or more back supports or back engaging mechanisms; one or more leg or foot engaging mechanisms; one or more arm or hand engaging mechanisms; one or more head engaging mechanisms; It may include other suitable hardware components, or combinations thereof.

As used herein, the term "enhanced environment" refers to the enhanced environment in its entirety, at least one aspect of the enhanced environment, two or more aspects of the enhanced environment, or any suitable number of enhanced environments. It can refer to aspects.

As used herein, the term "threshold" and/or the term "range" refer to percentages, absolute values, units of measure, differential values, numerical quantities, or other suitable representations of one or more values. It may contain one or more values represented as

The term "optimal treatment plan" is based on a particular parameter or combination of two or more parameters, such as, but not limited to, the amount of monetary value generated by the treatment plan and/or claim sequence. , may refer to optimizing a treatment plan, wherein the amount of monetary value is an absolute amount in dollars or another currency, net present value (NPV) or any other measure, from the treatment plan and/or claim sequence. resulting patient outcomes, fees paid to healthcare professionals, patient payment plans to pay off amounts owed or portions thereof, reimbursement plans, amounts of revenues owed to insurance or third party providers, profits; or other amount of monetary value, or some combination thereof.

Real time may refer to 2 seconds or less. Near real-time can refer to any interaction of a time sufficiently short to allow two individuals to engage in interaction via such a user interface, generally less than 10 seconds, but will exceed 2 seconds.

Any of the systems and methods described in this disclosure can be used in connection with rehabilitation. Rehabilitation includes cardiac rehabilitation, rehabilitation from stroke, multiple sclerosis, Parkinson's disease, myasthenia gravis, Alzheimer's disease, any other neurodegenerative or neuromuscular disease, brain injury, spinal cord injury, spinal cord disease, joint injury , joint diseases, etc. Rehabilitation involves improving blood and lymphatic flow, working with the brain and nervous system to control and act on traumatized areas to increase the rate of healing, pain (joint pain and reversing or reducing muscle soreness), reversing or reducing stiffness, restoring range of motion, promoting cardiovascular activity to stimulate the release of pain-blocking hormones, or enriching with oxygen Muscle contraction may also be involved to promote good blood flow and help to feel overall well-being. Rehabilitation includes individuals of average height in fairly good physical condition with no substantial deformities, as well as those who are elderly, obese, subject to disease processes, injured and/or mobile. It can be provided to individuals who are more typically in need of rehabilitation, such as those with severe gait limitations. Unless otherwise specified, rehabilitation should be understood to include preoperative rehabilitation (also referred to as "prehabilitation" or "prefab"). Preoperative rehabilitation may be used as a preventive treatment or as a preoperative or therapeutic pretreatment. Preoperative rehabilitation includes, but is not limited to, preventing or reducing risk of injury (e.g., before injury occurs), improving recovery time after surgery, improving strength after surgery, or treating injury. , functional impairment, or other negative consequences of any external or internal surgical or non-surgical treatment of the patient's body. performed by or on the patient for any of the ) can contain any action. For example, a mastectomy may require preoperative rehabilitation to strengthen the muscles or muscle groups directly or indirectly affected by the mastectomy. As further non-limiting examples, removal of intestinal tumors, hernia repair, open-heart surgery, or other procedures performed on visceral organs or structures, whether or not to repair those organs or structures. including, but not limited to, the skull or face, abdomen, ribs, and/or thoracic cavity, and all joints and appendages, whether or not to resect portions of or treat them. It may require cutting, dissecting, and/or damaging multiple muscles and muscle groups in or around them. Preoperative rehabilitation can improve a patient's speed of recovery, extent of quality of life, level of pain, etc. in all of the above procedures. In one embodiment of preoperative rehabilitation, the preoperative or non-preoperative treatment may include one or more exercise sets for the patient to perform prior to such treatment or therapy. To accommodate elective surgery such as knee replacement, one or more exercise sets may be required to be performed. Patients prepare areas of their body for surgical procedures by performing one or more exercise sets, thereby strengthening muscle groups, improving existing muscle memory, and reducing pain. may reduce stiffness, establish new muscle memory, increase mobility (ie, improve range of motion), improve blood flow, and the like.

The following discussion is directed to various embodiments of the disclosure. While one or more of these embodiments may be preferred, the disclosed embodiments should not be construed as limiting the scope of this disclosure, including the claims, and may be used otherwise. Neither should. In addition, those skilled in the art will appreciate that the following description has broad application, and discussion of any embodiment is meant only to exemplify that embodiment, and the scope of this disclosure, including the claims, is It will be understood that no limitation to the embodiments is intended to be implied.

specific characteristics (e.g., vital signs or other measurements, performance, demographic, geographic, diagnostic, measurement- or test-based, medical history, etiology, cohort-related, differential diagnosis, surgical, physical Determining a treatment regimen for a patient with therapeutic, pharmacological, and other recommended treatments can be a technically challenging problem. For example, a large amount of information can be considered when determining a treatment plan, which can lead to inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitation setting, some of the many pieces of information considered may include patient characteristics, such as personal information, performance information, and measurement information. Personal information may include, for example, demographic, psychological scientific or other information. The performance information may include, for example, elapsed time using the treatment device, amount of force exerted on the portion of the treatment device, range of motion achieved with the treatment device, speed of movement of the portion of the treatment device, multiple pains using the treatment device It may include indications of levels, or some combination thereof. Measurement information may include, for example, vital signs, respiration rate, heart rate, temperature, blood pressure, or some combination thereof. It may be desirable to process the characteristics of a large number of patients, the treatment plans to be administered to those patients, and the results of the treatment plans for those patients.

Furthermore, another technical problem may involve remotely treating a patient via a computing device during a telemedicine or telehealth session from a location different from where the patient is located. An additional technical problem is to control or allow control of the therapeutic device used by the patient at the location where the patient is located from different locations. In many cases, when a patient undergoes rehabilitation surgery (e.g., knee surgery), health care providers use to carry out treatment protocols at the patient's home or any mobile or temporary residence. A therapeutic device may be prescribed to the patient. Health care providers can refer to doctors, physician assistants, nurses, chiropractors, dentists, physical therapists, acupuncturists, physical trainers, coaches, personal trainers, and the like. A health care provider may refer to any person having a license, license, position, etc., in fields such as medicine, physical therapy, rehabilitation, and the like.

Monitoring the actual progress of the patient using the treatment device by the provider when the provider is located at a different location than the patient and the treatment device ), it can be technically difficult to modify the treatment plan according to the patient's progress, to adapt the treatment device to the patient's personal characteristics as the patient implements the treatment plan, and the like.

Thus, systems such as those described herein that use sensor data to modify the treatment plan and/or adapt the treatment device while the patient uses the treatment device to implement the treatment plan. and methods may be desirable.

In some embodiments, the systems and methods described herein can be configured to receive treatment data about a user while the user is using the treatment device to implement a treatment plan. The users may include patient-users or persons performing various exercises using the therapy device. A treatment plan may correspond to a rehabilitation treatment plan, a preoperative rehabilitation treatment plan, an exercise treatment plan, or any other suitable treatment plan. The therapy data may include various characteristics of the user, various measured information about the user while the user is using the therapy device, various characteristics of the therapy device, treatment plans, other suitable data, or combinations thereof.

In some embodiments, at least a portion of the treatment data is configured to sense various characteristics of the treatment device and/or measurement information of the user while the user uses the treatment device to implement the treatment plan. may correspond to the sensor data of the sensor. Additionally or alternatively, at least a portion of the treatment data may be obtained from sensors associated with the wearable device configured to sense user measurement information while the user uses the treatment device to implement the treatment plan. of sensor data.

The various properties of the treatment device may include one or more settings of the treatment device, the current number of rotations per time period (e.g., one minute, etc.) of a rotating member (e.g., wheel, etc.) of the treatment device, the resistance setting of the treatment device. , other suitable characteristics of the treatment device, or combinations thereof. The measurement information may include one or more of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's temperature, the user's blood pressure, other suitable measurement information of the user, or combinations thereof.

In some embodiments, the systems and methods described herein may be configured to generate treatment information using treatment data. The treatment information is formatted for a treatment plan by a user during use of the treatment device such that the treatment data is presentable on a computing device of a healthcare provider or medical personnel responsible for implementing the treatment plan by the user. may include a summary of the implementation of The terms "medical provider" and "medical practitioner" may be used interchangeably herein. A health care provider or health professional may be a health care professional (e.g., a doctor, nurse, therapist, etc.), an exercise professional (e.g., a coach, trainer, dietitian, etc.), or a medical and exercise attribute. may include another professional (eg, such as an exercise physiologist, physical therapist, occupational therapist, etc.) who shares at least one of the. As used herein, but not limited to the foregoing, a healthcare provider or practitioner may refer to a human, a robot, a virtual assistant, a virtual assistant in virtual and/or augmented reality, or a software program, integrated It may be an artificial intelligence entity including software and hardware, or hardware alone.

The systems and methods described herein write treatment information to associated memory for access at the healthcare provider's computing device and/or write treatment information to the healthcare provider's computing device. can be configured to provide For example, the systems and methods described herein can be configured to provide treatment information to an interface configured to present treatment information to a healthcare provider. The interface may include a graphical user interface configured to provide treatment information and receive input from a healthcare provider. The interface may be activated and/or driven by a text input field, a drop-down selection input field, a radio button input field, a virtual switch input field, a virtual lever input field, by voice, by touch, by touch, by biometrics, or otherwise. may include one or more input fields, such as a default input field, other suitable input fields, or combinations thereof.

In some embodiments, a healthcare provider may review the treatment information and determine whether to modify one or more characteristics of the treatment plan and/or treatment device. For example, a healthcare provider may review the treatment information and compare the treatment information to treatment plans being followed by the user.

The healthcare provider may provide (i) expected information about the user while the user uses the treatment device to perform the treatment plan; user's measurement information (eg, indicated by treatment information). The expected information may include one or more of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, the user's blood pressure, other suitable information of the user, or combinations thereof. The health care provider indicates that if one or more portions or portions of the measured information are within tolerances associated with one or more corresponding portions or portions of the expected information, then the treatment plan will have the desired effect. It can be determined that Conversely, the health care provider agrees that if one or more portions or portions of the measured information are outside the acceptable range associated with one or more corresponding portions or portions of the expected information, the treatment plan will be terminated. It can be determined that the desired effect is not given.

For example, the health care provider may indicate that the blood pressure values (eg, systolic, diastolic, and/or pulse pressure) associated with the user (eg, indicated by measurement information) while the user is using the therapy device are , is within an acceptable range (eg, plus or minus 1%, plus or minus 5%, or any suitable range) of the expected blood pressure value indicated by the expected information. A healthcare provider may determine that a treatment regimen is providing the desired effect if the blood pressure values associated with the user while the user is using the therapy device are within the expected blood pressure value range. Conversely, a healthcare provider may determine that a treatment regimen is not having the desired effect if the blood pressure values associated with the user while the user is using the therapy device are outside the range of expected blood pressure values. obtain.

In some embodiments, the healthcare provider may compare the expected characteristics of the treatment device while the user uses the treatment device to implement the treatment plan with the characteristics of the treatment device indicated by the treatment information. . For example, the healthcare provider may compare the expected resistance setting of the therapy device to the actual resistance setting of the therapy device indicated by the therapy information. The healthcare provider determines that the user is performing the treatment plan properly if the actual characteristics of the treatment device indicated by the treatment information are within the corresponding ones of the expected characteristics of the treatment device. can be determined. Conversely, the health care provider may not allow the user to properly perform the treatment plan if the actual characteristics of the treatment device as indicated by the treatment information fall outside the corresponding ones of the expected characteristics of the treatment device. It can be determined that it does not.

If the health care provider determines that the treatment information indicates that the user is performing the treatment plan properly and/or that the treatment plan is providing the desired effect, the health care provider may use the treatment plan or It may be determined not to modify one or more properties of the therapeutic device. Conversely, while the user is using the treatment device to implement the treatment plan, the health care provider may determine that the treatment information indicates that the user is not or has not properly implemented the treatment plan; If/or determines that the treatment regimen does not provide or indicates that it has not provided the desired effect, the healthcare provider may choose to modify one or more characteristics of the treatment regimen and/or treatment device. can decide.

In some embodiments, if the healthcare provider decides to modify one or more characteristics of the treatment plan and/or treatment device, the healthcare provider interacts with the interface to modify the treatment plan and /or may provide treatment plan input that indicates one or more modifications to one or more properties of the treatment device. For example, a healthcare provider may use the interface to provide input indicating an increase or decrease in the resistance setting of the therapy device, or other suitable modification to one or more characteristics of the therapy device. Additionally or alternatively, a healthcare provider may use the interface to provide input indicating modifications to the treatment plan. For example, a healthcare provider may use the interface to provide input indicating an increase or decrease in the amount of time the user is required to use the treatment device according to the treatment plan, or other suitable modifications to the treatment plan. may be provided.

In some embodiments, the systems and methods described herein can be configured to modify the treatment plan based on one or more modifications indicated by the treatment plan inputs. Additionally or alternatively, the systems and methods described herein modify one or more characteristics of the treatment device based on the modified at least one aspect of the treatment plan and/or treatment plan inputs. It may be configured as For example, the treatment plan input may indicate modifying one or more characteristics of the treatment device, and/or the treatment plan may indicate that the user may modify the treatment plan to achieve the desired outcome of the modified treatment plan. May require or indicate adjustment of the device.

In some embodiments, the systems and methods described herein can be configured to receive subsequent treatment data about the user while the user uses the treatment device to implement the treatment plan. For example, after a healthcare provider modifies a treatment plan and/or provides input controlling one or more characteristics of a treatment device, a user may use the treatment device to implement the modified treatment plan. can keep doing Subsequent treatment data may correspond to treatment data generated while the user uses the treatment device to implement the modified treatment plan. In some embodiments, subsequent treatment data is provided to the user after the healthcare provider receives the treatment information and determines not to modify the treatment plan and/or control one or more characteristics of the treatment device. may correspond to treatment data generated while continuing to implement the treatment plan using the treatment device.

Based on subsequent treatment plan input received from the healthcare provider's computing device, the systems and methods described herein further modify the treatment plan and/or modify one or more characteristics of the treatment device. may be configured to control the Subsequent treatment plan inputs may correspond to inputs provided by a healthcare provider at the interface in response to receiving and/or reviewing subsequent treatment information corresponding to subsequent treatment data. The systems and methods described herein provide therapy information to healthcare providers based on therapy data received continuously and/or periodically from the sensors described herein or other suitable sources. of computing devices on a continuous and/or periodic basis.

The healthcare provider may continuously or periodically receive and/or review the treatment information while the user uses the treatment device to implement the treatment plan. Based on one or more trends indicated by the treatment information received on an ongoing and/or periodic basis, the healthcare provider may decide whether to modify the treatment plan and/or determine one or more characteristics of the treatment device. can be determined whether to control For example, the one or more trends may be an increase in heart rate or other suitable indication that the user is not performing the treatment plan properly and/or that the user's implementation of the treatment plan is not having the desired effect. can show a trend.

In some embodiments, the systems and methods described herein use artificial intelligence and/or machine learning to assign patients to cohorts and, based on the assignments during adaptive telemedicine sessions, to treat treatment devices. can be configured to dynamically control the In some embodiments, multiple treatment devices may be provided to the patient. The treatment device may be used by the patient to carry out the treatment plan at any suitable location, including the patient's home, gym, rehabilitation center, hospital, or permanent or temporary residence.

In some embodiments, the therapy device may be communicatively coupled to the server. Patient characteristics, including treatment data, may be collected before, during, and/or after the patient implements a treatment plan. For example, personal information, performance information, and measurement information may be collected before, during, and/or after a person performs a treatment plan. The results of performing each exercise (eg, improved performance or reduced performance) can be collected from the treatment device throughout the treatment plan and after the treatment plan has been performed. Treatment device parameters, settings, configurations, etc. (eg, pedal position, amount of resistance, etc.) may be collected before, during, and/or after the treatment plan is implemented.

Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be time-stamped and correlated with a particular step of the treatment plan. Such techniques can help determine which steps in the treatment plan lead to the desired outcome (e.g., improvement in strength, range of motion, etc.) and which steps lead to reduced recovery (e.g., continue exercising after 3 minutes). may allow one to determine whether a condition actually slows or impairs recovery.

The data may be transferred to the treatment device and/or any suitable computing device (e.g., the computing device described herein) over time as the patient performs various treatment regimens using the treatment device. (computing device into which personal information is entered, such as an interface, clinician interface, patient interface, etc.). Data that may be collected may include patient characteristics, treatment regimens administered by the patient, results of treatment regimens, any of the data described herein, any other suitable data, or combinations thereof. .

In some embodiments, data may be processed to group specific people into cohorts. People may be grouped by people who have certain or selected similar characteristics, treatment regimens, and results of implementing treatment regimens. For example, athletes without a medical condition who implement a treatment regimen (e.g., use a treatment device for 30 minutes a day, five times a week for three weeks) and who fully recover may receive first can be grouped into cohorts of Older adults classified as obese and following a treatment regimen (e.g., using a treatment regimen of 10 minutes a day three times a week for four weeks) and improving their range of motion by 75 percent were second Can be grouped into cohorts.

In some embodiments, an artificial intelligence engine may include one or more machine learning models trained using cohorts. For example, one or more machine learning models can be trained to receive input of new patient characteristics and to output patient treatment plans that produce desired results. A machine learning model may match patterns between the new patient's characteristics and at least one patient of the patients included in a particular cohort. When patterns are matched, a machine learning model can assign new patients to specific cohorts and select treatment plans associated with at least one patient. The artificial intelligence engine may be configured to remotely control the treatment device based on the treatment plan while the new patient uses the treatment device to implement the treatment plan.

As can be appreciated, the characteristics of a new patient (eg, a new user) may change as the new patient uses the treatment device to implement a treatment plan. For example, patient performance may improve faster than expected for those within the cohort to which new patients are currently assigned. Thus, a machine learning model can be trained to dynamically reassign new patients to different cohorts containing people with similar characteristics to the currently modified characteristics as new patients, based on the modified characteristics. . For example, a clinically obese patient may lose weight and no longer meet the weight criteria of the initial cohort, resulting in the patient's weight being reassigned to different cohorts with different weight criteria.

A different treatment plan may be selected for the new patient, the treatment device may be remotely (e.g., referred to as remotely) controlled based on the different treatment plan, and the new patient may be treated using the treatment device. Therapeutic device while implementing the plan. Such technology may provide a technical solution to remotely control treatment devices.

Furthermore, the systems and methods described herein provide faster treatment for the patient because the treatment plan that most accurately matches the patient's characteristics is selected and implemented in real-time at any given time. This may lead to recovery time and/or better results. "Real time" can also refer to near real time, which can be less than 10 seconds. As described herein, the term "outcome" can refer to a medical result or medical outcome. Outcomes and outcomes can refer to responses to medical intervention.

Depending on what results are desired, the artificial intelligence engine can be trained to output several treatment plans. For example, one outcome may include recovery to a threshold level (e.g., 75% range of motion) in the fastest amount of time, while another outcome may include full recovery regardless of the amount of time (e.g. , 100% range of motion). Data obtained from a patient and sorted into cohorts is such that a first treatment plan provides first results for people with characteristics similar to those of the patient and a second treatment plan provides results for people with characteristics similar to those of the patient It may be indicated to provide secondary results for people with similar characteristics.

In addition, the artificial intelligence engine outputs treatment plans that are sub-optimal for the patient, i.e., sub-optimal, non-standard, or otherwise excluded (all without limitation, referred to as "excluded treatment plans"). can be trained to For example, if a patient has hypertension, certain exercises may put the patient at unnecessary risk or even induce a hypertensive crisis, and thus the exercise may be excluded from the patient. If a treatment plan can be flagged, it may not be approved or preferred by the patient. In some embodiments, the artificial intelligence engine may monitor therapy data received while, for example, a patient (e.g., a user) with hypertension uses a therapy device to implement an appropriate therapy plan, and may administer therapy. Excluded treatment regimens that may provide beneficial results to the patient if the data indicate that the patient is, for example, undergoing the appropriate treatment regimen without exacerbating the patient's hypertensive condition. can be modified to include the features of

In some embodiments, treatment plans and/or excluded treatment plans may be presented to a healthcare provider during a telemedicine or telehealth session. A healthcare provider may select a particular treatment plan, have the patient transmit the treatment plan to the patient, and/or control the treatment device based on the treatment plan. In some embodiments, the artificial intelligence engine is remote from the patient and treatment device to facilitate telehealth or telemedicine applications, including remote diagnosis, treatment plan determination, and rehabilitation and/or pharmacological prescription. may receive and/or operate on.

In such cases, the recommended treatment plan and/or the excluded treatment plan are displayed in real-time or near real-time, during a telemedicine or telehealth session on the user interface of the healthcare provider's computing device. can be presented at the same time. Videos may also be accompanied by audio, text, and other multimedia information. Real time may refer to 2 seconds or less. Near real-time can refer to any interaction of a time sufficiently short to allow two individuals to engage in interaction via such a user interface, generally less than 10 seconds, but will exceed 2 seconds.

Presenting the treatment plan generated by the artificial intelligence engine at the same time as presenting the patient video allows the healthcare provider to visually and/or otherwise communicate with the patient while also reviewing the treatment plan on the same user interface. can provide an enhanced user interface. An enhanced user interface may improve the experience of healthcare providers using the computing device and may encourage healthcare providers to reuse user interfaces. Such techniques also benefit from computing resources (e.g., processing , memory, network). The artificial intelligence engine can be configured to dynamically provide treatment plans and excluded treatment plans on the fly.

In some embodiments, the treatment device may be adaptive and/or individualized, as the properties, configuration, and location of the treatment device may be adapted to the needs of a particular patient. For example, the pedal may be used to increase or decrease range of motion to comply with a treatment plan designed for the user (e.g., via a telemedicine session or upon detection of certain measurements). In response, it can be dynamically adjusted on the fly (based on programmed configuration). In some embodiments, the healthcare provider may remotely adapt the therapy device to the patient's needs by having control instructions transmitted from the server to the therapy device during the telemedicine session. Such adaptive properties may improve patient recovery outcomes, further advance the goals of personalized medicine, and allow individualization of treatment regimens on an individual basis.

FIG. 1 generally illustrates a block diagram of a computer-implemented system 10 for managing treatment plans, hereinafter referred to as the "system." Managing treatment plans may include using an artificial intelligence engine to recommend treatment plans and/or provide excluded treatment plans that should not be recommended to the patient.

System 10 also includes a server 30 configured to store (eg, write to associated memory) and provide data related to managing treatment plans. Server 30 may include one or more computers and may take the form of one or more distributed and/or virtualized computers. Server 30 also includes a first communication interface 32 configured to communicate with clinician interface 20 over first network 34 . In some embodiments, first network 34 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like. The server 30 includes a first processor 36 and a first machine-readable storage memory 38, which may be abbreviated as "memory" and implemented by the first processor 36. contains first instructions 40 for performing various actions of the server 30 for

Server 30 is configured to store data relating to treatment plans. For example, memory 38 includes a system data store 42 configured to hold system data, such as data relating to treatment plans for treating one or more patients. The server 30 is also configured to store data regarding the patient's performance according to the treatment plan. For example, memory 38 includes a patient data store 44 configured to hold patient data, such as data relating to one or more patients, including data representing each patient's performance within a treatment plan.

Additionally or alternatively, characteristics of people (e.g., personal, practice, measurements, etc.), treatment plans followed by people, levels of compliance with treatment plans, and results of treatment plans may be correlated and other statistical Alternatively, a probabilistic measure may be used to allow for or divide treatment plans into databases corresponding to different patient cohorts in the patient data store 44 . For example, a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment regimen followed by a first patient, and a first outcome of the treatment regimen. Data relating to the first cohort of the first patient having can be stored in the first patient database. a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment regimen followed by the second patient, and a second outcome of the treatment regimen; Data for a second cohort of a second patient can be stored in a second patient database. Any single characteristic or any combination of characteristics may be used to segregate cohorts of patients. In some embodiments, 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, except as limited by mathematical combinations and/or partitioning theory.

This characteristic data, treatment plan data, and outcome data may be obtained from multiple treatment devices and/or computing devices over time and stored in database 44 . Characteristic data, treatment plan data, and outcome data may be correlated in a patient-cohort database within patient data store 44 . People characteristics may include personal information, performance information, and/or measurement information.

In addition to historical information about other people stored in patient cohort equivalent databases, real-time or near real-time information based on current patient characteristics about current patients being treated is stored in appropriate patient cohort equivalent databases. can be stored. A patient's characteristics may be determined to match or resemble characteristics of another person in a particular cohort (eg, Cohort A), and the patient may be assigned to that cohort.

In some embodiments, the server 30 uses one or more machine learning models 13 to power an artificial intelligence (AI) engine 11 that implements at least one of the embodiments disclosed herein. can be implemented. Server 30 may include training engine 9 capable of generating one or more machine learning models 13 . The machine learning model 13 assigns people to particular cohorts based on their characteristics, selects treatment plans using real-time and historical data correlation with patient cohort counterparts, and selects treatment devices 70, among other things. can be trained to control

One or more machine learning models 13 may be generated by training engine 9 and implemented in computer instructions executable by one or more processing devices of training engine 9 and/or server 30 . Training engine 9 may train one or more machine learning models 13 to generate one or more machine learning models 13 . One or more machine learning models 13 may be used by artificial intelligence engine 11 .

The training engine 9 can be a rackmount server, router computer, personal computer, portable digital assistant, smart phone, laptop computer, tablet computer, netbook, desktop computer, Internet of Things (IoT) device, or any other suitable computing device. device, or a combination thereof. Training engine 9 may be a cloud-based or real-time software platform and may include privacy software or protocols and/or security software or protocols.

To train one or more machine learning models 13, training engine 9 uses a training data set of a corpus of characteristics of people who have used treatment devices 70 to implement treatment plans, people using treatment devices 70 details of the treatment plan performed by (e.g., treatment protocol including exercise, amount of time exercise is performed, frequency of exercise performed, schedule of exercise, parameters of treatment device 70 throughout each step of the treatment plan /configuration/settings, etc.), and the results of treatment plans performed by people. One or more machine learning models 13 may be trained to match patterns of patient characteristics with characteristics of other people assigned to a particular cohort. The term "match" can refer to an exact match, a relative match, a substantial match, and the like. One or more machine learning models 13 may be trained to receive patient characteristics as input, map the characteristics to characteristics of people assigned to a cohort, and select treatment plans from that cohort. One or more machine learning models 13 may also be trained to control machine learning device 70 based on the treatment plan.

Different machine learning models 13 can be trained to recommend different treatment plans for different desired outcomes. For example, one machine learning model may be trained to recommend treatment plans for the most effective recovery, while another machine learning model is trained to recommend treatment plans based on speed of recovery. can be

Using training data, including training inputs and corresponding target outputs, one or more machine learning models 13 may reference model artifacts created by training engine 9 . The training engine 9 can find patterns in the training data that map training inputs to target outputs and produce machine learning models 13 that incorporate these patterns. In some embodiments, artificial intelligence engine 11, database 33, and/or training engine 9 may reside in separate components depicted in FIG. 1 (eg, assistant interface 94, clinician interface 20, etc.). .

One or more machine learning models 13 may, for example, include single-level linear or non-linear operations (e.g., support vector machines (SVM)), or machine learning models 13 may be deep networks, i.e., multi-level It may be a machine learning model including 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 is the neuron's output signal to the inputs of the remaining neurons and to itself). For example, a machine learning model may include multiple layers and/or hidden layers that perform computations (eg, dot products) using various neurons.

System 10 also includes a patient interface 50 configured to communicate information to the patient and receive feedback from the patient. Specifically, the patient interface includes an input device 52 and an output device 54, which may be collectively referred to as patient user interfaces 52,54. Input devices 52 may include one or more devices such as a keyboard, mouse, touch screen input, gesture sensors, and/or microphones and processors configured for speech recognition. Output device 54 may take one or more different forms, including, for example, a computer monitor or display screen on a tablet, smartphone, or smartwatch. Output device 54 may include other hardware and/or software components, such as projectors, virtual reality capabilities, augmented reality capabilities, and the like. Output device 54 may incorporate a variety of different visual, audio, or other presentation technologies. For example, output device 54 may provide non-visual displays such as audio signals that may include speech and/or other sounds such as tones, chimes, and/or melodies that may signal various conditions and/or directions. can contain. Output device 54 may comprise one or more different display screens for presenting various data and/or interfaces or controls for use by the patient. Output device 54 may include graphics, which may be presented by a web-based interface and/or by computer programs or applications (apps).

As generally illustrated in FIG. 1 , patient interface 50 may also be referred to as a remote communication interface configured to communicate with server 30 and/or clinician interface 20 via second network 58 . 2 communication interfaces 56 . In some embodiments, second network 58 may include a local area network (LAN), such as an Ethernet network. In some embodiments, second network 58 may include the Internet, and communication between patient interface 50 and server 30 and/or clinician interface 20 uses, for example, a virtual private network (VPN). It may be secured, such as possibly through encryption. In some embodiments, second network 58 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like. In some embodiments, second network 58 may be the same as first network 34 and/or may be operatively coupled to first network 34 .

The patient interface 50 includes a second processor 60 and a second machine-readable storage memory 62 that retains second instructions 64 for execution by the second processor 60 to perform various actions of the patient interface 50. and including. The second machine-readable storage memory 62 also includes a local data store 66 configured to hold data such as data relating to treatment plans and/or patient data such as data representing patient performance within treatment plans. . Patient interface 50 also includes a local communication interface 68 configured to communicate with various devices for use by the patient in the vicinity of patient interface 50 . Local communication interface 68 may include wired and/or wireless communication. In some embodiments, local communication interface 68 may include a local wireless network, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like.

The system 10 also includes a treatment device 70 configured to be manipulated by the patient and/or to manipulate body parts of the patient to perform activities according to the treatment plan. In some embodiments, treatment device 70 may take the form of an exercise and rehabilitation apparatus configured to implement and/or assist in the implementation of a rehabilitation regimen, which may be an orthopedic rehabilitation regimen, wherein treatment is , rehabilitation of patient body parts such as joints or bones or muscle groups. Treatment device 70 is any suitable medical, rehabilitation, therapeutic, etc. apparatus configured to be remotely controlled via another computing device to treat and/or exercise a patient. could be. Treatment device 70 can be an electromechanical machine, including one or more weights, electromechanical bicycles, electromechanical spin wheels, smart mirrors, treadmills, and the like. Body parts can include, for example, the spine, hands, feet, knees, or shoulders. A body part may include a portion of a joint, bone, or muscle group, such as one or more vertebrae, tendons, or ligaments. As generally illustrated in FIG. 1, treatment device 70 includes controller 72, which may include one or more processors, computer memory, and/or other components. Treatment device 70 also includes a fourth communication interface 74 configured to communicate with patient interface 50 via local communication interface 68 . The therapy device 70 also includes one or more internal sensors 76 and actuators 78, such as motors. Actuators 78 may be used, for example, to move a patient's body part and/or resist force applied by the patient.

Internal sensors 76 may measure one or more operational characteristics of therapeutic device 70 such as, for example, force position, velocity, and/or velocity. In some embodiments, internal sensor 76 may include a position sensor configured to measure at least one of linear or angular motion of the patient's body part. For example, an internal sensor 76 in the form of a position sensor can measure the distance a patient can move a portion of the treatment device 70, such distance being the range of motion that the patient's body part can achieve. can correspond to In some embodiments, internal sensor 76 may include a force sensor configured to measure force applied by the patient. For example, an internal sensor 76 in the form of a force sensor may measure the force or load that a patient can apply to treatment device 70 using a particular body part.

The system 10 illustrated generally in FIG. 1 also includes a gait sensor 82 that communicates with the server 30 via the local communication interface 68 of the patient interface 50 . Gait sensor 82 may track and store the number of steps taken by the patient. In some embodiments, gait sensor 82 may take the form of a wristband, wristwatch, or smartwatch. In some embodiments, the gait sensor 82 may be integrated within a phone such as a smart phone.

The system 10 illustrated generally in FIG. 1 also includes a goniometer 84 that communicates with server 30 via local communication interface 68 of patient interface 50 . Goniometer 84 measures the angle of the patient's body part. For example, goniometer 84 may measure the angle of flexion of the patient's knee or elbow or shoulder.

The system 10 illustrated generally in FIG. 1 also includes a pressure sensor 86 in communication with server 30 via local communication interface 68 of patient interface 50 . Pressure sensor 86 measures the amount of pressure or load exerted by the patient's body part. For example, pressure sensor 86 may measure the amount of force applied by the patient's foot when pedaling a stationary bicycle.

The system 10 illustrated generally in FIG. 1 also includes a supervisory interface 90 that may be similar or identical to the clinician interface 20 . In some embodiments, supervisory interface 90 may have enhanced functionality beyond that provided by clinician interface 20 . Supervisory interface 90 may be configured for use by a person responsible for treatment planning, such as an orthopedic surgeon.

The system 10 illustrated generally in FIG. 1 also includes a reporting interface 92 that may be similar or identical to clinician interface 20 . In some embodiments, reporting interface 92 may have less functionality than that provided by clinician interface 20 . For example, reporting interface 92 may not have the ability to modify treatment plans. Such reporting interface 92 may be used, for example, by a claimant to determine use of system 10 for billing purposes. In another example, reporting interface 92 does not have the ability to display patient identifiable information, and for certain data fields about data subjects and/or for certain data fields about data subject quasi-identifiers: Only depersonalized and/or anonymized data may be presented. Such a reporting interface 92 can be used, for example, by researchers to determine different effects of treatment regimens on different patients.

System 10 includes an assistant interface 94 for a healthcare provider, such as those described herein, to communicate remotely with patient interface 50 and/or therapy device 70 . Such remote communication may allow healthcare providers to use system 10 to provide assistance or guidance to patients. More specifically, assistant interface 94 transmits telemedicine signals 96, 97, 98a, 98b, 99a, 99b via a network connection, such as via first network 34 and/or second network 58, for example. configured to communicate with a patient interface 50; Telemedicine signals 96, 97, 98a, 98b, 99a, 99b include audio signal 96, audio-visual signal 97, interface control signal 98a for controlling the function of patient interface 50, and monitoring the status of patient interface 50. It includes one of an interface monitor signal 98b, a device control signal 99a for changing operating parameters of the therapy device 70, and/or a device monitor signal 99b for monitoring the status of the therapy device 70. In some embodiments, each of control signals 98 a , 99 a may be unidirectional and convey commands from assistant interface 94 to patient interface 50 . In some embodiments, in response to successfully receiving the control signals 98a, 99a and/or conveying successful and/or unsuccessful implementation of the requested control action, the acknowledgment message is the patient It can be sent from interface 50 to assistant interface 94 . In some embodiments, each of the monitor signals 98b, 99b may be a one-way status information command from the patient interface 50 to the assistant interface 94. In some embodiments, an acknowledgment message may be sent from assistant interface 94 to patient interface 50 in response to successfully receiving one of monitor signals 98b, 99b.

In some embodiments, patient interface 50 provides device control signals 99a and device monitor signals 99b between therapy device 70 and one or more other devices, such as assistant interface 94 and/or server 30. It can be configured as a pass-through. For example, patient interface 50 may be configured to transmit device control signals 99a in response to device control signals 99a in telemedicine signals 96, 97, 98a, 98b, 99a, 99b from assistant interface 94.

In some embodiments, assistant interface 94 may be presented on a shared physical device as clinician interface 20 . For example, clinician interface 20 may include one or more screens that implement assistant interface 94 . Alternatively or additionally, clinician interface 20 may include additional hardware components, such as video cameras, speakers, and/or microphones, for implementing aspects of assistant interface 94 .

In some embodiments, one or more portions of the telemedicine signals 96, 97, 98a, 98b, 99a, 99b are sourced from pre-recorded sources for presentation by the output device 54 of the patient interface 50 ( for example, audio recordings, video recordings, or animations). For example, a tutorial video may be streamed from server 30 and presented on patient interface 50 . Content from pre-recorded sources may be requested by the patient via patient interface 50 . Alternatively, via controls on assistant interface 94 , the healthcare provider may play content from pre-recorded sources on patient interface 50 .

Assistant interface 94 includes assistant input device 22 and assistant display 24, which may be collectively referred to as assistant user interfaces 22,24. Assistant input device 22 may include, for example, one or more of a phone, keyboard, mouse, trackpad, or touch screen. Alternatively or additionally, assistant input device 22 may include one or more microphones. In some embodiments, the one or more microphones may take the form of a telephone handset, headset, or one or more wide area microphones configured for the healthcare provider to speak to the patient via the patient interface 50. . In some embodiments, the assistant input device 22 comprises hardware and/or software configured to interpret commands spoken by the healthcare provider using one or more microphones; It can be configured to provide voice-based functionality. The assistant input device 22 may be provided by or include functionality similar to existing voice-based assistants such as Siri from Apple, Alexa from Amazon, Google Assistant, or Bixby from Samsung. Assistant input device 22 may include other hardware and/or software components. Assistant input device 22 may include one or more general purpose and/or special purpose devices.

Assistant display 24 may take one or more different forms, including, for example, a computer monitor or display screen on a tablet, smartphone, or smartwatch. Assistant display 24 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities. Assistant display 24 may incorporate a variety of different visual, audio, or other presentation technologies. For example, assistant display 24 may include non-verbal signals, such as voice signals, which may include speech and/or other sounds, such as tones, chimes, melodies, and/or music, which may signal various conditions and/or directions. A visual display may be included. Assistant display 24 may comprise one or more different display screens that present various data and/or interfaces or controls for use by the healthcare provider. The assistant display 24 may include graphics, which may be presented by a web-based interface and/or by computer programs or applications (apps).

In some embodiments, system 10 may provide computer translation of language from assistant interface 94 to patient interface 50 and/or vice versa. Computer conversion of language may include computer conversion of speech and/or computer conversion of text. Additionally or alternatively, system 10 may provide speech recognition of text and/or vocalizations. For example, system 10 may convert speech to printed text and/or system 10 may vocalize words from printed text. System 10 may be configured to recognize words spoken by any or all of the patient, clinician, and/or healthcare provider. In some embodiments, system 10 may be configured to recognize and respond to requests or commands spoken by the patient. For example, system 10 may automatically initiate a telemedicine session in response to a verbal command (which may be given in any one of several different languages) by the patient.

In some embodiments, server 30 may generate aspects of assistant display 24 for presentation by assistant interface 94 . For example, server 30 may include a web server configured to generate display screens for presentation on assistant display 24 . For example, the artificial intelligence engine 11 may generate recommended and/or excluded treatment plans for the patient and may generate those recommended and/or excluded treatment plans for presentation on the assistant display 24 of the assistant interface 94 . Or it may generate a display screen containing an external treatment plan. In some embodiments, assistant display 24 may be configured to present a virtualized desktop hosted by server 30 . In some embodiments, server 30 may be configured to communicate with assistant interface 94 over first network 34 . In some embodiments, first network 34 may include a local area network (LAN), such as an Ethernet network.

In some embodiments, first network 34 may include the Internet, and communication between server 30 and assistant interface 94 is by using encryption, for example, over a virtual private network (VPN). may be secured via privacy-enhancing techniques such as; Alternatively or additionally, server 30 communicates with assistant interface 94 via one or more networks independent of first network 34 and/or other means of communication such as direct wired or wireless communication channels. may be configured to communicate. In some embodiments, patient interface 50 and therapy device 70 may each operate from a patient location that is geographically remote from the location of assistant interface 94 . For example, patient interface 50 and therapy device 70 may be used as part of a home rehabilitation system, which may be assisted remotely by using assistant interface 94 at a centralized location such as a clinic or call center.

In some embodiments, the assistant interface 94 is a number of different terminals (e.g., computing devices) that may be grouped together, e.g., at one or more call centers or at one or more clinician's offices. can be one of In some embodiments, multiple assistant interfaces 94 may be geographically distributed. In some embodiments, a person may work as a healthcare provider remotely from any conventional office infrastructure. Such remote work may be implemented, for example, when assistant interface 94 takes the form of a computer and/or telephone. This telecommuting functionality may enable telecommuting arrangements, which may include part-time and/or flexible working hours for healthcare providers.

2-3 illustrate one embodiment of a therapeutic device 70. FIG. More specifically, FIG. 2 generally illustrates a therapeutic device 70 in the form of a stationary cycling machine 100, which may be called a stationary bike for short. A stationary cycling machine 100 includes a set of pedals 102 each mounted on a pedal arm 104 for rotation about an axis 106 . In some embodiments, as generally illustrated in FIG. 2, the pedals 102 are movable on the pedal arm 104 to adjust the range of motion that the patient uses in pedaling. For example, positioning the pedals inward toward the axis 106 corresponds to a smaller range of motion than when the pedals are positioned outward from the axis 106 . A pressure sensor 86 is attached to or embedded in one of the pedals 102 to measure the amount of force applied to the pedals 102 by the patient. Pressure sensor 86 may communicate wirelessly to therapy device 70 and/or to patient interface 50 .

FIG. 4 generally illustrates a person (patient) using the therapeutic device of FIG. 2, showing sensors and various data parameters connected to the patient interface 50 . An exemplary patient interface 50 is a tablet computer or smartphone, such as an iPad, iPhone, Android device, or Surface tablet (each a registered trademark), or a phablet, manually held by the patient. In some other embodiments, patient interface 50 may be embedded in or attached to treatment device 70 .

FIG. 4 generally illustrates the patient wearing the gait sensor 82 on the patient's wrist, and a note reading "Today's Steps 1355" indicates that the gait sensor 82 recorded its step count. , indicates that it has been transmitted to the patient interface 50 . FIG. 4 also generally illustrates the patient wearing the goniometer 84 on the patient's right knee, and the notation reading "Knee Angle 72°" indicates that the goniometer 84 measures that knee angle. , indicates that it is transmitting to the patient interface 50 . FIG. 4 also shows "12.5 lbs of force" on the right side of one of the pedals 102 having a pressure sensor 86, the right pedal pressure sensor 86 measuring that force measurement and sending it to the patient interface 50. It is generally exemplified to indicate that it is transmitting.

FIG. 4 also shows “27 pounds of force” on the left side of one of the pedals 102 with a pressure sensor 86 , the left pedal pressure sensor 86 measuring and transmitting that force measurement to the patient interface 50 . It is generally exemplified to indicate that FIG. 4 also generally illustrates other patient data such as the indicator "session time 0:04:13" indicating that the patient has been using the therapy device 70 for 4 minutes and 13 seconds. This session time may be determined by patient interface 50 based on information received from therapy device 70 . Figure 4 also generally illustrates an index labeled "Pain Level 3". Such pain levels may be obtained from patents in response to a petition, such as a question, presented on patient interface 50 .

FIG. 5 is an illustrative embodiment of overview display 120 of assistant interface 94 . Specifically, overview display 120 presents several different controls and interfaces for a healthcare provider to remotely assist a patient using patient interface 50 and/or treatment device 70 . This remote assistance functionality may also be referred to as telemedicine or telehealth.

Specifically, summary display 120 includes patient profile display 130 that presents historical information about the patient using therapy device 70 . Patient profile display 130 may take the form of a portion or region of overview display 120, as generally illustrated in FIG. 5, although patient profile display 130 may take other forms, such as a separate screen or pop-up window. can be taken.

In some embodiments, patient profile display 130 may include a limited subset of patient history information. More specifically, the data presented on patient profile display 130 may depend on the healthcare provider's need for that information. For example, a health care provider assisting a patient with a medical issue may be provided with historical medical information about the patient, whereas a technician troubleshooting a problem with treatment device 70 may have more information about the patient. A limited set can be provided. For example, the technician may be given only the patient's name.

Patient profile display 130 may include depersonalized and/or anonymized data, or use any privacy-enhancing technology in ways that may violate patient confidentiality requirements; Sensitive patient data may be prevented from being communicated. Such privacy-enhancing technologies include, but are not limited to, the Health Insurance Portability and Accountability Act (HIPAA) or the General Data Protection Regulation (GDPR), where patients may be considered "data subjects." compliance with laws, regulations or other governance rules.

In some embodiments, patient profile display 130 may present information regarding a treatment plan to be followed when the patient uses treatment device 70 . Such treatment plan information may be restricted to healthcare providers. For example, a health care provider assisting a patient with a problem with a treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting a problem with treatment device 70 may not have any information regarding the patient's treatment plan. May not be provided.

In some embodiments, one or more recommended and/or excluded treatment plans may be presented to the healthcare provider in patient profile display 130 . One or more recommended treatment plans and/or excluded treatment plans may be generated by the artificial intelligence engine 11 of the server 30 and received in real time from the server 30, particularly during a telemedicine or telehealth session. An example of presenting one or more recommended and/or ruled out treatment plans is described below with reference to FIG.

The example overview display 120 illustrated generally in FIG. 5 also includes a patient status display 134 that presents status information regarding the patient using the therapy device. Patient status display 134 may take the form of a portion or region of overview display 120 as generally illustrated in FIG. 5, although patient status display 134 may take other forms, such as a separate screen or pop-up window. can be taken.

Patient status display 134 includes sensor data 136 from one or more of external sensors 82 , 84 , 86 and/or from one or more internal sensors 76 of therapy device 70 . In some embodiments, patient status display 134 may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using therapy device 70 . The one or more wearable devices may include watches, bracelets, necklaces, chest bands, and the like. One or more wearable devices may be configured to monitor a patient's heart rate, temperature, blood pressure, one or more vital signs, etc. while the patient is using therapy device 70 . In some embodiments, the patient status display 134 may present other data 138 about the patient, such as last reported pain level or progress within a treatment plan.

What data is available for viewing and/or modifying on any or all of the user interfaces 20, 50, 90, 92, 94 of the system 10 using the user access controls; can restrict access, including In some embodiments, user access controls may be used to control what information is available to any given person using system 10 . For example, the data presented on the assistant interface 94 may be controlled by user access controls, with permissions set according to the needs and/or entitlements of the healthcare provider/user to view that information.

The example overview display 120 illustrated generally in FIG. 5 also includes a help data display 140 that presents information for use by the healthcare provider in assisting the patient. Help data display 140 may take the form of a portion or region of summary display 120, as generally illustrated in FIG. Help data display 140 may take other forms, such as a separate screen or pop-up window. Help data display 140 may include, for example, presenting answers to frequently asked questions regarding the use of patient interface 50 and/or therapy device 70 .

Help data display 140 may also include research data or best practices. In some embodiments, the help data display 140 may present scripts for answers or explanations in response to patient questions. In some embodiments, help data display 140 may present flowcharts or walkthroughs for healthcare providers to use in determining root causes and/or solutions to patient problems.

In some embodiments, assistant interface 94 presents two or more help data displays 140, which may be the same or different, to simultaneously present help data for use by the healthcare provider. obtain. For example, a first help data display can be used to present a troubleshooting flow chart for determining the root of a patient's problem, and a second help data display can be used by a healthcare provider to read to the patient. Script information, preferably information such as instructions for the patient to perform some action, may be presented, which may help narrow down or solve the problem. In some embodiments, the second help data display may automatically populate the script information based on the input to the troubleshooting flowchart in the first help data display.

The example overview display 120 illustrated generally in FIG. 5 also includes patient interface controls 150 for presenting information about the patient interface 50 and/or modifying one or more settings of the patient interface 50. including. Patient interface controls 150 may take the form of a portion or region of overview display 120, as generally illustrated in FIG. Patient interface controls 150 may take other forms, such as separate screens or pop-up windows. Patient interface control 150 may present the communicated information to assistant interface 94 via one or more of interface monitor signals 98b.

As generally illustrated in FIG. 5 , patient interface controls 150 include a display feed 152 of displays to be presented by patient interface 50 . In some embodiments, display feed 152 may include a live copy of the display screen currently being presented to the patient by patient interface 50 . In other words, display feed 152 may present an image of what is presented on the display screen of patient interface 50 .

In some embodiments, display feed 152 may include informal information about the display screen currently being presented by patient interface 50, such as screen name or screen number. Patient interface controls 150 may include patient interface settings controls 154 for a healthcare provider to adjust or control one or more settings or aspects of patient interface 50 . In some embodiments, patient interface settings control 154 may cause assistant interface 94 to generate and/or transmit interface control signals 98 to control features or settings of patient interface 50 .

In some embodiments, patient interface settings control 154 may include collaborative or co-browsing capabilities for healthcare providers to remotely view and/or control patient interface 50 . For example, the patient interface settings control 154 allows the healthcare provider to remotely enter text into one or more text entry fields on the patient interface 50 and/or using the assistant interface 94 mouse or touch screen. to remotely control the cursor on the patient interface 50 .

In some embodiments, using the patient interface 50, the patient interface settings control 154 may allow the healthcare provider to change settings that the patient cannot change. For example, the patient interface 50 may be blocked from accessing language settings to prevent the patient from inadvertently switching the language used for display on the patient interface 50, whereas the patient interface Settings control 154 may allow a healthcare provider to change the language setting of patient interface 50 . In another example, the patient interface 50 may set the font size to prevent the patient from inadvertently switching the font size used for display on the patient interface 50 such that the display becomes unreadable to the patient. The patient interface settings control 154 may allow the healthcare provider to change the font size setting of the patient interface 50, whereas the settings may not be able to be changed to a smaller size.

The example overview display 120 illustrated generally in FIG. 5 also includes a patient interface 50 and one or more other devices 70, 82, such as a treatment device 70, a gait sensor 82, and/or a goniometer 84. 84 and an interface communication display 156 that indicates the status of communication between and. Interface communication display 156 may take the form of a portion or region of overview display 120, as generally illustrated in FIG.

Interface communication display 156 may take other forms, such as a separate screen or pop-up window. The interface communications display 156 may include controls for a healthcare provider to remotely modify communications with one or more of the other devices 70 , 82 , 84 . For example, the healthcare provider may choose to reset communication with one of the other devices 70, 82, 84 or establish communication with a new one of the other devices 70, 82, 84. , the patient interface 50 can be remotely commanded. This functionality is useful, for example, if the patient has a problem with one of the other devices 70,82,84, or if the patient receives a new or replacement one of the other devices 70,82,84. can be used if

The example overview display 120 illustrated generally in FIG. 5 also includes device controls 160 for the healthcare provider to view and/or control information regarding the treatment device 70 . Device controls 160 may take the form of a portion or region of overview display 120, as generally illustrated in FIG. Device control 160 may take other forms, such as a separate screen or pop-up window. Device control 160 may include a device status display 162 with information regarding the current status of the device. Device status display 162 may present information communicated to assistant interface 94 via one or more of device monitor signals 99b. Device status display 162 may indicate whether therapy device 70 is currently in communication with patient interface 50 . Device status display 162 may present other current and/or historical information regarding the status of therapy device 70 .

Device controls 160 may include device settings controls 164 for a healthcare provider to adjust or control one or more aspects of treatment device 70 . The device settings control 164 provides the assistant interface 94 with the operating parameters and/or one or more characteristics of the therapy device 70 (e.g., pedal radius setting, resistance setting, target RPM, other suitable characteristics of the therapy device 70, or A device control signal 99 (eg, which as described may be referred to as a treatment plan input) may be generated and/or transmitted to alter a combination thereof).

The device settings controls 164 may include a mode button 166 and a position control 168 that allow the healthcare provider to put the actuator 78 of the treatment device 70 into manual mode and then use the position control 168 to set the actuator to 78 may be used together so that settings such as the position or speed of 78 can be changed. A mode button 166 may provide settings, such as position, for switching between automatic and manual modes.

In some embodiments, one or more settings may be adjustable at any time and without an associated auto/manual mode. In some embodiments, a healthcare provider may change the operating parameters of therapy device 70 , such as the pedal radius setting, while the patient is actively using therapy device 70 . Such “on-the-fly” adjustments may or may not be available to the patient using patient interface 50 .

In some embodiments, the device settings controls 164 may allow the healthcare provider to change settings that the patient cannot change using the patient interface 50 . For example, the patient interface 50 may be prevented from changing preconfigured settings, such as the height or tilt settings of the treatment device 70, whereas the device settings control 164 prevents the healthcare provider from It may be provided to change the height or tilt settings of the device 70 .

The example overview display 120 illustrated generally in FIG. 5 may also include patient communication controls 170 for controlling an audio or audiovisual communication session with the patient interface 50 . A communication session with patient interface 50 may include a live feed from assistant interface 94 for presentation by an output device of patient interface 50 . A live feed may take the form of an audio feed and/or a video feed. In some embodiments, patient interface 50 may be configured to provide two-way audio or audiovisual communication with a person using assistant interface 94 . Specifically, a communication session with patient interface 50 may include an interactive (two-way) video feed or an audiovisual feed, with patient interface 50 and assistant interface 94 each presenting the other's video.

In some embodiments, patient interface 50 may present video from assistant interface 94 while assistant interface 94 presents audio only, or assistant interface 94 presents live audio from patient interface 50. No signal or visual signal is presented. In some embodiments, assistant interface 94 may present video from patient interface 50 while patient interface 50 presents audio only, or patient interface 50 may present live audio from assistant interface 94. No signal or visual signal is presented.

In some embodiments, an audio or audiovisual communication session with the patient interface 50 may occur, at least in part, while the patient is performing a rehabilitation regimen on the body part. Patient communication control 170 may take the form of a portion or region of overview display 120, as generally illustrated in FIG. Patient communication control 170 may take other forms, such as a separate screen or pop-up window.

Voice communication and/or audio-visual communication is used by the assistant interface 94 and/or one or more other devices such as a telephone system or by the healthcare provider while the healthcare provider uses the assistant interface 94 It may be processed and/or directed by a videoconferencing system. Alternatively or additionally, voice communication and/or audiovisual communication may include communication with a third party. For example, system 10 allows a healthcare provider to initiate a three-way conversation with a patient and a subject matter expert, such as a healthcare provider or specialist, regarding the use of a particular piece of hardware or software. can make it possible. An exemplary patient communication control 170, illustrated generally in FIG. 5, includes a call control 172 for use by a healthcare provider in managing various aspects of voice or audiovisual communication with a patient. including. Call control 172 includes a disconnect button 174 for the healthcare provider to end the voice or audiovisual communication session. Call controls 172 also include a mute button 176 for temporarily muting audio or audiovisual signals from assistant interface 94 . In some embodiments, call control 172 may include other features such as a hold button (not shown).

Call controls 172 also provide one or more record/playback buttons, such as record, play, and pause buttons, for controlling the recording and/or playback of audio and/or video from a conference call session on patient interface 50 . A playback control unit 178 is included. Call control 172 also includes a video feed display 180 for presenting still and/or video images from patient interface 50 and a self-video display 182 showing a current image of the healthcare provider using assistant interface 94. ,including. Self-video display 182 may be presented in picture-in-picture format within a section of video feed display 180, as generally illustrated in FIG. Alternatively or additionally, selfie video display 182 may be presented separately and/or independently from video feed display 180 .

The example overview display 120 illustrated generally in FIG. 5 also includes a third party communication control 190 for use in conducting voice and/or audiovisual communications with a third party. Third party communication control 190 may take the form of a portion or region of overview display 120, as generally illustrated in FIG. Third party communication control 190 may take other forms, such as a separate on-screen display or a pop-up window.

A third party communication control 190 provides a contact list and/or contact list for contacting third parties, e.g., subject matter experts, such as healthcare providers or specialists, regarding the use of a particular piece of hardware or software. It may include one or more controls, such as buttons or controls. Third party communication controls 190 may include teleconferencing capabilities for third parties to simultaneously communicate with both the healthcare provider via assistant interface 94 and the patient via patient interface 50 . For example, system 10 may provide for a healthcare provider to initiate a three-way conversation with a patient and a third party.

FIG. 6 generally illustrates an exemplary block diagram for training a machine learning model 13 to output a treatment plan 602 for a patient based on data 600 regarding the patient in accordance with this disclosure. Data regarding other patients may be received by server 30 . Other patients may be performing treatment regimens using different treatment devices.

The data may include characteristics of other patients, details of treatment plans performed by other patients, and/or results of implementing treatment plans (e.g., recovery rate of patient's body part, amount of recovery of patient's body part, etc.). , increase or decrease in muscle strength of a portion of the patient's body, increase or decrease in range of motion of the portion of the patient's body, etc.).

Data are assigned to the various cohorts as depicted. Cohort A contains data of patients with similar first characteristics, first treatment regimens, and first outcomes. Cohort B includes data from patients with similar second characteristics, second treatment regimens, and second outcomes. For example, Cohort A may include first characteristics of patients in their twenties without any medical condition who have undergone surgery for a fractured limb, and the treatment plan for these patients may include a specific treatment protocol. (e.g., use the treatment device 70 for 30 minutes five times a week for 3 weeks, and the value of the properties, configurations, and/or settings of the treatment device 70 is X (where X is a number) for the first 2 weeks; The last week is set to Y (where Y is a number).

Cohort A and Cohort B may be included in a training data set used to train machine learning model 13 . A machine learning model 13 can be trained to match patterns between characteristics of each cohort and output a treatment plan that provides results. Thus, when new patient data 600 is input to the trained machine learning model 13, the trained machine learning model 13 matches the features contained in the data 600 to the features of either Cohort A or Cohort B. and output an appropriate treatment plan 602 . In some embodiments, machine learning model 13 may be trained to output one or more excluded treatment plans that should not be performed by new patients.

FIG. 7 generally illustrates one embodiment of a summary display 120 of assistant interface 94 presenting recommended and excluded treatment plans in real-time during a telemedicine session in accordance with the present disclosure. As depicted, summary display 120 only includes sections of patient profile 130 and video feed display 180 , including selfie video display 182 . Any suitable configuration of the controls and interfaces of overview display 120 described with reference to FIG. 5 may be presented in addition to or instead of patient profile 130, video feed display 180, and self-video display 182. obtain.

A healthcare provider using assistant interface 94 (e.g., a computing device) during a telemedicine session can view summary display 120 (e.g., on display screen 24 of assistant interface 94) that also presents video from the patient on video feed display 180. presented in the self-video 182 in a portion of the presented user interface). Additionally, the video feed display 180 also allows the healthcare provider to share recommended and/or excluded treatment plans with the patient on the patient interface 50 in real time or near real time during the telemedicine session. may include graphical user interface (GUI) objects 700 (eg, buttons) that allow A healthcare provider may select GUI object 700 to share recommended and/or excluded treatment plans. As depicted, another portion of overview display 120 includes patient profile display 130 .

Patient profile display 130 presents two exemplary recommended treatment plans 600 and one exemplary excluded treatment plan 602 . As described herein, treatment regimens may be recommended taking into account the characteristics of the patient being treated. Characteristics of the patient being treated and a cohort of other people who have used the treatment device 70 to implement the treatment plan in order to generate a recommended treatment plan 600 that the patient should follow to achieve the desired outcome. , can be matched by one or more machine learning models 13 of the artificial intelligence engine 11 . Each of the recommended treatment plans may be generated based on different desired results.

For example, as depicted, the patient profile display 130 reads, "Patient characteristics match characteristics of use in Cohort A. The following treatment plan is based on the patient characteristics and the desired outcome. Recommended. The patient profile display 130 then presents recommended treatment plans from Cohort A, each treatment plan providing different results.

As depicted, treatment plan "A" states, "Patient X should use the treatment device for 30 minutes per day for 4 days to achieve an increase in range of motion of Y%, and Patient X should , has type 2 diabetes, and patient X could be prescribed drug Z for pain control during the treatment plan (drug Z is approved for people with type 2 diabetes). )” is shown. Thus, the generated treatment plan achieves Y% range of motion increase. As can be appreciated, the treatment plan also provides a recommended drug (e.g., Drug Z) to prescribe to the patient for pain management given the patient's known medical condition (e.g., type 2 diabetes). include. That is, the recommended patient medication not only does not conflict with the patient's medical condition, but thereby improves the likelihood of a good patient outcome. This particular example and all such examples elsewhere herein indicate that the treatment regimen generated recommends multiple drugs, or confirms, evaluates, diagnoses and /or is not intended to be limited in any way to address treatment.

Recommended treatment regimen "B" may specify different treatment regimens, including different treatment protocols for treatment devices, different drug regimens, etc., based on different desired outcomes of the treatment regimens.

As depicted, patient profile display 130 may also present treatment plans 602 that are excluded. These types of treatment plans are presented to the health care provider using the assistant interface 94 to warn the health care provider not to recommend certain portions of the treatment plan to the patient. For example, an excluded treatment plan may specify: "Patient X should not use the treatment device for more than 30 minutes a day for heart disease, and Patient X should has type 2 diabetes and patient X should not be prescribed drug M for pain control during the treatment plan (in this scenario, drug M would cause complications to people with type 2 Specifically, the excluded treatment regimen points to a treatment protocol limitation that patient X should not exercise more than 30 minutes per day due to heart disease. . The excluded treatment regimen also points out that patient X should not be prescribed drug M because drug M conflicts with the medical condition of type 2 diabetes.

A healthcare provider may select a treatment plan for the patient on summary display 120 . For example, a healthcare provider may select from patient treatment plans 600 using an input peripheral (eg, mouse, touch screen, microphone, keyboard, etc.). In some embodiments, during the telemedicine session, the healthcare provider may discuss the pros and cons of the recommended treatment plan 600 with the patient.

In any event, the health care provider can select the treatment regimen that the patient should follow to achieve the desired outcome. The selected treatment plan may be transmitted to patient interface 50 for presentation. The patient may view the selected treatment plan on patient interface 50 . In some embodiments, health care providers and patients may review details (eg, treatment protocols using treatment device 70, dietary regimens, drug regimens, etc.) in real time or near real time during a telemedicine session. In some embodiments, server 30 may control therapy device 70 as the user uses therapy device 70 based on the selected treatment plan and during a telemedicine session.

FIG. 8 generally illustrates one embodiment of a summary display 120 of assistant interface 94 presenting a recommended treatment plan that has changed as a result of changes in patient data in real-time during a telemedicine session, according to the present disclosure. do. As can be appreciated, treatment device 70 and/or any computing device (eg, patient interface 50) may transmit data while a patient uses treatment device 70 to implement a treatment plan. The data may include updated patient characteristics and/or other treatment data. For example, updated characteristics may include new performance information and/or measurement information. The performance information may include the velocity of the portion of the treatment device 70, the range of motion achieved by the patient, the force exerted on the portion of the treatment device 70, the patient's heart rate, the patient's blood pressure, the patient's breathing rate, and the like.

In some embodiments, data received at server 30 may be input to trained machine learning model 13, which characterizes the patient's progress toward the current treatment plan. It can be determined that Determining that the patient is on the current treatment plan may cause the trained machine learning model 13 to adjust the parameters of the treatment device 70 . Adjustments may be based on subsequent steps in the treatment plan to further improve patient performance.

In some embodiments, data received at server 30 may be input to trained machine learning model 13, which predicts that the patient is not moving toward the current treatment plan (e.g., schedule rather than schedule). being delayed, unable to maintain speed, unable to achieve a certain range of motion, too much pain, etc.) or being ahead of schedule in the current treatment plan (e.g., a certain speed exercised for longer than a specified amount of time without pain, applying more force than specified, etc.).

The trained machine learning model 13 may determine that the patient's characteristics are no longer consistent with the patient's characteristics within the cohort to which the patient is assigned. Thus, the trained machine learning model 13 may reassign the patient to another cohort that includes the qualifying trait patient trait. Accordingly, trained machine learning model 13 may select new treatment plans from the new cohort and control treatment device 70 based on the new treatment plans.

In some embodiments, prior to controlling treatment device 70 , server 30 may provide new treatment plan 800 to assistant interface 94 for presentation within patient profile 130 . As depicted, patient profile 130 states, "Patient characteristics have changed to match those of use in Cohort B. Based on patient characteristics and desired outcome, the following treatment plan is recommended for patients”. The patient profile 130 then presents a new treatment plan 800 ("Patient X should use the treatment device for 10 minutes a day for 3 days to achieve an increase in range of motion of L% A healthcare provider may select a new treatment plan 800 and server 30 may receive the selection.Server 30 may control treatment device 70 based on new treatment plan 800. Several implementations. In form, the new treatment plan 800 can be transmitted to the patient interface 50 so that the patient can view the details of the new treatment plan 800 .

In some embodiments, server 30 may receive treatment data about a patient while the patient is using treatment device 70 to implement a treatment plan. As described, the treatment plan may correspond to a rehabilitation treatment plan, a preoperative rehabilitation treatment plan, an exercise treatment plan, or any other suitable treatment plan. Treatment data may include various characteristics of the patient (eg, as described herein), various measured information about the patient while the patient is using the treatment device 70 (eg, as described herein). etc.), various characteristics of the treatment device 70 (eg, such as those described herein), treatment plans, other suitable data, or combinations thereof.

In some embodiments, at least some of the therapy data is sensor data from one or more of the external sensors 82, 84, 86 and/or from one or more internal sensors 76 of the therapy device 70. 136. In some embodiments, at least some of the therapy data may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using therapy device 70. The one or more wearable devices may include watches, bracelets, necklaces, chest straps, head sweatbands, wrist sweatbands, any other suitable sweatbands, any other suitable wearables, or combinations thereof. . While the patient is using therapy device 70, one or more wearable devices may be configured to monitor the patient's heart rate, temperature, blood pressure, one or more vital signs, and the like.

In some embodiments, server 30 may use the therapy data to generate therapy information. The treatment information is a formatted summary of the user's implementation of the treatment plan during use of the treatment device, such that the treatment data is presentable at the computing device of the healthcare provider responsible for the user's implementation of the treatment plan. can include In some embodiments, patient profile display 120 may include and/or display treatment information.

Server 30 may be configured to provide treatment information in summary display 120 . For example, server 30 may store therapy information for access by summary display 120 and/or communicate therapy information to summary display 120 . In some embodiments, server 30 provides treatment information to patient profile display 130, or other suitable section, portion, or component of summary display 120, or to any other suitable display or interface. obtain.

In some embodiments, a healthcare provider assisting the patient while using treatment device 70 reviews the treatment information and decides whether to modify the treatment plan and/or one or more characteristics of treatment device 70. can judge. For example, a health care provider may review the treatment information and compare the treatment information to treatment plans being followed by the patient.

During the patient's use of the treatment device 70, the healthcare provider may provide one or more pieces or portions of the expected information regarding the patient's ability to implement the treatment plan in order for the patient to use the treatment device 70 for treatment. During execution of the plan, it may be compared to one or more corresponding portions or portions of measured information about the patient (eg, indicated by treatment information). The expected information may include one or more of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, the user's blood pressure, other suitable information of the user, or combinations thereof. A health care provider determines that a treatment regimen is having a desired effect if one or more portions or portions of the measured information are within acceptable ranges of one or more corresponding portions or portions of the expected information. It can be determined that Conversely, if one or more patches or portions of the measured information are outside the acceptable range of one or more corresponding portions or portions of the expected information, the health care provider may determine that the treatment regimen will not achieve the desired effect. It can be determined that it is not given.

In some embodiments, while the patient is using the treatment device 70 to implement a treatment plan, the healthcare provider may specify the expected respective characteristics of the treatment device 70 to the treatment device 70 indicated by the treatment information. can be compared with the corresponding properties of For example, the healthcare provider may compare the expected resistance setting of the therapy device 70 with the actual resistance setting of the therapy device 70 indicated by the therapy information.

A health care provider may determine that a patient is properly performing a treatment regimen when the actual characteristics of the treatment device 70 indicated by the treatment information are within the expected characteristics of the treatment device 70. . Conversely, if the actual characteristics of the treatment device 70 as indicated by the treatment information are outside the expected characteristics of the treatment device 70, the healthcare provider may conclude that the patient is not properly following the treatment plan. can judge.

If the health care provider determines that the treatment information indicates that the patient is performing the treatment plan properly and/or that the treatment plan is having the desired effect, the health care provider may administer the treatment plan or It may be determined not to modify one or more properties of therapeutic device 70 . Conversely, if the healthcare provider determines that the treatment information indicates that the patient is not performing the treatment regimen properly and/or that the treatment regimen is not having the desired effect, the healthcare provider: While the user is using treatment device 70 to implement a treatment plan, the user may decide to modify one or more characteristics of treatment plan and/or treatment device 70 .

In some embodiments, server 30 may receive subsequent treatment data about the patient while the patient uses treatment device 70 to implement the modified treatment plan. For example, after a healthcare provider modifies the treatment plan and/or provides input to control one or more characteristics of treatment device 70, the patient uses treatment device 70 to view the modified treatment plan. can continue to implement Subsequent treatment data may correspond to treatment data generated while the user uses treatment device 70 to implement the modified treatment plan. In some embodiments, subsequent treatment data is provided after the healthcare provider receives the treatment information and determines not to modify the treatment plan and/or control one or more characteristics of treatment device 70. It may correspond to treatment data generated while the patient continues to implement a treatment plan using treatment device 70 .

Server 30 may further modify the treatment plan and/or control one or more characteristics of treatment device 70 based on subsequent treatment plan inputs received from summary display 120 . Subsequent treatment plan inputs may correspond to inputs provided by a healthcare provider in summary display 120 in response to receiving and/or reviewing subsequent treatment information corresponding to subsequent treatment data. Server 30 may continuously and/or post therapy information to other sections, portions, or components of patient profile display 130 and/or summary display 120 based on therapy data received on an ongoing and/or periodic basis. Alternatively, it should be understood that it may be provided periodically.

The healthcare provider may continuously or periodically receive and/or review the treatment information while the user uses the treatment device to implement the treatment plan. The healthcare provider may modify the treatment plan and/or control one or more characteristics of the treatment device based on one or more trends indicated by the treatment information received on an ongoing and/or periodic basis. can decide whether to For example, the one or more trends may indicate that the user is not performing the treatment plan properly and/or that the user's implementation of the treatment plan is not having the desired effect. It can indicate possible trend changes.

FIG. 9 generally illustrates a method 900 for monitoring performance of a treatment plan by a user using a treatment device and selectively modifying one or more characteristics of the treatment plan and treatment device according to the present disclosure. It is a flow diagram to do. Method 900 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general purpose computer system or dedicated machine), or a combination of both. Method 900 and/or each of its individual functions, routines, subroutines, or acts may be executed by any component of FIG. ) may be performed by In some embodiments, method 900 may be performed by a single processing thread. Alternatively, method 900 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or method operations.

For ease of explanation, method 900 is depicted and described as a series of acts. However, operations in accordance with this disclosure may occur in various orders and/or concurrently and/or with other operations not presented and described herein. For example, the acts depicted in method 900 may be performed in combination with any other acts of any other methods disclosed herein. Moreover, not all illustrated acts may be required to implement methodology 900 in accordance with the disclosed subject matter. Additionally, those skilled in the art will understand and appreciate that the method 900 can alternatively be represented as a series of interrelated states via a state diagram or events.

At 902 , a processing device may receive treatment data regarding a user implementing a treatment plan using a treatment device, such as treatment device 70 . Treatment data may include characteristics of the user, measurement information about the user while the user is using the treatment device 70, characteristics of the treatment device 70, treatment plans, other suitable data, or combinations thereof.

At 904, the processing device may use the therapy data to generate therapy information. Treatment information may include a summary of the user's implementation of a treatment plan while using treatment device 70 . The treatment information may be formatted such that the treatment data is presentable at the computing device of the healthcare provider responsible for implementing the treatment plan by the user.

At 906, the processing device can be configured to provide (eg, store for access, make available, make accessible, transmit, etc.) treatment information at the healthcare provider's computing device. . At 908, the processing device can be configured to provide treatment information at an interface of the healthcare provider's computing device. For example, the processing device may store the treatment information for access by the healthcare provider's computing device and/or may store the treatment information for display on the patient profile display 130 of the overview display 120 and/or the treatment information may be stored on the healthcare provider's computing device. It may be communicated (eg, transmitted) to a device. As described, overview display 120 may be configured to receive input, such as treatment plan input, indicative of one or more modifications to the treatment plan and/or one or more characteristics of treatment device 70 . A healthcare provider may interact with various controls, input fields, and other aspects of overview display 120 to provide treatment plan inputs.

At 910, the processing device may modify the treatment plan in response to receiving treatment plan input including at least one modification to the treatment plan. For example, the processing device may modify various features and characteristics of the treatment plan based on at least one modification indicated by the treatment plan input.

At 912, the processing device may selectively control treatment device 70 using the modified treatment plan. For example, the processing device may modify one or more characteristics of treatment device 70 based on modifications to the treatment plan. Additionally or alternatively, the processing device may adapt, modify, adjust, or otherwise control one or more characteristics based on treatment plan inputs. For example, the treatment plan input may indicate at least one modification to one or more properties of treatment device 70 . The processing device may modify one or more characteristics of treatment device 70 based on at least one modification indicated by the treatment plan input.

FIG. 10 generally illustrates an alternative method 1000 for monitoring performance of a treatment plan by a user using a treatment device and selectively modifying one or more characteristics of the treatment plan and treatment device, according to the present disclosure. FIG. 4 is an exemplary flow diagram; Method 1000 includes acts performed by a processor of a computing device (eg, any component of FIG. 1, such as server 30 running artificial intelligence engine 11). In some embodiments, one or more operations of method 1000 are implemented in computer instructions stored on a memory device and executed by a processing device. Method 1000 may be performed in the same or similar manner as described above with respect to method 900 . The acts of method 1000 may be performed in any combination with the acts of any of the methods described herein.

At 1002, the processing device may receive first therapy data regarding a user implementing a therapy plan using a therapy device, such as therapy device 70, during a telemedicine session. The first treatment data includes at least measurement information related to the user while the user uses the treatment device 70 to implement the treatment plan. The first therapy data is sensor data from one or more of the external sensors, such as external sensors 82 , 84 , 86 and/or from one or more internal sensors, such as internal sensor 76 of therapy device 70 . 136 may correspond to sensor data.

In some embodiments, at least a portion of the first therapy data is obtained from one or more sensors associated with one or more corresponding wearable devices worn by the user while using therapy device 70. may include sensor data from The one or more wearable devices may include watches, bracelets, necklaces, chest straps, head sweatbands, wrist sweatbands, any other suitable sweatbands, and other suitable wearable devices, or combinations thereof. . One or more wearable devices may be configured to monitor a user's heart rate, temperature, blood pressure, one or more vital signs, etc. while the user is using therapy device 70 .

At 1004, the processing device may generate first therapy information using the first therapy data. The first treatment information may include a summary of the user's implementation of the treatment plan while using the treatment device 70 . The first treatment information may be formatted such that the first treatment data is presentable at a computing device of a healthcare provider responsible for implementing the treatment plan by the user.

At 1006, the processing device writes the first therapy information to an associated memory for access at the healthcare provider's computing device and/or writes the first therapy information at the healthcare provider's computing device. can be configured to provide At 1008, the processing device can be configured to provide the first treatment information at an interface of the healthcare provider's computing device. For example, the processing device may be configured to provide the first treatment information in patient profile display 130 of overview display 120 . As described, overview display 120 may be configured to receive input, such as treatment plan input, indicative of one or more modifications to the treatment plan and/or one or more characteristics of treatment device 70 . A healthcare provider may interact with various controls, input fields, and other aspects of overview display 120 to provide treatment plan inputs.

At 1010, the processing device can receive a first treatment plan input in response to the first treatment information. The first treatment plan input may indicate at least one modification to the treatment plan. In some embodiments, the first treatment plan input may be provided by a healthcare provider as described. In some embodiments, based on the first treatment information, artificial intelligence engine 11 may generate a first treatment plan input.

At 1012, the processing device may modify the treatment plan in response to receiving a first treatment plan input including at least one modification to the treatment plan. For example, the processing device may modify various features and characteristics of the treatment plan based on at least one modification indicated by the first treatment plan input.

At 1014, the processing device may selectively control treatment device 70 using the modified treatment plan. For example, the processing device may modify one or more characteristics of treatment device 70 based on modifications to the treatment plan. Additionally or alternatively, the processing device may adapt, modify, adjust or otherwise control one or more characteristics based on the first treatment plan input. For example, the first treatment plan input may indicate at least one modification to one or more properties of treatment device 70 . The processing device may modify one or more characteristics of treatment device 70 based on at least one modification indicated by the first treatment plan input.

At 1016, the processing device may receive a second treatment plan input in response to second treatment information generated using the second treatment data. For example, the processing device may receive second therapy data regarding the user while the user is using therapy device 70 . The second therapy data may include therapy data received by the processing device after the first therapy data. In some embodiments, the second treatment data may relate to the user while the user uses treatment device 70 to implement the modified treatment plan.

In some embodiments, the second treatment data is the time during which the user uses the treatment device 70 to implement the treatment plan (eg, if the healthcare provider does not modify the treatment plan, as described). It can be user related. The processing device may generate second therapy information based on the second therapy data. The processing device may receive a second treatment plan input indicative of at least one modification to the treatment plan.

As described, the processing device may transfer the second therapy information to any other suitable section, portion, or component of patient profile display 130 and/or summary display 120, or any other suitable display or can be configured to provide an interface. A healthcare provider (eg, and/or artificial intelligence engine 11) may review the second treatment information and determine whether to modify and/or further modify the treatment plan based on the second treatment information. .

At 1018, using the second treatment plan input, the processing device may modify the treatment plan. For example, the processing device further modifies various features and characteristics of the treatment plan based on at least one modification indicated by the second treatment plan input (e.g., if the processing device has already modified the treatment plan ) and/or modify (eg, if the processing device has not previously modified the treatment plan).

At 1020, the processing device may selectively control the treatment device 70 using the modified treatment plan. For example, the processing device may modify one or more characteristics of treatment device 70 based on modifications to the treatment plan. Additionally or alternatively, the processing device may adapt, modify, adjust or otherwise control one or more characteristics based on the second treatment plan input. For example, the second treatment plan input may indicate at least one modification to one or more properties of treatment device 70 . The processing device may modify one or more characteristics of treatment device 70 based on at least one modification indicated by the second treatment plan input.

FIG. 11 generally illustrates an alternative method 1100 for monitoring performance of a treatment plan by a user using a treatment device and selectively modifying one or more characteristics of the treatment plan and treatment device, according to the present disclosure. FIG. 4 is an exemplary flow diagram; Method 1100 includes acts performed by a processor of a computing device (eg, any component of FIG. 1, such as server 30 running artificial intelligence engine 11). In some embodiments, one or more operations of method 1100 are implemented in computer instructions stored on a memory device and executed by a processing device. Method 1100 may be performed in the same or similar manner as described above with respect to method 900 and/or method 1000. The acts of method 1100 may be performed in any combination with the acts of any of the methods described herein.

At 1102 , the processing device may receive treatment data regarding a user implementing a treatment plan using a treatment device, such as treatment device 70 . Treatment data can include any of the data described herein. The therapy data may be from one or more of the external sensors, such as external sensors 82 , 84 , 86 and/or from one or more internal sensors, such as internal sensor 76 of therapy device 70 , such as sensor data 136 . It can correspond to sensor data. In some embodiments, at least a portion of the therapy data is sensor data from one or more sensors associated with one or more corresponding wearable devices worn by the user while using the therapy device 70. may contain data. The one or more wearable devices include watches, bracelets, necklaces, chest straps, head sweatbands, wrist sweatbands, any other suitable sweatbands, and any other suitable wearable devices, or combinations thereof. can contain. One or more wearable devices may be configured to monitor a user's heart rate, temperature, blood pressure, one or more vital signs, etc. while the user is using therapy device 70 .

At 1104, the processing device may generate therapy information using the therapy data. Treatment information may include a summary of the user's implementation of a treatment plan while using treatment device 70 . The treatment information may be formatted such that the treatment data is presentable at the computing device of the healthcare provider responsible for implementing the treatment plan by the user.

At 1106 , the processing device may be configured to provide treatment information to at least one of the healthcare provider's computing device and the machine learning model executed by the artificial intelligence engine 11 .

At 1108, the processing device may receive treatment plan input in response to the treatment information. The treatment plan input may indicate at least one modification to the treatment plan. In some embodiments, treatment plan input may be provided by a healthcare provider as described. In some embodiments, based on the treatment information, artificial intelligence engine 11 executing machine learning models may generate treatment plan inputs.

At 1110, the processing device determines whether the treatment plan input indicates at least one modification to the treatment plan. If the processing device determines that the treatment plan input does not dictate at least one modification to the treatment plan, the processing device returns to 1102 to prompt the user to perform the treatment plan while using the treatment device 70 to implement the treatment plan. Continue to receive treatment data about If the processing device determines that the treatment plan input indicates at least one modification to the treatment plan, the processing device continues at 1112 .

At 1112, using the treatment plan input, the processing device may modify the treatment plan. For example, using at least one modification to the treatment plan indicated by the treatment plan input, the processing device may modify the treatment plan. Based on at least one modification indicated by the treatment plan input, the treatment device may modify various features and characteristics of the treatment plan.

At 1114 , the processing device may selectively control treatment device 70 using the modified treatment plan. For example, the processing device may modify one or more characteristics of treatment device 70 based on at least one modification to the treatment plan. Additionally or alternatively, the processing device may adapt, modify, adjust, or otherwise control one or more characteristics based on treatment plan inputs. For example, the treatment plan input may indicate at least one modification to one or more properties of treatment device 70 . Based on at least one modification indicated by the treatment plan input, the processing device may modify one or more characteristics of treatment device 70 . The processing device may return to 1102 and continue to receive treatment data regarding the user while the user uses the treatment device 70 to implement the treatment plan.

FIG. 12 generally illustrates an example computer system 1200 capable of performing any one or more of the methods described herein, in accordance with one or more aspects of the disclosure. In one example, the computer system 1200 includes computing devices such as an assistance interface 94, a reporting interface 92, a supervisory interface 90, a clinician interface 20, a server 30 (including the AI engine 11), a patient interface 50, and a gait sensor 82. , goniometer 84, treatment device 70, pressure sensor 86, or any suitable component of FIG. Computer system 1200 may be capable of executing instructions implementing one or more machine learning models 13 of artificial intelligence engine 11 of FIG. Computer systems may be connected (eg, networked) to other computer systems within a LAN, intranet, extranet, or Internet, including via a cloud or peer-to-peer network.

A computer system can operate in the capacity of a server in a client-server network environment. Computer systems include personal computers (PCs), tablet computers, wearables (e.g. wristbands), set-top boxes (STBs), personal digital assistants (PDAs), mobile phones, cameras, camcorders, Internet of Things (IoT) devices. , or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, although only a single computer system is illustrated, the term "computer" can also refer to a set of instructions (or sets of instructions) for performing any one or more of the methods discussed herein. ), individually or jointly, to include any collection of computers.

Computer system 1200 includes a processing device 1202, a main memory 1204 (e.g., dynamic random memory such as read-only memory (ROM), flash memory, solid-state drive (SSD), synchronous DRAM (SDRAM), etc.) that communicate with each other via bus 1110. access memory (DRAM)), static memory 1206 (eg, flash memory, solid state drive (SSD), static random access memory (SRAM)), and data storage device 1208 .

Processing device 1202 represents one or more general purpose processing devices such as microprocessors, central processing units, and the like. More specifically, processing device 1202 implements a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, or other instruction set. , or a processor that implements a combination of instruction sets. Processing device 1402 may also be one or more dedicated processing devices such as an application specific integrated circuit (ASIC), system-on-chip, field programmable gate array (FPGA), digital signal processor (DSP), network processor, etc. may Processing device 1402 is configured to execute instructions to perform any of the operations and steps discussed herein.

Computer system 1200 may further include network interface device 1212 . Computer system 1200 also supports a video display 1214 (e.g., liquid crystal display (LCD), light emitting diode (LED), organic light emitting diode (OLED), quantum LED, cathode ray tube (CRT), shadow mask CRT, aperture grill CRT, monochrome). CRT), one or more input devices 1216 (eg, keyboard and/or mouse, or game-like controls), and one or more speakers 1218 (eg, speakers). In one illustrative example, video display 1214 and input device 1216 may be combined into a single component or device (eg, an LCD touchscreen).

Data storage device 1216 may include computer readable media 1220 having instructions 1222 stored thereon that embody any one or more of the methods, acts, or functions described herein. Instructions 1222 may also reside completely or at least partially within main memory 1204 and/or processing device 1202 during execution of instructions 1222 by computer system 1200 . Thus, main memory 1204 and processing device 1202 also constitute computer-readable media. Instructions 1222 may also be transmitted or received over a network via network interface device 1212 .

Although computer-readable storage medium 1220 is generally illustrated in illustrative examples as being a single medium, the term "computer-readable storage medium" refers to a single unit that stores one or more sets of instructions. It should be understood to include a medium or multiple mediums (eg, centralized or distributed databases and/or associated caches and servers). The term "computer-readable storage medium" also means any medium capable of storing, encoding or carrying a set of instructions for execution by a machine and causing the machine to perform any one or more of the methodologies of the present disclosure. shall be construed to include the medium of Accordingly, the term "computer-readable storage medium" shall be taken to include, but not be limited to, solid state memory, optical media, and magnetic media.

specific characteristics (e.g., demographic, geographic, diagnostic, measurement- or trial-based, medical history, etiology, cohort-related, differential diagnosis, surgical, physiotherapeutic, pharmacological, and recommended Determining the optimal treatment regimen for a patient with other therapies such as hemorrhagic cancer can be a technically challenging problem. For example, a large amount of information can be considered when determining a treatment plan, which can lead to inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitation setting, some of the many pieces of information that are considered may include the patient's injury type, types of available medical procedures to perform, therapeutic regimens, drug regimens, and patient characteristics. Patient characteristics can be extensive and include patient medications, previous injuries of the patient, previous medical procedures performed on the patient, measurements of the patient (e.g., body fat, weight, etc.), allergies of the patient, It may include the patient's medical condition, the patient's historical information, the patient's vital signs (eg, temperature, blood pressure, heart rate), the patient's symptoms, the patient's family medical information, and the like.

Furthermore, in addition to the information described above, it may be desirable to consider additional historical information, such as clinical information regarding the results of treatment plans performed on other people using the treatment device. Clinical information can include clinical studies, clinical trials, evidence-based guidelines, journal articles, meta-analyses, and the like. Clinical information may be written by people with specific professional status (eg, MD, osteopathic physician, physical therapist, etc.), certifications, and the like. Clinical information may be obtained from any suitable data source.

In some embodiments, clinical information may describe people seeking treatment for a particular ailment (eg, injury, disease, any applicable medical condition, etc.). Clinical information indicates that a particular result is or when a person performs a particular treatment regimen (e.g., a medical procedure, a treatment protocol using a therapeutic device, a drug regimen, a dietary regimen, etc.) on a person. You can describe what you get. Clinical information can also include specific characteristics of the people described. A direct or indirect reference may be made to the value of the property in the clinical information. When it is desirable to compare patient characteristics with those of people in clinical information to determine what the patient's optimal treatment plan might be so that the patient can achieve the desired outcome There is Processing this historical information may be computationally intensive, inefficient, and/or infeasible using conventional techniques.

Accordingly, embodiments of the present disclosure relate to recommending optimal treatment plans using real-time and historical data correlation with patient cohort-sized databases. In some embodiments, an artificial intelligence engine can be trained to recommend optimal treatment plans based on patient characteristics and clinical information. For example, an artificial intelligence engine can be trained to match patterns between patient and people characteristics in various clinical information. Based on the patterns, the artificial intelligence engine can generate a treatment plan for the patient, which treatment plan produced desired results in the like-matched person or the clinical information of like-matched people. In that sense, the treatment plan generated can be "optimal" based on desired results (eg, speed, effectiveness, both speed and effectiveness, life expectancy, etc.). In other words, based on patient characteristics, there may be specific medical procedures, specific medications, specific rehabilitation exercises, etc. that should be included in the optimal treatment plan to achieve the desired results. .

Depending on what results are desired, the artificial intelligence engine can be trained to output some optimal or optimized treatment plan. For example, one outcome may include recovery to a threshold level (e.g., 75% range of motion) in the fastest amount of time, while another outcome may include full recovery irrespective of the amount of time (e.g. , 100% range of motion). The clinical information is such that a first treatment plan provides a first outcome for people with characteristics similar to those of the patient and a second treatment plan provides a second outcome for people with characteristics similar to the patient. It can be shown to provide results.

Additionally, the artificial intelligence engine may also be trained to output treatment plans that are not optimal for the patient (referred to as "rejected treatment plans"). For example, if a patient has diabetes, a particular drug may not be approved or suitable for the patient, and that drug may be flagged in the patient's excluded treatment regimen.

As discussed above, processing patient and clinical information in real-time may be impractical using conventional techniques simply due to the amount of data to be processed. Accordingly, in some embodiments, received clinical and/or patient information may be converted into a medical descriptive language. A medical description language may refer to an encoding that is configured to be efficiently processed by an artificial intelligence engine. For example, a clinical test can be received and interpreted, optionally with the addition of attribute grammars, and then keywords related to target information can be searched. A target information value may be identified. A canonical form defined by the medical description language may be defined and/or generated, the canonical form comprising tags identifying values of target information and optionally tags implementing the attribute grammar of the medical description language. include.

Medical description languages are extensible and may include any property of object-oriented or artificial intelligence programming languages. The medical description language may define other methods or procedures. A medical description language can include data in the form of fields (often known as attributes or properties) and code in the form of procedures (often known as methods). We can implement the concept of an "object". A medical description language may encapsulate data and functions that manipulate the data to protect the data from interference and misuse. Medical description languages may also implement data hiding or obfuscation, which prevents certain aspects of data or functions from being accessible to another component. A medical description language may implement inheritance that arranges components as an "is a type of" relationship, where a first component may be a type of a second component, where the first component is the second component's Inherit functions and data. Medical description languages may also implement polymorphism, which is the provision of a single interface to different kinds of components.

The clinical information may be transformed into a medical descriptive language before the artificial intelligence engine determines the optimal and/or ruled out treatment plans. The artificial intelligence engine uses a medical descriptive language to represent clinical information so that the artificial intelligence engine can more efficiently determine an optimal treatment plan without using the initial data format in which the clinical information is received. can be trained by Additionally, the artificial intelligence engine may receive clinical information continuously or intermittently, and the clinical information may be included in the training data to update the artificial intelligence engine.

In some embodiments, optimal treatment plans and/or ruled out treatment plans may be presented to medical personnel. A medical practitioner may select a particular optimal treatment plan and have the patient transmit that treatment plan to the patient. In some embodiments, the artificial intelligence engine is remote from the source of clinical information to facilitate telehealth or telemedicine applications, including remote diagnosis, treatment plan determination, and rehabilitation and/or pharmacological prescribing. and/or remotely from the patient. In such cases, the recommended and/or ruled out treatment plans are presented in real-time and concurrently with the patient's video during the telemedicine or telehealth session on the user interface of the healthcare professional's computing device. obtain. Videos may also be accompanied by audio, text, and other multimedia information. Real time may refer to less than 2 seconds.

Presenting the treatment plan generated by the artificial intelligence engine at the same time as presenting the patient video allows the healthcare professional to visually and/or otherwise communicate with the patient while also reviewing the treatment plan on the same user interface. can provide an enhanced user interface. An enhanced user interface may improve the experience of healthcare professionals using the computing device and may encourage healthcare professionals to reuse user interfaces. Such techniques also benefit from computing resources (e.g., processing , memory, network). The artificial intelligence engine dynamically provides optimal treatment plans and excluded treatment plans on the fly.

In some embodiments, the therapy device may be adaptive and/or individualized, as the properties, configuration, and location of the therapy device may be adapted to the needs of a particular patient. For example, the pedal may be used to increase or decrease range of motion to comply with a treatment plan designed for the user (e.g., via a telemedicine session or upon detection of certain measurements). In response, it can be dynamically adjusted on the fly (based on programmed configuration). Such adaptive properties can improve patient recovery outcomes.

Clause 1. 1. A method of receiving treatment data about a user using a treatment device to implement a treatment plan, the treatment data comprising characteristics of the user, measurement information about the user while the user is using the treatment device, receiving including at least one of characteristics of a treatment device and at least one aspect of a treatment plan; generating treatment information using treatment data; and a healthcare provider computing device. writing the treatment information to an associated memory for access in and communicating with an interface at the healthcare provider's computing device, the interface configured to receive treatment plan input; and modifying at least one aspect of the treatment plan in response to receiving treatment plan input including at least one modification to at least one aspect of the treatment plan; Method.

Clause 2. A method according to any clause herein, further comprising controlling the treatment device based on the modified at least one aspect of the treatment plan while the user is using the treatment device.

Article 3. Any provision of the present specification further comprising controlling the therapeutic device based on the modified at least one aspect of the treatment plan while the user is using the therapeutic device during a telemedicine session. Method.

Article 4. A method according to any clause herein, wherein the measurement information includes at least one of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, and the user's blood pressure.

Article 5. A method according to any clause herein, wherein at least some of the therapy data corresponds to sensor data from a sensor associated with a therapy device.

Clause 6. A method according to any clause herein, wherein at least a portion of the therapy data corresponds to sensor data from a sensor associated with the wearable device worn by the user while using the therapy device.

Article 7. A method according to any clause herein, further comprising receiving subsequent treatment data about the user while the user uses the treatment device to implement the treatment plan.

Article 8. modifying the modified treatment plan in response to receiving subsequent treatment plan input including at least one further modification to the modified at least one aspect of the treatment plan; A method according to any clause herein, wherein the input is based on at least one of therapeutic data and subsequent therapeutic data.

Article 9. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to receive treatment data relating to a user implementing a treatment plan using the treatment device. wherein the treatment data includes at least one of characteristics of the user, measurement information about the user while the user is using the treatment device, characteristics of the treatment device, and at least one aspect of the treatment plan. using the therapy data to generate therapy information; writing the therapy information to an associated memory for access at the healthcare provider's computing device; in communication with an interface in the device, the interface configured to receive treatment plan input; and receiving treatment plan input including at least one modification to the treatment plan. and modifying at least one aspect of the treatment plan in response to.

Clause 10. According to any clause herein, the processing device is further configured to control the treatment device based on the modified at least one aspect of the treatment plan while the user is using the treatment device. A computer readable medium as described.

Clause 11. of the present invention, wherein the processing device is further configured to control the treatment device based on the modified at least one aspect of the treatment plan while the user uses the treatment device during the telemedicine session. A computer-readable medium as described in any clause.

Clause 12. The computer readable according to any clause herein, wherein the measurement information includes at least one of a user's vital signs, a user's respiratory rate, a user's heart rate, a user's body temperature, and a user's blood pressure. medium.

Article 13. A computer readable medium according to any clause herein, wherein at least some of the therapy data corresponds to sensor data from a sensor associated with a therapy device.

Article 14. A computer readable according to any clause herein, wherein at least a portion of the therapy data corresponds to sensor data from a sensor associated with the wearable device worn by the user while using the therapy device. medium.

Article 15. The computer readable according to any clause herein, wherein the processing device is further configured to receive subsequent treatment data regarding the user while the user uses the treatment device to implement the treatment plan. medium.

Article 16. The processing device is further configured to modify the modified at least one aspect of the treatment plan in response to receiving subsequent treatment plan input including at least one further modification to the treatment plan; The computer readable medium according to any clause herein, wherein the subsequent treatment plan input is based on at least one of treatment data and subsequent treatment data.

Article 17. A system comprising a memory device storing instructions and a processing device communicatively coupled to the memory device, the processing device executing the instructions to generate a treatment plan using the treatment device. receiving treatment data about a user to administer, the treatment data comprising characteristics of the user, measurement information about the user while the user is using the treatment device, characteristics of the treatment device, and at least one aspect of the treatment plan; generating treatment information using the treatment data; and storing the treatment information in an associated memory for access at the healthcare provider's computing device. and communicating with an interface at the healthcare provider's computing device, the interface configured to receive a treatment plan input; modifying at least one aspect of the treatment plan in response to receiving treatment plan input including the modification.

Article 18. According to any clause herein, the processing device is further configured to control the treatment device based on the modified at least one aspect of the treatment plan while the user is using the treatment device. System as described.

Article 19. of the present invention, wherein the processing device is further configured to control the treatment device based on the modified at least one aspect of the treatment plan while the user uses the treatment device during the telemedicine session. A system as described in any clause.

Clause 20. The system according to any clause herein, wherein the measurement information includes at least one of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, and the user's blood pressure.

Article 21. A system according to any clause herein, wherein at least some of the therapy data corresponds to sensor data from a sensor associated with a therapy device.

Article 22. A system according to any clause herein, wherein at least some of the therapy data corresponds to sensor data from sensors associated with the wearable device worn by the user while using the therapy device.

Article 23. A system according to any clause herein, wherein the processing device is further configured to receive subsequent treatment data regarding the user while the user uses the treatment device to implement the treatment plan.

Article 24. The processing device modifies the modified at least one aspect and any other aspect of the treatment plan in response to receiving subsequent treatment plan input including at least one further modification to the treatment plan. and the subsequent treatment plan input is based on at least one of the treatment data and the subsequent treatment data. system.

Methods and Systems for Describing and Recommending Optimal Treatment Plans in Adaptive Telemedical or Other Contexts FIG. indicates Managing treatment plans may include using an artificial intelligence engine to recommend optimal treatment plans and/or provide excluded treatment plans that should not be recommended to patients. A treatment plan can include one or more treatment protocols, each treatment protocol including one or more treatment sessions. Each treatment session includes several session durations, each session duration including specific activities for treating a patient's body part. For example, the treatment plan for postoperative rehabilitation after knee surgery was an initial treatment protocol with twice daily stretching sessions for the first three days after surgery and four times daily starting on the fourth day after surgery. and a more intensive treatment protocol with active exercise sessions performed. The treatment plan may also include information regarding medical procedures to be performed on the patient, the patient's treatment protocol using the treatment device, the patient's dietary regimen, the patient's drug regimen, the patient's sleep regimen, additional regimens, or any combination thereof. may include

System 2010 also includes a server 2030 configured to store and provide data related to managing treatment plans. Server 2030 may include one or more computers and may take the form of one or more distributed and/or virtualized computers. Server 2030 also includes a first communication interface 2032 configured to communicate with clinician interface 2020 via first network 2034 . In some embodiments, first network 2034 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like. The server 2030 includes a first processor 2036 and a first machine-readable storage memory 2038, which may be abbreviated as "memory" and implemented by the first processor 2036. contains first instructions 2040 for performing various actions of server 2030 for Server 2030 is configured to store data relating to treatment plans. For example, memory 2038 includes system data store 2042 configured to hold system data, such as data relating to treatment plans for treating one or more patients. Server 2030 is also configured to store data regarding the patient's performance in accordance with the treatment plan. For example, memory 2038 includes a patient data store 2044 configured to hold patient data, such as data relating to one or more patients, including data representing each patient's performance within a treatment plan.

In addition, the characteristics of the people, the treatment plans they follow, the level of compliance with the treatment plans, and the results of the treatment plans can be used to compare the treatment plans to the patient data store using correlations and other statistical or probabilistic measures. 2044 can be divided into databases corresponding to different patient cohorts. For example, a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment regimen followed by a first patient, and a first outcome of the treatment regimen. Data relating to the first cohort of the first patient having can be stored in the first patient database. a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment regimen followed by the second patient, and a second outcome of the treatment regimen; Data for a second cohort of a second patient can be stored in a second patient database. Any combination of characteristics may be used to segregate cohorts of patients. In some embodiments, different cohorts of patients may be stored in different partitions or volumes of the same database.

This characterization data, treatment plan data, and outcome data may be obtained from clinical information describing the characteristics of people who have undergone particular treatment plans and the results of those treatment plans. Characteristic data, treatment plan data, and outcome data may be correlated in a patient-cohort database within patient data store 2044 . People characteristics include drugs prescribed to people, injuries to people, medical procedures performed on people, measurements of people, allergies of people, medical conditions of people, historical information of people, vital signs of people , the people's symptoms, the people's family medical information, the people's other information, or some combination thereof.

In addition to historical information about other people stored in patient cohort-based databases, real-time information based on current patient characteristics about current patients being treated can be stored in appropriate patient cohort-based databases. . Patient characteristics include patient medications, patient injuries, medical procedures performed on the patient, patient measurements, patient allergies, patient medical conditions, patient history, patient vital signs, patient It may include symptoms, medical information of the patient's family, other information of the patient, or some combination thereof.

In some embodiments, the server 2030 uses one or more machine learning models 2013 to run an artificial intelligence (AI) engine 2011 that implements at least one of the embodiments disclosed herein. can be implemented. Server 2030 may include training engine 9 capable of generating one or more machine learning models 2013 . The machine learning model 2013 can be trained to generate and recommend optimal treatment plans using, among other things, real-time and historical data correlation, including patient cohort equivalents. One or more machine learning models 2013 may be generated by training engine 209 and implemented in computer instructions executable by one or more processing devices of training engine 209 and/or server 2030 . Training engine 209 may train one or more machine learning models 2013 to generate one or more machine learning models 2013 . One or more machine learning models 2013 may be used by artificial intelligence engine 2011 .

The training engine 209 can be a rackmount server, router computer, personal computer, portable digital assistant, smart phone, laptop computer, tablet computer, netbook, desktop computer, Internet of Things (IoT) device, or any other desired computing device. device, or any combination of the above. Training engine 9 may be a cloud-based or real-time software platform and may include privacy software or protocols and/or security software or protocols.

To train one or more machine learning models 2013, the training engine 209 may use a training dataset of a corpus of keywords representing target information to identify clinical information. The training data set may also include a corpus of clinical information (eg, clinical trials, meta-analyses, evidence-based guidelines, journal articles, etc.) having a first data format. Clinical information can include, among other things, the characteristics of people, the treatment plans they follow, and the results of treatment plans. The training data set may also include example medical description languages, including tags for target information, telemedical information, and values embedded with the tags. One or more machine learning models can be trained to transform clinical information from the first data format into a machine description language having a canonical (eg, tag-value pair and/or attribute grammar) format. Training may be performed by identifying keywords for the targeting information, identifying values for the keywords, and generating canonical values that include the tags for the targeting information and the values for the targeting information.

One or more machine learning models 2013 also convert patient characteristics received in real-time (e.g., from an electronic medical record (EMR) system) into a medical descriptive language for storage in appropriate patient cohort equivalent databases. may be trained to One or more machine learning models 2013 can be trained to match patterns of patient characteristics described by the medical descriptive language with characteristics of other people described by the medical descriptive language representing clinical information. In some embodiments, medical descriptive languages representing clinical information may be stored in various patient cohort-equivalent databases of patient data store 2044 . Thus, in some embodiments, one or more machine learning models 2013 may access a patient cohort equivalent database when trained or when recommending an optimal treatment plan for a patient. Computing resources, processing efficiency, accuracy, and error minimization may be enhanced using medical descriptive languages in canonical form as opposed to the full body of text and/or EMR records. Accuracy can be improved and errors minimized, particularly through the use of formal medical descriptive language, which can be interpreted to have one meaning, whereas informal descriptive is semantically overloaded and can lead to unsolvable meanings.

Different machine learning models 2013 can be trained to recommend different optimal treatment plans for different desired outcomes. For example, one machine learning model can be trained to recommend the optimal treatment plan for the most effective recovery, while another machine learning model recommends the optimal treatment plan based on speed of recovery. can be trained to

Using training data, including training inputs and corresponding target outputs, one or more machine learning models 2013 may reference model artifacts created by training engine 209 . Training engine 209 may find patterns in the training data that map training inputs to target outputs and produce machine learning models 2013 that incorporate these patterns. In some embodiments, artificial intelligence engine 2011, database 2033, and/or training engine 209 may reside in separate components depicted in FIG. 13 (eg, assistant interface 2094, clinician interface 2020, etc.). .

As described in more detail below, the one or more machine learning models 2013 may include, for example, single-level linear or nonlinear operations (eg, support vector machines (SVMs)), machine learning models 2013 may be a deep network, ie, a machine learning model that includes 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 is the neuron's output signal to the inputs of the remaining neurons and to itself). For example, a machine learning model may include multiple layers and/or hidden layers that perform computations (eg, dot products) using various neurons.

System 2010 also includes a patient interface 2050 configured to communicate information to the patient and receive feedback from the patient. Specifically, the patient interface includes an input device 2052 and an output device 2054, which may be collectively referred to as patient user interfaces 2052,2054. Input devices 2052 may include one or more devices such as a keyboard, mouse, touch screen input, gesture sensors, and/or microphones and processors configured for speech recognition. Output device 2054 can take one or more different forms, including, for example, a computer monitor or display screen on a tablet, smartphone, or smartwatch. Output device 2054 may include other hardware and/or software components such as projectors, virtual reality capabilities, augmented reality capabilities, and the like. Output device 2054 may incorporate a variety of different visual, audio, or other presentation technologies. For example, output device 2054 includes non-visual displays such as audio signals that may include speech and/or other sounds such as tones, chimes, and/or melodies that may signal various conditions and/or directions. obtain. Output device 2054 may comprise one or more different display screens presenting various data and/or interfaces or controls for use by the patient. Output device 2054 may include graphics, which may be presented by a web-based interface and/or by computer programs or applications (apps).

As shown in FIG. 13, patient interface 2050 is a second communication interface, also referred to as a remote communication interface, configured to communicate with server 2030 and/or clinician interface 2020 via second network 2058. 2056 included. In some embodiments, second network 2058 may include a local area network (LAN), such as an Ethernet network. In some embodiments, second network 2058 may include the Internet, and communication between patient interface 2050 and server 2030 and/or clinician interface 2020 may use, for example, a virtual private network (VPN). It may be secured via encryption, such as by In some embodiments, second network 2058 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like. In some embodiments, second network 2058 may be the same as first network 2034 and/or may be operatively coupled to first network 2034 .

The patient interface 2050 includes a second processor 2060 and a second machine-readable storage memory 2062 that retains second instructions 2064 for execution by the second processor 2060 to perform various actions of the patient interface 2050. and including. The second machine-readable storage memory 2062 also includes a local data store 2066 configured to hold patient data, such as data relating to treatment plans and/or data representing patient performance within treatment plans. . The patient interface 2050 also includes a local communication interface 2068 configured to communicate with various devices for use by the patient in the vicinity of the patient interface 2050. Local communication interface 2068 may include wired and/or wireless communications. In some embodiments, local communication interface 2068 may include local wireless networks, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like.

The system 2010 also includes a therapy device 2070 configured to be manipulated by the patient and/or to manipulate body parts of the patient to perform activities according to the treatment plan. In some embodiments, the therapy device 2070 can take the form of an exercise and rehabilitation device configured to implement and/or assist in the implementation of a rehabilitation regimen, which can be an orthopedic rehabilitation regimen, wherein the treatment is , rehabilitation of patient body parts such as joints or bones or muscle groups. Body parts can include, for example, the spine, hands, feet, knees, or shoulders. A body part may include a portion of a joint, bone, or muscle group, such as one or more vertebrae, tendons, or ligaments. As shown in FIG. 13, therapy device 2070 includes controller 2072, which may include one or more processors, computer memory, and/or other components. Therapy device 2070 also includes a fourth communication interface 2074 configured to communicate with patient interface 2050 via local communication interface 2068 . The therapeutic device 2070 also includes one or more internal sensors 2076 and actuators 2078, such as motors. Actuators 2078 may be used, for example, to move a patient's body part and/or resist force by the patient.

Internal sensors 2076 may measure one or more operational characteristics of therapy device 2070 such as, for example, force position, velocity, and/or velocity. In some embodiments, internal sensor 2076 may include a position sensor configured to measure at least one of linear or angular motion of the patient's body part. For example, an internal sensor 2076 in the form of a position sensor can measure the distance a patient can move a portion of the therapy device 2070, such distance being the range of motion that a patient's body part can achieve. can correspond to In some embodiments, internal sensor 2076 may include a force sensor configured to measure force applied by the patient. For example, an internal sensor 2076 in the form of a force sensor may measure the force or load that a patient can apply to therapy device 2070 using a particular body part.

System 10 shown in FIG. 13 also includes a gait sensor 2082 that communicates with server 2030 via local communication interface 2068 of patient interface 2050 . Gait sensor 2082 may track and store the number of steps taken by the patient. In some embodiments, the gait sensor 2082 may take the form of a wristband, wristwatch, or smartwatch. In some embodiments, the gait sensor 2082 may be integrated within a phone such as a smart phone.

System 2010 shown in FIG. 13 also includes goniometer 2084 in communication with server 2030 via local communication interface 2068 of patient interface 2050 . Goniometer 2084 measures the angle of the patient's body part. For example, goniometer 2084 may measure the angle of flexion of the patient's knee or elbow or shoulder.

The system 2010 shown in FIG. 13 also includes a pressure sensor 2086 that communicates with the server 2030 via the local communication interface 68 of the patient interface 2050. The system 2010 shown in FIG. Pressure sensor 2086 measures the amount of pressure or load exerted by the patient's body part. For example, pressure sensor 2086 may measure the amount of force applied by the patient's foot when pedaling a stationary bicycle.

The system 2010 shown in FIG. 13 also includes a supervisory interface 2090 that may be similar or identical to the clinician interface 2020. In some embodiments, supervisory interface 2090 may have enhanced functionality over that provided by clinician interface 2020 . Supervisory interface 2090 may be configured for use by those responsible for treatment planning, such as an orthopedic surgeon.

The system 2010 shown in FIG. 13 also includes a reporting interface 2092 that may be similar or identical to the clinician interface 2020. In some embodiments, the reporting interface 2092 may have less functionality than that provided by the clinician interface 2020. For example, reporting interface 2092 may not have the ability to modify treatment plans. Such reporting interface 2092 may be used, for example, by a claimant to determine use of system 2010 for billing purposes. In another example, reporting interface 2092 does not have the ability to display patient identifiable information, and for certain data fields about data subjects and/or for certain data fields about data subject quasi-identifiers, Only depersonalized and/or anonymized data may be presented. Such a reporting interface 2092 can be used, for example, by researchers to determine different effects of treatment regimens on different patients.

System 2010 includes an assistant interface 2094 for an assistant, such as a doctor, nurse, physical therapist, or technician, to communicate remotely with patient interface 2050 and/or therapy device 2070 . Such remote communication may allow an assistant to use system 2010 to provide assistance or guidance to the patient. More specifically, assistant interface 2094 transmits telemedicine signals 2096, 2097, 2098a, 2098b, 2099a, 2099b via a network connection, such as via first network 2034 and/or second network 2058, for example. configured to communicate with a patient interface 2050; Telemedicine signals 2096, 2097, 2098a, 2098b, 2099a, 2099b include audio signal 2096, audio-visual signal 2097, interface control signal 2098a for controlling the function of patient interface 2050, and monitoring the status of patient interface 2050. It includes one of an interface monitor signal 2098 b , a device control signal 2099 a for changing operating parameters of the therapy device 2070 , and/or a device monitor signal 2099 b for monitoring the status of the therapy device 2070 . In some embodiments, each of control signals 2098 a , 2099 a may be unidirectional and convey commands from assistant interface 2094 to patient interface 2050 . In some embodiments, in response to successfully receiving the control signals 2098a, 2099a and/or communicating successful and/or unsuccessful implementation of the requested control action, the acknowledgment message is It can be sent from interface 2050 to assistant interface 2094 . In some embodiments, each of the monitor signals 2098b, 2099b can be a one-way status information command from the patient interface 2050 to the assistant interface 2094. In some embodiments, an acknowledgment message may be sent from assistant interface 2094 to patient interface 2050 in response to successfully receiving one of monitor signals 2098b, 2099b.

In some embodiments, patient interface 2050 provides device control signals 2099a and device monitor signals 2099b between therapy device 2070 and one or more other devices, such as assistant interface 2094 and/or server 2030. It can be configured as a pass-through. For example, patient interface 2050 may be configured to transmit device control signals 2099a in response to device control signals 2099a in telemedicine signals 2096, 2097, 2098a, 2098b, 2099a, 2099b from assistant interface 2094.

In some embodiments, the assistant interface 2094 may be presented on a shared physical device as the clinician interface 2020. For example, clinician interface 2020 may include one or more screens that implement assistant interface 2094 . Alternatively or additionally, clinician interface 2020 may include additional hardware components, such as video cameras, speakers, and/or microphones, for implementing aspects of assistant interface 2094 .

In some embodiments, one or more portions of telemedicine signals 2096, 2097, 2098a, 2098b, 2099a, 2099b are sourced from pre-recorded sources (such as for example, audio recordings, video recordings, or animations). For example, a tutorial video can be streamed from server 2030 and presented on patient interface 2050 . Content from pre-recorded sources may be requested by the patient via patient interface 2050 . Alternatively, via controls on assistant interface 2094 , the assistant may play content from pre-recorded sources on patient interface 2050 .

Assistant interface 2094 includes assistant input device 2022 and assistant display 2024, which may be collectively referred to as assistant user interfaces 2022, 2024. Assistant input device 2022 may include one or more of a phone, keyboard, mouse, trackpad, or touch screen, for example. Alternatively or additionally, assistant input device 2022 may include one or more microphones. In some embodiments, the one or more microphones may take the form of a telephone handset, headset, or one or more wide area microphones configured for the assistant to speak to the patient via the patient interface 2050. In some embodiments, the assistant input device 2022 includes hardware and/or software configured to interpret instructions spoken by the assistant by using one or more microphones and is voice-based. can be configured to provide the functionality of Assistant input device 2022 may be provided by or include functionality similar to existing voice-based assistants such as Siri from Apple, Alexa from Amazon, Google Assistant, or Bixby from Samsung. Assistant input device 2022 may include other hardware and/or software components. Assistant input device 2022 may include one or more general purpose and/or special purpose devices.

Assistant display 2024 can take one or more different forms, including, for example, a computer monitor or display screen on a tablet, smartphone, or smartwatch. Assistant display 2024 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities. Assistant display 2024 may incorporate a variety of different visual, audio, or other presentation technologies. For example, assistant display 2024 may include non-verbal signals, such as voice signals, which may include speech and/or other sounds, such as tones, chimes, melodies, and/or music, which may signal various conditions and/or directions. A visual display may be included. Assistant display 2024 may comprise one or more different display screens that present various data and/or interfaces or controls for use by the assistant. The assistant display 2024 may include graphics, which may be presented by a web-based interface and/or by computer programs or applications (apps).

In some embodiments, system 2010 may provide computer translation of language from assistant interface 2094 to patient interface 2050 and/or vice versa. Computer conversion of language may include computer conversion of speech and/or computer conversion of text. Additionally or alternatively, system 2010 may provide speech recognition of text and/or vocalizations. For example, system 2010 may convert speech to printed text, and/or system 2010 may vocalize words from printed text. System 2010 may be configured to recognize words spoken by any or all of the patient, clinician, and/or assistant. In some embodiments, the system 2010 can be configured to recognize and respond to requests or commands spoken by the patient. For example, system 2010 may automatically initiate a telemedicine session in response to a verbal command (which may be given in any one of several different languages) by the patient.

In some embodiments, server 2030 may generate aspects of assistant display 2024 for presentation by assistant interface 2094 . For example, server 2030 may include a web server configured to generate display screens for presentation on assistant display 2024 . For example, the artificial intelligence engine 2011 generates a recommended optimal treatment plan and/or a ruled out treatment plan for the patient and uses those recommended optimal treatment plans for presentation on the assistant display 2024 of the assistant interface 2094. A display screen may be generated that includes the treatment plan and/or the treatment plan to be rejected. In some embodiments, assistant display 2024 may be configured to present a virtualized desktop hosted by server 2030 . In some embodiments, server 2030 may be configured to communicate with assistant interface 2094 via first network 2034 . In some embodiments, first network 2034 may include a local area network (LAN), such as an Ethernet network. In some embodiments, first network 2034 may include the Internet, and communication between server 2030 and assistant interface 2094 is by using encryption, for example, over a virtual private network (VPN). may be secured via privacy-enhancing techniques such as; Alternatively or additionally, server 2030 communicates with assistant interface 2094 via one or more networks independent of first network 2034 and/or other means of communication such as direct wired or wireless communication channels. may be configured to communicate. In some embodiments, patient interface 2050 and therapy device 2070 may each operate from a patient location that is geographically remote from the location of assistant interface 2094 . For example, patient interface 2050 and therapy device 2070 may be used as part of a home rehabilitation system, which may be assisted remotely by using assistant interface 2094 at a centralized location such as a clinic or call center.

In some embodiments, the assistant interface 2094 is a number of different terminals (e.g., computing devices) that can be grouped together, e.g., at one or more call centers or at one or more clinician's offices. can be one of In some embodiments, multiple assistant interfaces 2094 may be geographically distributed. In some embodiments, a person may work as an assistant remotely from any conventional office infrastructure. Such remote work may be implemented, for example, when assistant interface 94 takes the form of a computer and/or telephone. This telecommuting functionality may enable telecommuting arrangements, which may include part-time and/or flexible working hours for assistants.

14-15 illustrate one embodiment of a therapeutic device 2070. FIG. More specifically, FIG. 14 shows a therapeutic device 2070 in the form of a stationary cycling machine 2100, which may be called a stationary bike for short. A stationary cycling machine 2100 includes a set of pedals 2102 each mounted on a pedal arm 2104 for rotation about an axis 2106 . In some embodiments, as shown in FIG. 14, pedals 2102 are movable on pedal arms 2104 to adjust the range of motion that the patient uses in pedaling. For example, locating the pedals inward toward the axis 2106 corresponds to a smaller range of motion than when the pedals are locating outward from the axis 2106 . A pressure sensor 2086 is attached to or embedded in one of the pedals 2102 to measure the amount of force applied to the pedals 2102 by the patient. Pressure sensor 2086 may communicate wirelessly to therapy device 2070 and/or to patient interface 2050 .

FIG. 16 shows a person (patient) using the therapy device of FIG. 14, showing sensors and various data parameters connected to the patient interface 2050 . An exemplary patient interface 2050 is a tablet computer or smart phone, such as an iPad, iPhone, Android device, or Surface tablet, or a phablet that is manually held by the patient. In some other embodiments, patient interface 2050 may be embedded in or attached to therapeutic device 2070 . FIG. 16 shows the patient wearing the gait sensor 2082 on the patient's wrist, and a note reading "Today's Steps 21355" indicates that the gait sensor 2082 records its step count and 2050 indicates that it has been transmitted. FIG. 16 also shows the patient wearing a goniometer 2084 on the patient's right knee, and the notation reading "Knee Angle 72°" indicates that the goniometer 2084 measures that knee angle and 2050 indicates that it is transmitting. FIG. 16 also shows the right side of one of the pedals 2102 having a pressure sensor 2086 of “12.5 lbs force”, the right pedal pressure sensor 2086 measuring and transmitting that force measurement to the patient interface 2050. Indicates that the FIG. 16 also shows the left side of one of the pedals 2102 having a pressure sensor 2086 with “27 lbs of force”, the left pedal pressure sensor 2086 measuring and transmitting that force measurement to the patient interface 2050. indicates that FIG. 16 also shows other patient data such as the indication "Session Time 0:04:13" indicating that the patient has been using therapy device 2070 for 4 minutes and 13 seconds. This session time may be determined by patient interface 2050 based on information received from therapy device 2070 . Figure 16 also shows an index labeled "Pain Level 3". Such pain levels may be obtained from patents in response to a petition, such as a question, presented on patient interface 2050 .

FIG. 17 is an illustrative embodiment of summary display 2120 of assistant interface 2094 . Specifically, overview display 2120 presents several different controls and interfaces for the assistant to remotely assist the patient using patient interface 2050 and/or therapy device 2070 . This remote assistance functionality may also be referred to as telemedicine or telehealth.

Specifically, summary display 2120 includes a patient profile display 2130 that presents historical information about the patient using therapy device 2070 . The patient profile display 2130 may take the form of a portion or region of the overview display 2120 as shown in FIG. 17, although the patient profile display 2130 may take other forms such as a separate screen or pop-up window. In some embodiments, the patient profile display 2130 may include a limited subset of patient history information. More specifically, the data presented on the patient profile display 2130 may depend on the Assistant's need for that information. For example, a medical practitioner assisting a patient with a medical issue may be provided with historical medical information about the patient, whereas a technician troubleshooting a problem with the therapy device 2070 may have more information about the patient. A limited set can be provided. For example, a technician may be given only the patient's name. Patient profile display 2130 may include depersonalized and/or anonymized data, or use any privacy-enhancing technology in ways that may violate patient confidentiality requirements; Sensitive patient data may be prevented from being communicated. Such privacy-enhancing technologies include, but are not limited to, the Health Insurance Portability and Accountability Act (HIPAA) or the General Data Protection Regulation (GDPR), where patients may be considered "data subjects." compliance with laws, regulations or other governance rules.

In some embodiments, the patient profile display 2130 may present information regarding the treatment plan to be followed when the patient uses the treatment device 2070. Such treatment plan information may be restricted to medical assistants, such as doctors or physical therapists. For example, a medical practitioner assisting a patient with a problem with a treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting a problem with the treatment device 2070 may not have any information regarding the patient's treatment plan. May not be provided.

In some embodiments, one or more recommended optimal treatment plans and/or excluded treatment plans may be presented to the assistant in the patient profile display 2130. One or more recommended optimal treatment plans and/or ruled out treatment plans may be generated by the artificial intelligence engine 2011 of the server 2030 and received in real-time from the server 2030, particularly during a telemedicine or telehealth session. . An example of presenting one or more recommended optimal treatment plans and/or ruled out treatment plans is described below with reference to FIG.

The exemplary overview display 2120 shown in FIG. 17 also includes a patient status display 2134 that presents status information regarding the patient using the therapy device. The patient status display 2134 may take the form of a portion or region of the overview display 2120, as shown in FIG. 17, although the patient status display 2134 may take other forms, such as a separate screen or pop-up window. Patient status display 2134 includes sensor data 2136 from one or more of external sensors 2082 , 2084 , 2086 and/or from one or more internal sensors 2076 of therapy device 2070 . In some embodiments, the patient status display 2134 may present other data 2138 about the patient, such as last reported pain level or progress within a treatment plan.

What data is available to view and/or modify for any or all of the user interfaces 2020, 2050, 2090, 2092, 2094 of the system 2010 using user access controls? can restrict access, including In some embodiments, user access controls may be used to control what information is available to any given person using system 2010 . For example, data presented on assistant interface 2094 may be controlled by user access controls, with permissions set according to the assistant's/user's needs and/or entitlements to view the information.

The example overview display 2120 shown in FIG. 17 also includes a help data display 2140 that presents information for use by the assistant in assisting the patient. Help data display 2140 may take the form of a portion or region of summary display 2120, as shown in FIG. Help data display 2140 may take other forms, such as a separate screen or pop-up window. Help data display 2140 may include, for example, presenting answers to frequently asked questions regarding the use of patient interface 2050 and/or therapy device 2070 . Help data display 2140 may also include research data or best practices. In some embodiments, the help data display 2140 may present scripts for answers or explanations in response to patient questions. In some embodiments, the help data display 2140 may present a flow chart or walkthrough for the assistant to use in determining the root cause and/or solution to the patient's problem. In some embodiments, assistant interface 2094 may present two or more help data displays 2140, which may be the same or different, to simultaneously present help data for use by the assistant. For example, a first help data display can be used to present a troubleshooting flow chart for determining the root of a patient's problem, and a second help data display can be used for the assistant to read out to the patient: Script information may be presented, preferably information such as instructions for the patient to perform some action, which may help narrow down or solve the problem. In some embodiments, the second help data display may automatically populate the script information based on the input to the troubleshooting flowchart in the first help data display.

The example overview display 2120 shown in FIG. 17 also includes patient interface controls 2150 for presenting information about the patient interface 2050 and/or for modifying one or more settings of the patient interface 2050 . Patient interface controls 2150 may take the form of a portion or region of overview display 2120, as shown in FIG. Patient interface controls 2150 may take other forms, such as separate screens or pop-up windows. Patient interface control 2150 may present the communicated information to assistant interface 2094 via one or more of interface monitor signals 2098b. As shown in FIG. 17, patient interface controls 2150 include a display feed 2152 of displays presented by patient interface 2050 . In some embodiments, display feed 2152 may contain a live copy of the display screen currently being presented to the patient by patient interface 2050 . In other words, display feed 2152 may present an image of what is presented on the display screen of patient interface 2050 . In some embodiments, display feed 2152 may include informal information about the display screen currently being presented by patient interface 2050, such as screen name or screen number. Patient interface controls 2150 may include patient interface settings controls 2154 for an assistant to adjust or control one or more settings or aspects of patient interface 2050 . In some embodiments, patient interface settings control 2154 may cause assistant interface 2094 to generate and/or transmit interface control signals 2098 to control features or settings of patient interface 2050 .

In some embodiments, patient interface settings controls 2154 may include collaborative browsing or co-browsing capabilities for assistants to remotely view and/or control patient interface 2050 . For example, the patient interface settings control 2154 allows the assistant to remotely enter text into one or more text entry fields on the patient interface 2050 and/or using the mouse or touch screen of the assistant interface 2094 to It may be possible to remotely control the cursor on patient interface 2050 .

In some embodiments, using the patient interface 2050, the patient interface settings control 2154 may allow the assistant to change settings that the patient cannot change. For example, the patient interface 2050 may be blocked from accessing language settings to prevent the patient from inadvertently switching the language used for display on the patient interface 2050, whereas the patient interface 2050 may be blocked from accessing language settings. Settings control 2154 may allow the assistant to change the language setting of patient interface 2050 . In another example, the patient interface 2050 may set the font size to prevent the patient from inadvertently switching the font size used for display on the patient interface 2050 such that the display becomes unreadable to the patient. The patient interface settings control 154 may allow the assistant to change the font size setting of the patient interface 50, whereas the setting may not be able to be changed to a smaller size.

The example overview display 2120 shown in FIG. 17 also includes the patient interface 2050 and one or more other devices 2070, 2082, 2084, such as the therapy device 2070, the gait sensor 2082, and/or the goniometer 2084. includes an interface communications display 2156 that indicates the status of communications between. Interface communications display 2156 may take the form of a portion or region of overview display 2120, as shown in FIG. Interface communication display 2156 may take other forms, such as a separate screen or pop-up window. Interface communications display 2156 may include controls for the Assistant to remotely modify communications with one or more of other devices 2070 , 2082 , 2084 . For example, the assistant may reset communication with one of the other devices 2070, 2082, 2084, or establish communication with a new one of the other devices 2070, 2082, 2084. The patient interface 2050 can be commanded remotely. This functionality is useful, for example, if the patient has a problem with one of the other devices 2070, 2082, 2084, or if the patient receives a new or replacement one of the other devices 2070, 2082, 2084. can be used if

The example overview display 2120 shown in FIG. 17 also includes device controls 2160 for the assistant to view and/or control information about the therapy device 2070. Device controls 2160 may take the form of a portion or region of overview display 2120, as shown in FIG. Device control 2160 may take other forms, such as a separate screen or pop-up window. Device control 2160 may include a device status display 2162 with information regarding the current status of the device. Device status display 2162 may present information communicated to assistant interface 2094 via one or more of device monitor signals 2099b. Device status display 2162 may indicate whether therapy device 2070 is currently in communication with patient interface 2050 . Device status display 2162 may present other current and/or historical information regarding the status of therapy device 2070 .

Device controls 2160 may include device settings controls 2164 for the Assistant to adjust or control one or more aspects of therapy device 2070 . Device settings control 2164 may cause assistant interface 2094 to generate and/or transmit device control signals 2099 to change operating parameters of therapy device 2070 (eg, pedal radius settings, resistance settings, target RPM, etc.). The device settings controls 2164 can include a mode button 2166 and a position control 2168 that allow the assistant to put the actuator 2078 of the therapy device 2070 into manual mode and then use the position control 2168 to set the actuator 2078 to It can be used in conjunction so that settings such as position or speed can be changed. A mode button 2166 may provide settings, such as position, for switching between automatic and manual modes. In some embodiments, one or more settings may be adjustable at any time and without an associated auto/manual mode. In some embodiments, the assistant may change the operating parameters of therapy device 2070, such as the pedal radius setting, while the patient is actively using therapy device 2070. Such “on the fly” adjustments may or may not be available to the patient using patient interface 2050 . In some embodiments, the device settings control 2164 may use the patient interface 2050 to allow the assistant to change settings that the patient cannot change. For example, the patient interface 2050 may be prevented from changing pre-configured settings, such as the height or tilt settings of the therapy device 2070, whereas the device settings control 2164 prevents the assistant from changing the therapy device 2070 may offer to change the height or tilt settings of the .

The exemplary overview display 2120 shown in FIG. 17 may also include patient communication controls 2170 for controlling an audio or audiovisual communication session with the patient interface 2050. A communication session with patient interface 2050 may include a live feed from assistant interface 94 for presentation by an output device of patient interface 2050 . A live feed may take the form of an audio feed and/or a video feed. In some embodiments, patient interface 2050 may be configured to provide two-way audio or audiovisual communication with a person using assistant interface 2094 . Specifically, a communication session with patient interface 2050 may include an interactive (two-way) video feed or an audiovisual feed, with patient interface 2050 and assistant interface 2094 each presenting the other's video. In some embodiments, the patient interface 2050 may present video from the assistant interface 2094 while the assistant interface 2094 presents audio only, or the assistant interface 2094 presents live audio from the patient interface 2050. No signal or visual signal is presented. In some embodiments, assistant interface 2094 may present video from patient interface 2050 while patient interface 2050 presents audio only, or patient interface 2050 presents live audio from assistant interface 2094. No signal or visual signal is presented.

In some embodiments, an audio or audiovisual communication session with the patient interface 2050 may occur, at least in part, while the patient is performing a rehabilitation regimen on the body part. Patient communication control 2170 may take the form of a portion or region of overview display 2120, as shown in FIG. Patient communication control 2170 may take other forms, such as a separate screen or pop-up window. Audio and/or audio-visual communication may be via assistant interface 2094 and/or via one or more separate devices such as a telephone system or video conferencing system used by the assistant while the assistant is using assistant interface 2094. , may be processed and/or directed. Alternatively or additionally, voice communication and/or audiovisual communication may include communication with a third party. For example, the system 2010 enables an assistant to initiate a three-way conversation with a patient and a subject matter expert, such as a healthcare professional or specialist, regarding the use of a particular piece of hardware or software. obtain. The example patient communication control 2170 shown in FIG. 17 includes a call control 2172 for use by a healthcare provider in managing various aspects of voice or audiovisual communication with a patient. Call controls 2172 include a disconnect button 2174 for the Assistant to end a voice or audiovisual communication session. Call controls 2172 also include a mute button 2176 for temporarily muting audio or audiovisual signals from assistant interface 2094 . In some embodiments, call control 2172 may include other features such as a hold button (not shown). Call controls 2172 also provide one or more record/playback controls, such as record, play, and pause buttons, for controlling the recording and/or playback of audio and/or video from a conference call session on patient interface 2050 . A playback control unit 2178 is included. The call control 2172 also includes a video feed display 2180 for presenting still and/or video images from the patient interface 2050 and a self video display 2182 showing a current image of the assistant using the assistant interface. . Self-video display 2182 may be presented as a picture-in-picture format within a section of video feed display 2180, as shown in FIG. Alternatively or additionally, selfie video display 2182 may be presented separately and/or independently from video feed display 2180 .

The example overview display 2120 shown in FIG. 17 also includes a third party communication control 2190 for use in conducting voice and/or audiovisual communications with a third party. Third party communication control 2190 may take the form of a portion or region of overview display 2120, as shown in FIG. Third party communication control 2190 may take other forms, such as a separate on-screen display or a pop-up window. Third party communication control 2190 provides a contact list and/or contact list for contacting third parties, e.g. It may include one or more controls, such as buttons or controls. Third party communication controls 2190 may include teleconferencing capabilities for third parties to simultaneously communicate with both the assistant via assistant interface 2094 and the patient via patient interface 2050 . For example, system 2010 may provide for assistants to initiate three-way conversations with patients and third parties.

FIG. 18 illustrates an exemplary embodiment of a summary display 2120 of assistant interface 2094 that presents recommended optimal treatment plans and excluded treatment plans in real-time during a telemedicine session in accordance with this disclosure. As depicted, overview display 2120 only includes sections of patient profile 2130 and video feed display 2180 , including selfie video display 2182 . Any suitable configuration of the controls and interfaces of overview display 2120 described with reference to FIG. obtain.

An assistant (e.g., a healthcare worker) using assistant interface 2094 (e.g., a computing device) during a telemedicine session can view overview display 2120 (e.g., of assistant interface 2094) that also presents video from the patient on video feed display 2180. self-video 2182 in a portion of the user interface presented on display screen 2024). As depicted, another portion of overview display 2120 includes patient profile display 2130 .

Patient profile display 2130 presents two exemplary optimal treatment plans 2600 and one exemplary excluded treatment plan 2602 . As described herein, treatment regimens can be recommended taking into account various clinical information and characteristics of the patient being treated. Clinical information may include information about other people's characteristics, treatment plans followed by other people, and results of treatment plans. Patterns between the characteristics of the patient being treated and others are used by the artificial intelligence engine 2011 to generate a recommended optimal treatment plan 2600 that the patient should follow to achieve the desired outcome. It can be matched by one or more machine learning models 2013 . Each of the recommended optimal treatment plans can be generated based on different desired results.

For example, assume the following: Treatment plan "A" states, "Patient X should achieve an increased range of motion of Y% using the treatment device 30 minutes per day for 4 days, Patient X has type 2 diabetes and patient X could be prescribed drug Z for pain control during the treatment plan (drug Z is approved for people with type 2 diabetes). )” is displayed. Therefore, the optimal treatment plan generated achieves a Y% increase in range of motion. As can be appreciated, the optimal treatment regimen also takes into account the patient's known medical conditions (e.g., type 2 diabetes) and recommended medications (e.g., Drug Z )including. That is, the recommended patient medication not only does not conflict with the patient's medical condition, but thereby improves the likelihood of a good patient outcome.

The recommended optimal treatment plan "B" may specify different treatment plans, including different treatment protocols for treatment devices, different drug regimens, etc., based on different desired outcomes of the treatment plans.

As depicted, patient profile display 2130 may also present treatment plans 2602 that are excluded. These types of treatment plans are presented to the Assistant using the Assistant interface 2094 to warn the Assistant not to recommend certain parts of the treatment plan to the patient. For example, an excluded treatment plan may specify: "Patient X should not use the therapy device for more than 30 minutes per day for heart disease, and Patient X should has type 2 diabetes and patient X should not be prescribed drug M for pain control during the treatment plan (in this scenario, drug M would cause complications to people with type 2 can cause it)." Specifically, the treatment plan that is excluded points to a limitation of the treatment protocol that patient X should not exercise more than 30 minutes per day due to heart disease. The excluded treatment regimen also points out that patient X should not be prescribed drug M because drug M conflicts with the medical condition of type 2 diabetes.

The Assistant may select the optimal treatment plan for the patient on summary display 2120 . For example, an assistant may select from the patient's optimal treatment plan 2600 using an input peripheral (eg, mouse, touch screen, microphone, keyboard, etc.). In some embodiments, during the telemedicine session, the assistant may discuss the pros and cons of the recommended optimal treatment plan 2600 with the patient.

Either way, the assistant can select the optimal treatment plan for the patient to follow to achieve the desired result. The selected optimal treatment plan may be transmitted to patient interface 2050 for presentation. The patient can view the selected optimal treatment plan on patient interface 2050 . In some embodiments, assistants and patients can discuss details (eg, treatment protocols using therapy device 2070, dietary regimens, drug regimens, etc.) in real-time during a telemedicine session.

FIG. 19 illustrates an exemplary embodiment of a server 2030 that transforms clinical information 2700 into medical description language 2702 for processing by artificial intelligence engine 2011 according to this disclosure. Clinical information 2700 may be written by a person with certain professional certifications, licenses, or positions. In the depicted example, clinical information 2700 includes a portion of a meta-analysis for a clinical trial entitled "EFFECT OF USING TREATMENT PLAN FOR HIP OSTEOARTHRITIS PAIN." This part contains a "Results" section and a "Conclusion" section. For clarity of illustration, many other portions of clinical information 2700 (eg, details of trial procedures, subject biographies, etc.) may not be depicted.

One or more machine learning models 2013 can be trained to interpret bodies of structured or unstructured text (eg, clinical information 700) in searching a corpus of keywords representing target information. Target information may be included in one or more portions of clinical information 2700 . Targeted information may include characteristics of people (e.g., vital signs, medical conditions, medical procedures, allergies, family medical information, measurements, etc.), treatment plans people follow, results of treatment plans, clinical trial information, treatment plans, etc. It may refer to any suitable information of interest, such as the treatment device used.

Using tags representing target information and values associated with the tags, one or more machine learning models 2013 can generate a canonical form defined by a medical descriptive language. Values can be numbers, letters, alphanumeric characters, strings, arrays, etc., and are derived from portions of clinical information 2700 (including target information). Target information may be organized in a parent-child relationship based on information structure, organization, and/or relationships. For example, the keyword "result" is identified as a parent-level tag because the result contains the child's target information such as test, subject, treatment plan, treatment device, subject characteristics, and conclusion. and can be determined. Thus, the parent level tags of "<results>" are "<trials>", "<subjects>", "<treatment plan>", "<treatment apparatus>", "<subject characteristics>", and "<conclusions>". >" child level tags. Each tag may have a corresponding closing tag (eg, "<results>...</results>").

Now consider an embodiment of the operations that the trained machine learning model 2013 performs to encode a portion of the clinical information 2700 in the medical description language 2702 . A trained machine learning model 2013 identified the keywords “treatment plan” and “treatment device” in the portion of clinical information 2700 . Once identified, the trained machine learning model 2013 analyzes words in the neighborhood (e.g., left and right) of the keyword to determine whether the words match the recognized context based on training data. can. Trained machine learning model 2013 may also determine whether a term is recognized as being associated with a keyword based on training data and based on attributes of the data. In FIG. 19, the trained machine learning model recognizes that the term "range of motion (ROM)" fits in the context of the keyword "treatment device" and is also associated with the keyword "treatment device." It can be determined that it is highly possible. Therefore, the value "ROM" is placed between "<treatment apparatus>" and "</treatment apparatus>", which are tags representing target information. Other tags representing target information in the canonical form of medical description language 2702 may be populated in a similar manner. Medical description language 2702 representing portions of clinical information 2700 may be stored in patient data store 2044 in a database corresponding to the appropriate patient cohort.

FIG. 20 illustrates an exemplary embodiment of a method 2800 for recommending an optimal treatment plan according to this disclosure. Method 2800 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general purpose computer system or dedicated machine), or a combination of both. Method 2800 and/or each of its individual functions, routines, subroutines, or acts may be executed by any component of FIG. ) may be performed by In particular implementations, method 2800 may be performed by a single processing thread. Alternatively, method 2800 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or method operations.

For ease of explanation, method 2800 is depicted and described as a series of acts. However, operations in accordance with this disclosure may occur in various orders and/or concurrently and/or with other operations not presented and described herein. For example, the acts depicted in method 2800 may be performed in combination with any other acts of any other methods disclosed herein. Moreover, not all illustrated acts may be required to implement method 2800 in accordance with the disclosed subject matter. Additionally, those skilled in the art will understand and appreciate that the method 2800 can alternatively be represented as a series of interrelated states via a state diagram or events.

At 2802 , the processing device may receive clinical information 2700 from the data source 2015 regarding the results of using the therapy device 2070 to implement a particular treatment regimen for people with particular characteristics. The clinical information has a first data type, which may include natural language text in the form of words arranged in sentences further arranged in paragraphs. A first data type may be a report or description, which may include information about clinical trials, medical studies, meta-analyses, evidence-based guidelines, journals, and the like. A first data format may include information arranged in an unstructured fashion and may have a first data size (eg, bytes, kilobytes, etc.).

Specific characteristics of people include drugs prescribed to people, injuries of people, medical procedures performed on people, measurements of people, allergies of people, medical conditions of people, first historical information of people , people's vital signs, people's symptoms, people's family medical information, or some combination thereof. These characteristics may also include the following information about people: demographic, geographic, diagnostic, measurement or test-based, medical history, etiology, cohort association, differential diagnosis, surgical, physical Therapeutic, pharmacological, and other recommended treatments.

At 2804 , the processing device may convert the portion of clinical information from the first data format into a medical descriptive language 2702 for use by artificial intelligence engine 2011 . Medical description language 2702 may include a second data format that structures the unstructured data of clinical information 2700 . For example, the medical description language 2702 may include using tag-value pairs, where the tags identify the type of value stored between them. Medical description language 2702 may have a second data size (eg, bits) that is smaller than the first data size of clinical information 2700 . A medical description language may include telemedical data.

At 2806, the processing device follows when the patient uses the therapy device 2070 to achieve the desired result based on the portion of the clinical information 2700 described by the medical description language 2702 and the set of characteristics about the patient. The optimal treatment plan 2600 to be determined may be determined. One or more machine learning models 2013 of the artificial intelligence engine 2011 can be trained to output an optimal treatment plan 2600. For example, one machine learning model 2013 may be trained to match patterns between portions of clinical information described by the medical description language 2702 to a set of patient characteristics. In some embodiments, the set of patient characteristics is also represented in the medical descriptive language. Patterns are associated with optimal treatment plans that can produce desired results.

In some embodiments, the optimal treatment plan includes the medical procedures to be performed on the patient, the patient's treatment protocol using the treatment device 2070, the patient's dietary regimen, the patient's drug regimen, the patient's sleep regimen, additional It may include information about regimens, or some combination thereof.

A desired result may include obtaining a particular result within a particular time period. Specific results include the range of motion a patient achieves using therapy device 2070, the amount of force the patient exerts on a portion of therapy device 2070, the amount of time the patient exercises using therapy device 2070, and the amount of time the patient exercises using therapy device 2070. It may include the distance traveled using the device 2070, the level of pain experienced by the patient when using the treatment device 2070, or some combination thereof.

In some embodiments, the processing device uses the therapy device 2070 to achieve a second desired result based on the portion of the clinical information described by the medical description language and the set of characteristics about the patient. A second optimal treatment plan may be determined for the patient to follow in order to do so. A desired outcome may relate to recovery outcome and a second desired outcome may relate to recovery time. Recovery outcomes may include achieving specific thresholds for functionality, mobility, range of motion, etc. of specific body parts. Recovery time may involve achieving a specified threshold of functionality, mobility, locomotion, range of motion, etc. of a specified body part within a specified threshold period of time. For example, some people may prefer to recover to a certain level of mobility as soon as possible without recovering completely. As discussed above, different machine learning models 2013 may be trained using different clinical information to provide different recommended treatment plans that may produce different desired results.

In some embodiments, the processing device uses the therapeutic device 2070 to achieve a desired result based on the portion of the clinical information described by the medical description language and the set of characteristics about the patient. Excluded treatment regimens 2602 may be determined that the patient should not be recommended to follow. In some embodiments, as depicted in FIG. 18, optimal treatment plan 2600 and ruled out treatment plan 2602 may be presented simultaneously in a first portion of the user interface (eg, patient profile display 2130). Meanwhile, at least video or other multimedia data from a patient engaged in a telemedicine session may be presented in another portion (eg, video feed display 2180).

In some embodiments, the optimal treatment plan 2600 and the excluded treatment plan 2602 may be presented simultaneously while the medical practitioner is not engaged in a telemedicine session. For example, optimal treatment plan 2600 and excluded treatment plan 2602 may be presented in a user interface before a telemedicine session begins or after a telemedicine session ends.

At 2808, the processing device may provide an optimal treatment plan that is presented in a user interface (eg, summary display 2120) on the healthcare professional's computing device (eg, assistant interface 2094). In addition, any other generated optimal treatment plan 2600 may be provided to the healthcare worker's computing device. For example, different optimal treatment plans that yield different outcomes may be presented to a healthcare professional. The processing device may receive the selected treatment plan of any of the presented treatment plans. In some embodiments, a healthcare professional may select the optimal treatment plan based on patient outcome preferences. For example, an athlete may want to optimize for performance, while a retiree may want to optimize for pain-free quality of life. The selected treatment plan may be transmitted to the patient's computing device for presentation on the user interface. In some embodiments, the optimal treatment plan causes the optimal treatment plan to be presented in the first portion of the user interface in real time while the video and optionally other multimedia of the patient are presented to the user. It may be provided to the healthcare worker's computing device during the telemedicine session so that it is simultaneously presented on the second portion of the interface. The selected treatment plan may be presented on the patient's computing device during the telemedicine session so that the medical practitioner can explain the selected treatment plan to the patient.

FIG. 21 illustrates an exemplary embodiment of a method 2900 for transforming clinical information into medical descriptive language according to this disclosure. Method 2900 includes acts performed by a processor of a computing device (eg, any component of FIG. 13, such as server 2030 running artificial intelligence engine 2011). In some embodiments, one or more operations of method 2900 are implemented in computer instructions stored on a memory device and executed by a processing device. Method 2900 may be performed in the same or similar manner as described above with respect to method 2800. The acts of method 2900 may be performed in any combination with the acts of any of the methods described herein.

Method 2900 may include acts 2804 from method 2800 described above depicted in FIG. For example, at 2804 of method 2600, a processing device may convert a portion of clinical information from a first data format into a medical descriptive language used by an artificial intelligence engine.

Method 2900 of FIG. 21 includes acts 2902, 2904, and 2906. FIG. Operations 2902 , 2904 and 2906 may be performed by one or more trained machine learning models 2013 of artificial intelligence engine 2011 .

At 2902, a processing device may interpret clinical information. At 2904, the processing device may identify portions of clinical information having values associated with the target information based on keywords representing the target information in the clinical information. At 2906, the processing device may generate a canonical form defined by the medical description language. The canonical form may include tags that identify the values of the target information. A tag can be an attribute that describes a particular characteristic of the target information. Specific characteristics may include which cohort class a person is placed in, age of the person, semantic information, relevance to a particular cohort, family history, and the like. In some embodiments, the specified characteristic may include any information or indication that a person is in danger.

Canonical forms are more efficient for the portion of clinical information represented by a medical descriptive language when training machine learning models to generate optimal treatment plans for patients using trained machine learning models. processing. Further, the canonical form may allow for more efficient processing by trained machine learning models when matching patterns among patient characteristics to portions of clinical information represented by medical descriptive language.

FIG. 22 illustrates an exemplary computer system 21000 capable of implementing any one or more of the methods described herein, in accordance with one or more aspects of the disclosure. In one example, computer system 21000 includes computing devices, support interface 2094, reporting interface 2092, supervisor interface 2090, clinician interface 2020, server 2030 (including AI engine 2011), patient interface 2050, gait sensor 2082. , goniometer 2084, therapy device 2070, pressure sensor 2086, or any suitable component of FIG. Computer system 21000 may be capable of executing instructions implementing one or more machine learning models 2013 of artificial intelligence engine 2011 of FIG. The computer system may be connected (eg, networked) to other computer systems within a LAN, intranet, extranet, or Internet, including via a cloud or peer-to-peer network. A computer system can operate in the capacity of a server in a client-server network environment. Computer systems include personal computers (PCs), tablet computers, wearables (e.g., wristbands), set-top boxes (STBs), personal digital assistants (PDAs), mobile phones, cameras, camcorders, Internet of Things (IoT) devices. , or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, although only a single computer system is illustrated, the term "computer" can also refer to a set of instructions (or sets of instructions) for performing any one or more of the methods discussed herein. ), individually or jointly, to include any collection of computers.

Computer system 21000 includes a processing device 21002, a main memory 21004 (e.g., dynamic random memory such as read only memory (ROM), flash memory, solid state drive (SSD), synchronous DRAM (SDRAM), etc.) that communicate with each other via bus 1010. access memory (DRAM)), static memory 21006 (eg, flash memory, solid state drive (SSD), static random access memory (SRAM)), and data storage devices 21008 .

Processing device 21002 represents one or more general purpose processing devices such as microprocessors, central processing units, and the like. More specifically, the processing device 21002 implements a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or other instruction set. , or a processor that implements a combination of instruction sets. Processing device 21002 may also be one or more dedicated processing devices such as an application specific integrated circuit (ASIC), system-on-chip, field programmable gate array (FPGA), digital signal processor (DSP), network processor, etc. may Processing device 21002 is configured to execute instructions to perform any of the operations and steps discussed herein.

Computer system 21000 may further include network interface device 21012 . Computer system 21000 also includes a video display 21014 (e.g., liquid crystal display (LCD), light emitting diode (LED), organic light emitting diode (OLED), quantum LED, cathode ray tube (CRT), shadow mask CRT, aperture grill CRT, monochrome). CRT), one or more input devices 21016 (eg, keyboard and/or mouse, or game-like controls), and one or more speakers 21018 (eg, speakers). In one illustrative example, video display 21014 and input device 21016 may be combined into a single component or device (eg, an LCD touchscreen).

Data storage device 21016 may include computer readable media 21020 having instructions 21022 stored thereon that embody any one or more of the methods, acts, or functions described herein. Instructions 21022 may also reside completely or at least partially within main memory 21004 and/or processing device 21002 during execution of instructions 21022 by computer system 21000 . Thus, main memory 21004 and processing device 21002 also constitute computer-readable media. Instructions 21022 may also be transmitted or received over a network via network interface device 21012 .

Although computer-readable storage medium 21020 is shown in the illustrative embodiment to be a single medium, the term "computer-readable storage medium" refers to a single medium that stores one or more sets of instructions. or multiple media (eg, centralized or distributed databases and/or associated caches and servers). The term "computer-readable storage medium" also means any medium capable of storing, encoding or carrying a set of instructions for execution by a machine and causing the machine to perform any one or more of the methodologies of the present disclosure. shall be construed to include the medium of Accordingly, the term "computer-readable storage medium" shall be taken to include, but not be limited to, solid state memory, optical media, and magnetic media.

Article 25. A method for providing, by an artificial intelligence engine, an optimal treatment plan for use with a treatment device, comprising:
Receiving from a data source clinical information about the results of implementing a particular treatment regimen for people with particular characteristics using a therapy device, the clinical information having a first data format , receiving and
converting a portion of the clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
Determining, based on a portion of the clinical information described by the medical description language and a plurality of characteristics about the patient, an optimal treatment plan to be followed by the patient using the treatment device to achieve a desired outcome. ,
providing an optimal treatment plan for presentation on a healthcare professional's computing device.

Article 26. converting the portion of clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
interpreting clinical information;
identifying portions of the clinical information having values associated with the target information based on keywords representing the target information in the clinical information;
Generating a canonical form defined by a medical description language, the canonical form including a tag identifying a value of the target information. described method.

Article 27. A method as in any clause herein, wherein a tag is an attribute that describes a particular characteristic of the target information.

Article 28. Providing the optimal treatment plan as presented on the healthcare worker's computing device

Further comprising causing the optimal treatment plan to be presented on a user interface of the healthcare professional's computing device during the telemedicine session, wherein the optimal treatment plan is presented on a display screen of the computing device. A method according to any clause herein, wherein such display screen is configured for use by a patient during a telemedicine session.

Article 29. Excluded treatments that the patient should not be recommended to follow when using the treatment device to achieve desired results based on the portion of the clinical information described by the medical descriptive language and multiple characteristics about the patient deciding on a plan;
The method according to any clause herein, further comprising providing the excluded treatment plan as presented on a healthcare professional's computing device.

Clause 30. A second optimal treatment to follow when the patient uses the treatment device to achieve a second desired result based on the portion of the clinical information described by the medical description language and a plurality of characteristics about the patient. determining a plan, wherein a desired outcome is associated with recovery outcome and a second desired outcome is associated with recovery time;
providing a second optimal treatment plan for presentation on a healthcare professional's computing device;
receiving a selected treatment plan, either the optimal treatment plan or a second optimal treatment plan;
transmitting the selected treatment plan to the patient's computing device for presentation on a user interface of the patient's computing device.

Article 31. A desired result includes obtaining a particular result within a particular time period, the particular result being
the range of motion the patient achieves using the therapy device;
the amount of force the patient exerts on a portion of the therapy device;
the amount of time the patient exercises using the therapy device;
A method according to any clause herein comprising the distance the patient travels using the treatment device, or any combination thereof.

Article 32. The specific characteristics of the people are the first drug prescribed to the people, the first injury of the people, the first medical procedure performed on the people, the first measurement of the people, the first allergies, people's first medical condition, people's first historical information, people's first vital signs, people's first symptoms, people's first family medical information, people's first population demographic information, first geographic information of people, first measurement-based or test-based information of people, first medical history information of people, first etiological information of people, first cohort of people Related information, people's first differential diagnostic information, people's first surgical information, people's first physiotherapy information, people's first pharmacological information, people's first recommended others treatment of, or any combination thereof,
The plurality of characteristics of the patient includes: a second drug of the patient; a second injury of the patient; a second medical procedure performed on the patient; a second measurement of the patient; a second allergy of the patient; a second medical condition of the patient; a second historical information of the patient; a second vital sign of the patient; a second symptom of the patient; a second family medical information of the patient; second geographic information of the patient; second measurement-based or test-based information of the patient; second historical medical information of the patient; second etiological information of the patient; second cohort-related information of the patient; patient second differential diagnostic information; patient second surgical information; patient second physiotherapy information; patient second pharmacological information; patient second other treatment recommended; or any combination thereof.

Article 33. Clinical information is written by a person with specific professional qualifications and includes journal articles, clinical trials, evidence-based guidelines, meta-analyses, or any combination thereof, as described in any of the methods herein. .

Article 34. Based on a portion of the clinical information described by the medical description language and a plurality of characteristics about the patient, determining an optimal treatment plan to be followed by the patient using the treatment device to achieve a desired outcome. ,
Matching patterns between portions of clinical information described by a medical description language to a plurality of characteristics of a patient, where the patterns are associated with optimal treatment plans leading to desired outcomes. A method according to any clause herein, further comprising.

Article 35. The optimal treatment plan is
a medical procedure performed on a patient;
a treatment protocol for a patient using a treatment device;
the patient's dietary regimen,
the patient's drug regimen,
A method according to any section herein, comprising the patient's sleep regimen, or any combination thereof.

Article 36. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
Receiving from a data source clinical information about the results of implementing a particular treatment regimen for people with particular characteristics using a therapy device, the clinical information having a first data format , receiving and
converting a portion of the clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
Determining, based on a portion of the clinical information described by the medical description language and a plurality of characteristics about the patient, an optimal treatment plan to be followed by the patient using the treatment device to achieve a desired outcome. ,
A computer readable medium for providing and causing an optimal treatment plan to be presented on a healthcare professional's computing device.

Article 37. converting the portion of clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
interpreting clinical information;
identifying portions of the clinical information having target information values based on keywords representing the target information in the clinical information;
Generating a canonical form defined by a medical description language, the canonical form including a tag identifying a value of the target information. A computer readable medium as described.

Article 38. Providing the optimal treatment plan as presented on the healthcare worker's computing device
during the telemedicine session, causing the optimal treatment plan to be presented on a user interface of the healthcare professional's computing device; A computer-readable medium as described in any section herein that is not presented on a user interface of the device.

Article 39. The processing device
A second optimal treatment to follow when the patient uses the treatment device to achieve a second desired result based on the portion of the clinical information described by the medical description language and a plurality of characteristics about the patient. determining a plan, wherein a desired outcome is associated with recovery outcome and a second desired outcome is associated with recovery time;
providing a second optimal treatment plan for presentation on a healthcare professional's computing device;
receiving a selected treatment plan, either the optimal treatment plan or a second optimal treatment plan;
and transmitting the selected treatment plan to the patient's computing device.

Clause 40. A desired result includes obtaining a particular result within a particular time period, the particular result being
the range of motion the patient achieves using the therapy device;
the amount of force the patient exerts on a portion of the therapy device;
the amount of time the patient exercises using the therapy device;
A computer readable medium according to any clause herein, comprising: a distance traveled by a patient using a therapy device, or any combination thereof.

Article 41. The specific characteristics of the people are the first drug prescribed to the people, the first injury of the people, the first medical procedure performed on the people, the first measurement of the people, the first allergies, people's first medical condition, people's first historical information, people's first vital signs, people's first symptoms, people's first family medical information, people's first population demographic information, first geographic information of people, first measurement-based or test-based information of people, first medical history information of people, first etiological information of people, first cohort of people Related information, people's first differential diagnostic information, people's first surgical information, people's first physiotherapy information, people's first pharmacological information, people's first recommended others treatment of, or any combination thereof,
The plurality of characteristics of the patient includes: a second drug of the patient; a second injury of the patient; a second medical procedure performed on the patient; a second measurement of the patient; a second allergy of the patient; a second medical condition of the patient; a second historical information of the patient; a second vital sign of the patient; a second symptom of the patient; a second family medical information of the patient; second geographic information of the patient; second measurement-based or test-based information of the patient; second historical medical information of the patient; second etiological information of the patient; second cohort-related information of the patient; patient second differential diagnostic information; patient second surgical information; patient second physiotherapy information; patient second pharmacological information; patient second other treatment recommended; or any combination thereof.

Article 42. The clinical information is written by a person with a particular professional qualification and includes journal articles, clinical trials, evidence-based guidelines, or any combination thereof, computer readable media as described in any section of this specification.

Article 43. a system,
a memory device storing instructions;
a processing device communicatively coupled to the memory device, the processing device executing the instructions to
Receiving from a data source clinical information about the results of implementing a particular treatment regimen for people with particular characteristics using a therapy device, the clinical information having a first data format , receiving and
converting a portion of the clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
Determining an optimal treatment plan to follow when a patient uses a treatment device to achieve a desired outcome based on a portion of the clinical information described by the medical description language and a plurality of characteristics about the patient. ,
A system that: and provides an optimal treatment plan for presentation on a healthcare professional's computing device.

Article 44. converting the portion of clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
interpreting clinical information;
identifying portions of the clinical information having the value of the target information based on keywords representing the target information described by the clinical information;
Generating a canonical form defined by a medical description language, the canonical form including a tag identifying a value of the target information. System as described.

Methods and systems for monitoring user characteristics during a telemedicine session using artificial intelligence Specific characteristics (e.g., vital signs or other measurements, practice, demographic, geographic, diagnostic, measurement-based or trial-based, medical history, etiology, cohort association, differential diagnosis, surgical, physiotherapy, behavioral, pharmacological, and other recommended treatments) Doing so can be a technically difficult problem. For example, a large amount of information can be considered when determining a treatment plan, which can lead to inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitation setting, some of the many pieces of information considered may include patient characteristics, such as personal information, performance information, and measurement information. Personal information may include, for example, age, weight, gender, height, body mass index, medical conditions, family history of medications, injuries, medical procedures, prescribed medications, behavioral or psychological conditions, or any combination thereof; It may include demographic, psychological, or other information. The performance information may include, for example, elapsed time using the treatment device, amount of force exerted on the portion of the treatment device, range of motion achieved with the treatment device, speed of movement of the portion of the treatment device, multiple pains using the treatment device It may include indications of levels, or some combination thereof. Measurement information may include, for example, vital signs, respiration rate, heart rate, temperature, blood pressure, glucose levels or other biomarkers, or some combination thereof. It may be desirable to process the characteristics of a large number of patients, the treatment plans to be administered to those patients, and the results of the treatment plans for those patients.

Furthermore, another technical problem may involve remotely treating a patient via a computing device during a telemedicine or telehealth session from a location different from where the patient is located. An additional technical problem is to control or allow control of the therapeutic device used by the patient at the location where the patient is located from different locations. In many cases, when a patient undergoes rehabilitation surgery (e.g., knee surgery), health care providers use to carry out treatment protocols at the patient's home or any mobile or temporary residence. A therapeutic device may be prescribed to the patient. Health care providers can refer to doctors, physician assistants, nurses, chiropractors, dentists, physical therapists, acupuncturists, physical trainers, coaches, personal trainers, and the like. A health care provider may refer to any person having a license, license, position, etc., in fields such as medicine, physical therapy, rehabilitation, and the like.

Monitoring the actual progress of the patient using the treatment device by the provider when the provider is located at a different location than the patient and the treatment device ), it can be technically difficult to modify the treatment plan according to the patient's progress, to adapt the treatment device to the patient's personal characteristics as the patient implements the treatment plan, and the like.

Accordingly, systems and methods, such as those described herein, configured to monitor a patient's actual progress while the patient uses a treatment device to implement a treatment plan may be desirable. be. In some embodiments, the systems and methods described herein can be configured to receive treatment data regarding a user implementing a treatment plan using a treatment device. A user may include a patient, user, or person performing various exercises using a therapeutic device.

Treatment data may include various characteristics of the user, various baseline measurement information about the user, various measurement information about the user while the user is using the treatment device, various characteristics of the treatment device, treatment plans, and other suitable data. , or combinations thereof. In some embodiments, the systems and methods described herein can be configured to receive therapy data during a telemedicine session.

In some embodiments, at least a portion of the treatment data is configured to sense various characteristics of the treatment device and/or measurement information of the user while the user uses the treatment device to implement the treatment plan. may correspond to the sensor data of the sensor. Additionally or alternatively, at least a portion of the treatment data may be obtained from sensors associated with the wearable device configured to sense user measurement information while the user uses the treatment device to implement the treatment plan. of sensor data.

The various properties of the treatment device may include one or more settings of the treatment device, the current number of rotations per time period (e.g., one minute, etc.) of a rotating member (e.g., wheel, etc.) of the treatment device, the resistance setting of the treatment device. , other suitable characteristics of the treatment device, or combinations thereof. The baseline information includes one or more of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, the user's blood pressure, the glucose level or other biomarkers, the user's other suitable measurement information, or combinations thereof. The measurement information may include one or more of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, the user's blood pressure, the user's glucose, while the user uses the therapy device to implement the treatment plan. It may include level or other suitable measurement information of the user, or a combination thereof.

In some embodiments, the systems and methods described herein can be configured to write therapy data to associated memory for access by an artificial intelligence engine. The artificial intelligence engine may be configured to use one or more machine learning models configured to generate one or more predictions using at least a portion of the therapy data. For example, the artificial intelligence engine may use machine learning models trained using different treatment data corresponding to different users. A machine learning model may be configured to receive therapy data corresponding to a user. A machine learning model may analyze at least one aspect of the treatment data and generate at least one prediction corresponding to the at least one aspect of the treatment data. At least one prediction may indicate one or more predicted characteristics of the user. The one or more predicted characteristics of the user are the user's predicted vital signs, the user's predicted respiratory rate, the user's predicted heart rate, the user's predicted body temperature, the user's predicted blood pressure, The predicted performance parameters of a user administering a treatment plan, the predicted outcome of a treatment plan being administered by the user, the user's predicted injury resulting from the user administering the treatment plan, or the user's other may include the preferred predicted properties of

In some embodiments, the systems and methods described herein can be configured to receive one or more predictions from an artificial intelligence engine. The systems and methods described herein may be configured to identify a threshold corresponding to each prediction received from the artificial intelligence engine. For example, the systems and methods described herein may identify one or more characteristics of the user indicated by each prediction.

The systems and methods described herein may be configured to access a database configured to associate thresholds with user characteristics and/or combinations of user characteristics. For example, the database may contain information relating the first threshold to the user's blood pressure. Additionally or alternatively, the database may include information relating thresholds to the user's blood pressure and the user's heart rate. It should be appreciated that the database may include any number of thresholds associated with any of a variety of user characteristics and/or any combination of user characteristics. In some embodiments, the threshold corresponding to each prediction may include a value or range of values, including upper and lower bounds.

In some embodiments, the systems and methods described herein may be configured to determine whether a prediction received from an artificial intelligence engine falls within corresponding thresholds. For example, the systems and methods described herein can be configured to compare predictions to corresponding thresholds. The systems and methods described herein may be configured to determine if the prediction is within a predetermined range of thresholds. For example, if the threshold includes a value, the predetermined range may include an upper limit above this value (e.g., expressed as a percentage of 0.5% or 1%, or e.g., 250 or 750 (unit of measure or other suitable numerical value); or other suitable upper limit) and a lower limit below this value (e.g. a percentage expression of 0.5% or 1%, or e.g. 250 or 750 (unit of measure or other suitable numerical value), or other suitable lower bound), and Similarly, if the threshold includes a range that includes a first upper bound and a first lower bound (eg, defining an acceptable range of one or more user characteristics corresponding to the prediction), then the predetermined range is the first a second upper limit (e.g., a percentage expression of 0.5% or 1%, or e.g., 250 or 750 (units of measure or other suitable numerical value), or other suitable numerical value) above the upper limit; a second lower limit below the lower limit (e.g., expressed as a percentage of 0.5% or 1%, or e.g., 250 or 750 (units of measure or other suitable numerical value), or other suitable lower limit); . It should be appreciated that the threshold may include any suitable predetermined range and may include any suitable form in addition to or in addition to those described herein.

If the systems and methods described herein determine that the prediction is within the threshold range, the systems and methods described herein perform an interface (e.g., , or over) to provide prognostic and therapeutic data. In some embodiments, the systems and methods described herein may be configured to generate treatment information using treatment data. Treatment information may include a summary of the user's implementation of the treatment plan while using the treatment device. The summary may be formatted such that the treatment data is presentable on the healthcare provider's computing device. The systems and methods described herein may be configured to communicate treatment information along with prognostic and/or treatment data to a healthcare provider's computing device. Alternatively, if the systems and methods described herein determine that the prediction is outside the threshold range, the systems and methods described herein may convert therapy data for the user to It may be configured to update as follows.

In some embodiments, the systems and methods described herein responsive to determining that the prediction is within a threshold range, based on the prediction, at least one aspect of the treatment plan and/or treatment One or more characteristics of the device may be modified.

In some embodiments, the systems and methods described herein are configured to control a therapeutic device while a user uses the therapeutic device during a telemedicine session based on the generated prediction. can be For example, the systems and methods described herein may control one or more properties of a therapeutic device based on predictions and/or treatment plans.

A health care provider may be a health care professional (such as a doctor, nurse, therapist, etc.), an exercise professional (such as a coach, trainer, dietitian, etc.), or at least one of a medical and athletic attribute. It may include another professional (eg, exercise physiologist, physical therapist, occupational therapist, etc.) sharing one. As used herein, but not limited to the foregoing, a “health care provider” may be a human, robot, virtual assistant, virtual assistant in virtual and/or augmented reality, or a software program, integrated software and an artificial intelligence entity comprising hardware, or hardware alone.

In some embodiments, the interface may include a graphical user interface configured to provide treatment information and receive input from a healthcare provider. The interface may be activated and/or driven by a text input field, a drop-down selection input field, a radio button input field, a virtual switch input field, a virtual lever input field, by voice, by touch, by touch, by biometrics, or otherwise. may include one or more input fields, such as a default input field, other suitable input fields, or combinations thereof.

In some embodiments, a healthcare provider may review treatment information and/or predictions. A healthcare provider may determine whether to modify at least one aspect of the treatment plan and/or one or more characteristics of the treatment device based on the treatment information and/or prognostic review. For example, a healthcare provider may review treatment information. Based on the review of the treatment information, the health care provider may compare the treatment information with the treatment plan being followed by the user.

The healthcare provider may provide (i) expected information about the user while the user uses the treatment device to perform the treatment plan; users.

The expected information may include one or more of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, the user's blood pressure, other suitable information of the user, or combinations thereof. A health care provider may determine that a treatment regimen is providing the desired effect if the prediction is within an acceptable range associated with one or more corresponding portions or portions of expected information. Alternatively, the health care provider determines that the treatment regimen is not providing the desired effect if the prediction falls outside the tolerance associated with one or more corresponding parts or portions of expected information. can.

For example, the health care provider should ensure that the blood pressure value indicated by the prediction (e.g., systolic, diastolic, and/or pulse pressure) is within an acceptable range of expected blood pressure values indicated by the prognostic information. (e.g., plus or minus 1%, plus or minus 5%, plus or minus 1 unit of measurement (or other suitable number), or any suitable percentage-based range or numerical range). can judge. The health care provider may determine that the treatment regimen is having the desired effect if the blood pressure values are within the range of expected blood pressure values. Alternatively, if the blood pressure value is outside the range of expected blood pressure values, it may be determined that the treatment regimen is not having the desired effect.

In some embodiments, while the user is using the treatment device to implement the treatment plan, the healthcare provider compares the expected characteristics of the treatment device to the characteristics of the treatment device indicated by the treatment information. obtain. For example, the healthcare provider may compare the expected resistance setting of the therapy device to the actual resistance setting of the therapy device indicated by the therapy information. The healthcare provider determines that the user is performing the treatment plan properly if the actual characteristics of the treatment device indicated by the treatment information are within the corresponding ones of the expected characteristics of the treatment device. can be determined. Alternatively, if the actual characteristics of the treatment device indicated by the treatment information are outside the range of corresponding ones of the expected characteristics of the treatment device, the healthcare provider may advise the user to properly adapt the treatment plan. It can be determined that it is not implemented.

If the health care provider determines that the prognostic and/or treatment information indicates that the user is performing the treatment plan properly and/or that the treatment plan is providing the desired effect, the health care provider , may decide not to modify one or more characteristics of at least one treatment plan and/or treatment device. Alternatively, while the user is performing a treatment plan using the treatment device, the health care provider may determine if the prognostic and/or treatment information indicates that the user is not properly performing or is not performing the treatment plan. and/or indicates that the treatment regimen did not or did not provide the desired effect, the health care provider may provide at least one aspect of the treatment regimen and/or the treatment device. It may be decided to modify one or more properties.

In some embodiments, if the healthcare provider decides to modify at least one aspect of the treatment regimen and/or modify one or more characteristics of the treatment device, the healthcare provider will interface with May interact to provide treatment plan input indicating one or more modifications to the treatment plan and/or modify one or more characteristics of the treatment device. For example, a healthcare provider may use the interface to provide input indicating an increase or decrease in the resistance setting of the therapy device, or other suitable modification to one or more characteristics of the therapy device. Additionally or alternatively, a healthcare provider may use the interface to provide input indicating modifications to the treatment plan. For example, a health care provider may use the interface to provide input indicating an increase or decrease in the amount of time the user is required to use the treatment device according to the treatment plan, or other suitable modifications to the treatment plan. may be provided.

In some embodiments, based on one or more modifications indicated by the treatment plan input, the systems and methods described herein modify at least one aspect of the treatment plan and/or one of the treatment devices. It can be configured to modify the above characteristics.

In some embodiments, the systems and methods described herein are configured to receive subsequent treatment data about the user while the user uses the treatment device to implement the modified treatment plan. can be For example, after a healthcare provider provides input to modify one or more characteristics of a treatment plan and/or treatment device, and/or an artificial intelligence engine modifies one or more characteristics of a treatment plan and/or treatment device. After modification, the user may continue to use the treatment device to implement the modified treatment plan. Subsequent treatment data may correspond to treatment data generated while the user uses the treatment device to implement the modified treatment plan. In some embodiments, subsequent treatment data indicates that the healthcare provider received the treatment information and determined not to modify one or more characteristics of the treatment plan and/or treatment device and/or an artificial intelligence engine. may correspond to treatment data generated while the user continues to implement the treatment plan using the treatment device after determining that the treatment does not modify one or more characteristics of the treatment plan and/or treatment device.

In some embodiments, the artificial intelligence engine may use one or more machine learning models to generate one or more subsequent predictions based on subsequent treatment data. The systems and methods described herein may determine whether each subsequent prediction falls within a corresponding threshold. The systems and methods described herein transmit subsequent treatment data, subsequent treatment information, and/or predictions to health care providers in response to determining that each subsequent prediction is within a threshold range. of computing devices. In some embodiments, based on subsequent predictions, the systems and methods described herein can modify at least one aspect of the treatment regimen and/or one or more characteristics of the treatment device.

In some embodiments, the systems and methods described herein may be configured to receive subsequent treatment plan input from a healthcare provider's computing device. Based on subsequent treatment plan input received from the healthcare provider's computing device, the systems and methods described herein can further modify the treatment plan and/or modify one or more of the treatment devices. can be configured to control the characteristics of Subsequent treatment plan input corresponds to input provided by a healthcare provider at the interface in response to receiving and/or reviewing subsequent treatment information and/or subsequent predictions corresponding to subsequent treatment data can. It should be appreciated that the systems and methods described herein can be configured to continuously and/or periodically generate predictions based on therapy data. The systems and methods described herein can be used by a healthcare provider's computing power based on therapy data continuously and/or periodically received from the sensors described herein or other suitable sources. It may be configured to provide therapeutic information to the device on an ongoing and/or periodic basis. Additionally or alternatively, the systems and methods described herein may continuously and/or periodically monitor characteristics of the user while the user uses the treatment device to implement the treatment plan. can be configured to

In some embodiments, the healthcare provider and/or the systems and methods described herein continuously or periodically receive treatment information while the user uses the treatment device to implement the treatment plan. , treatment data, and/or predictions may be received and/or reviewed. whether the healthcare provider and/or the systems and methods described herein modify the treatment plan based on one or more trends indicated by the treatment information, treatment data, and/or prediction; or determine whether to modify and/or control one or more properties of the therapeutic device. For example, the one or more trends may be an increase in heart rate or other favorable indication that the user is not performing the treatment plan properly and/or that the user's implementation of the treatment plan is not having the desired effect. can show a trend.

In some embodiments, the systems and methods described herein use artificial intelligence and/or machine learning to assign patients to cohorts and, based on the assignments during adaptive telemedicine sessions, to treat treatment devices. can be configured to dynamically control the In some embodiments, one or more therapeutic devices may be provided to the patient. One or more treatment devices may be used by the patient for treatment at any suitable location, including the patient's home, gym, rehabilitation center, hospital, place of work, hotel, conference center, or permanent or temporary residence. Can be used to implement plans.

In some embodiments, the therapy device may be communicatively coupled to the server. Patient characteristics, including treatment data, may be collected before, during, and/or after the patient implements a treatment plan. For example, personal information, performance information, and measurement information may be collected before, during, and/or after a person performs a treatment plan. The results of performing each exercise (eg, improved performance or reduced performance) can be collected from the treatment device throughout the treatment plan and after the treatment plan has been performed. Treatment device parameters, settings, configurations, etc. (eg, pedal position, amount of resistance, etc.) may be collected before, during, and/or after the treatment plan is implemented.

Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be time-stamped and correlated with a particular step of the treatment plan. Such techniques are used to determine which steps in the treatment plan are likely to lead to the desired outcome (e.g., improvement in strength, range of motion, etc.) and which steps are likely to lead to reduced returns ( For example, it may allow determining whether continuing to exercise after 3 minutes actually delays or harms recovery.

The data may be transferred to the treatment device and/or any suitable computing device (e.g., the computing device described herein) over time as the patient uses the treatment device to perform various treatment regimens. (computing device into which personal information is entered, such as an interface, clinician interface, patient interface, etc.). Data that may be collected may include patient characteristics, treatment regimens administered by the patient, results of treatment regimens, any of the data described herein, any other suitable data, or combinations thereof. .

In some embodiments, data may be processed to group specific people into cohorts. People may be grouped by people who have certain or selected similar characteristics, treatment regimens, and results of implementing treatment regimens. For example, athletes without a medical condition who implement a treatment regimen (e.g., use a treatment device for 30 minutes a day, five times a week for three weeks) and who fully recover may receive first can be grouped into cohorts of Older adults classified as obese and following a treatment regimen (e.g., using a treatment regimen of 10 minutes a day three times a week for four weeks) and improving their range of motion by 75 percent were second Can be grouped into cohorts.

In some embodiments, an artificial intelligence engine may include one or more machine learning models trained using cohorts. For example, one or more machine learning models can be trained to receive input of new patient characteristics and to output patient treatment plans that produce desired results. A machine learning model may match patterns between the new patient's characteristics and at least one patient of the patients included in a particular cohort. When patterns are matched, a machine learning model can assign new patients to specific cohorts and select treatment plans associated with at least one patient. The artificial intelligence engine may be configured to remotely control the treatment device based on the treatment plan while the new patient uses the treatment device to implement the treatment plan.

As can be appreciated, the characteristics of a new patient (eg, a new user) may change as the new patient uses the treatment device to implement a treatment plan. For example, patient performance may improve faster than expected for those within the cohort to which new patients are currently assigned. Thus, a machine learning model can be trained to dynamically reassign new patients to different cohorts containing people with similar characteristics to the currently modified characteristics as new patients, based on the modified characteristics. . For example, a clinically obese patient may lose weight and no longer meet the weight criteria of the initial cohort, resulting in the patient's weight being reassigned to different cohorts with different weight criteria.

A different treatment plan may be selected for the new patient, and the treatment device may be remotely (e.g., referred to as remotely) to perform the treatment plan while the new patient uses the treatment device to perform the treatment plan. can be controlled based on Such technology may provide a technical solution to remotely control treatment devices.

Furthermore, the systems and methods described herein provide faster treatment for the patient because the treatment plan that most accurately matches the patient's characteristics is selected and implemented in real-time at any given time. It may lead to recovery time and/or better results. "Real time" can also refer to near real time, which can be less than 10 seconds. As described herein, the term "outcome" can refer to a medical result or medical outcome. Outcomes and outcomes can refer to responses to medical intervention.

Depending on what results are desired, the artificial intelligence engine can be trained to output several treatment plans. For example, one outcome may include recovery to a threshold level (e.g., 75% range of motion) in the fastest amount of time, while another outcome may include full recovery irrespective of the amount of time (e.g. , 100% range of motion). Data obtained from a patient and sorted into cohorts is such that a first treatment plan provides first results for people with characteristics similar to those of the patient and a second treatment plan provides results for people with characteristics similar to those of the patient. It may be indicated to provide secondary results for people with similar characteristics.

In addition, the artificial intelligence engine outputs treatment plans that are suboptimal, i.e., suboptimal, non-standard, or otherwise excluded (all without limitation, referred to as "excluded treatment plans") for the patient. can be trained to For example, if a patient has hypertension, certain exercises may put the patient at unnecessary risk or even induce a hypertensive crisis, and thus the exercise may be excluded from the patient. If a treatment plan can be flagged, it may not be approved or preferred by the patient. In some embodiments, the artificial intelligence engine may, for example, monitor therapy data received while a patient (e.g., a user) with hypertension uses a therapy device to implement an appropriate therapy plan, and perform therapy. Excluded treatment regimens that may provide beneficial results to the patient if the data indicate that the patient is, for example, undergoing the appropriate treatment regimen without exacerbating the patient's hypertensive condition. can be modified to include the features of

In some embodiments, treatment plans and/or excluded treatment plans may be presented to a healthcare provider during a telemedicine or telehealth session. A healthcare provider may select a particular treatment plan, have the patient transmit the treatment plan to the patient, and/or control the treatment device based on the treatment plan. In some embodiments, the artificial intelligence engine is remote from the patient and treatment device to facilitate telehealth or telemedicine applications, including remote diagnosis, treatment plan determination, and rehabilitation and/or pharmacological prescription. may receive and/or operate on.

In such cases, the recommended treatment plan and/or the excluded treatment plan are displayed in real-time or near-real-time on the patient's video during a telemedicine or telehealth session on the user interface of the healthcare provider's computing device. can be presented at the same time. Videos may also be accompanied by audio, text, and other multimedia information. Real time may refer to 2 seconds or less. Near real-time can refer to any interaction of a duration sufficiently short to allow two individuals to engage in interaction via such a user interface, generally less than 10 seconds, but will exceed 2 seconds.

Presenting the treatment plan generated by the artificial intelligence engine at the same time as presenting the patient video allows the healthcare provider to visually and/or otherwise communicate with the patient while also reviewing the treatment plan on the same user interface. can provide an enhanced user interface. An enhanced user interface may improve the experience of healthcare providers using the computing device and may encourage healthcare providers to reuse user interfaces. Such techniques also benefit from computing resources (e.g., processing , memory, network). The artificial intelligence engine can be configured to dynamically provide treatment plans and excluded treatment plans on the fly.

In some embodiments, the treatment device may be adaptive and/or individualized, as the properties, configuration, and location of the treatment device may be adapted to the needs of a particular patient. For example, the pedal may be used to increase or decrease range of motion to comply with a treatment plan designed for the user (e.g., via a telemedicine session or upon detection of certain measurements). In response, it can be dynamically adjusted on the fly (based on programmed configuration). In some embodiments, the healthcare provider may remotely adapt the therapy device to the patient's needs by having control instructions transmitted from the server to the therapy device during the telemedicine session. Such adaptive properties may improve patient recovery outcomes, further advance the goals of personalized medicine, and allow individualization of treatment regimens on an individual basis.

Technical problems associated with receiving information about a patient's medical condition in disparate formats may arise. For example, the server may retrieve a patient's medical Status information may be received. That is, some sources used by various healthcare providers may be installed on their local computing devices and use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain the format used by the source via interfaces exposed by the API used by the source. In some embodiments, when information is received from a source, the API may map, transform, and/or transform the format used by the source into a standardized format used by the artificial intelligence engine. Further, the information mapped, transformed, and/or transformed into the standardized format used by the artificial intelligence engine may be accessed by the artificial intelligence engine when implementing any of the techniques disclosed herein. may be stored in a database that Mapping, transforming, and/or using standardized information into a standardized format may allow for more accurate determination of procedures to perform for patients and/or billing sequences.

To that end, standardized information may enable the generation of treatment plans and/or billing sequences that have a specific format that can be processed by various applications (eg, telehealth). For example, applications such as telehealth applications may run on various computing devices of medical personnel and/or patients. Applications (eg, stand-alone or web-based) may be provided by the server and configured to process data according to the format in which treatment plans and billing sequences are implemented. Accordingly, the disclosed embodiments are capable of: (i) receiving non-standardized and/or different formats of information from various sources (e.g., EMR systems); (ii) standardizing the information; (iii) treatments that have a standardized form that can be processed by applications (e.g., telehealth applications) running on healthcare professionals and/or patient computing devices based on the standardized information; Generating plans and billing sequences can provide technical solutions.

FIG. 23 generally illustrates a block diagram of a computer-implemented system 3010 for managing treatment plans, hereinafter referred to as the "system." Managing treatment plans may include using an artificial intelligence engine to recommend treatment plans and/or provide excluded treatment plans that should not be recommended to the patient.

System 3010 also includes a server 3030 configured to store (eg, write to associated memory) and provide data related to managing treatment plans. Server 3030 may include one or more computers and may take the form of one or more distributed and/or virtualized computers. Server 3030 also includes a first communication interface 3032 configured to communicate with (eg, or via) clinician interface 3020 via a first network 3034 . In some embodiments, first network 3034 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like. The server 3030 includes a first processor 3036 and a first machine-readable storage memory 3038, which may be abbreviated as "memory" and implemented by the first processor 3036. holds first instructions 3040 for performing various actions of the server 3030 for

Server 3030 is configured to store data relating to treatment plans. For example, memory 3038 includes system data store 3042 configured to hold system data, such as data relating to treatment plans for treating one or more patients. Server 3030 is also configured to store data relating to patient performance in accordance with the treatment plan. For example, memory 3038 includes a patient data store 3044 configured to hold patient data, such as data relating to one or more patients, including data representing each patient's performance within a treatment plan.

Additionally or alternatively, characteristics of people (e.g., personal, practice, measurements, etc.), treatment plans followed by people, levels of compliance with treatment plans, and results of treatment plans may be correlated and other statistical Alternatively, a probabilistic measure may be used to allow treatment plans to be divided into databases corresponding to different patient cohorts in the patient data store 3044, or to be divided into such databases. For example, a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment regimen followed by a first patient, and a first outcome of the treatment regimen. Data relating to the first cohort of the first patient having can be stored in the first patient database. a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment regimen followed by the second patient, and a second outcome of the treatment regimen; Data for a second cohort of a second patient can be stored in a second patient database. Any single characteristic or any combination of characteristics may be used to segregate cohorts of patients. In some embodiments, 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, except as limited by mathematical combinations and/or partitioning theory.

This characteristic data, treatment plan data, and outcome data may be obtained from multiple treatment devices and/or computing devices over time and stored in database 3044 . Characteristic data, treatment plan data, and outcome data may be correlated in a patient-cohort database within patient data store 3044 . People characteristics may include personal information, performance information, and/or measurement information.

In addition to historical information about other people stored in patient cohort equivalent databases, real-time or near real-time information based on current patient characteristics about current patients being treated is stored in appropriate patient cohort equivalent databases. can be stored. A patient's characteristics may be determined to match or resemble characteristics of another person in a particular cohort (eg, Cohort A), and the patient may be assigned to that cohort.

In some embodiments, the server 3030 uses one or more machine learning models 3013 to run an artificial intelligence (AI) engine 3011 that implements at least one of the embodiments disclosed herein. can be implemented. Server 3030 may include training engine 9 capable of generating one or more machine learning models 3013 . The machine learning model 3013 assigns people to specific cohorts based on their characteristics, selects treatment plans using real-time and historical data correlation with patient cohort equivalents, and treats devices 3070, among other things. can be trained to control

One or more machine learning models 3013 may be generated by training engine 309 and implemented in computer instructions executable by one or more processing devices of training engine 309 and/or server 3030 . Training engine 309 may train one or more machine learning models 3013 to generate one or more machine learning models 3013 . One or more machine learning models 3013 may be used by artificial intelligence engine 3011 .

The training engine 309 may be a rackmount server, router computer, personal computer, portable digital assistant, smart phone, laptop computer, tablet computer, netbook, desktop computer, Internet of Things (IoT) device, or any other suitable computing device. device, or a combination thereof. Training engine 9 may be a cloud-based or real-time software platform and may include privacy software or protocols and/or security software or protocols.

To train one or more machine learning models 3013, the training engine 309 uses a training data set of a corpus of characteristics of people who have used the treatment devices 3070 to implement treatment plans, people using the treatment devices 3070 Details of the treatment plan performed by (e.g., treatment protocol including exercise, amount of time exercise is performed, frequency of exercise performed, schedule of exercise, parameters of treatment device 3070 throughout each step of the treatment plan /configuration/settings, etc.), and the results of treatment plans performed by people. One or more machine learning models 3013 can be trained to match patterns of patient characteristics with characteristics of other people assigned to a particular cohort. The term "match" can refer to an exact match, a relative match, a substantial match, and the like. One or more machine learning models 3013 can be trained to receive patient characteristics as input, map the characteristics to characteristics of people assigned to a cohort, and select treatment plans from that cohort. One or more machine learning models 3013 may also be trained to control the machine learning device 3070 based on the treatment plan.

Different machine learning models 3013 can be trained to recommend different treatment plans for different desired outcomes. For example, one machine learning model can be trained to recommend treatment plans for the most effective recovery, while another machine learning model is trained to recommend treatment plans based on speed of recovery. can be

Using training data, including training inputs and corresponding target outputs, one or more machine learning models 3013 may reference model artifacts created by training engine 309 . The training engine 309 can find patterns in the training data that map training inputs to target outputs and produce a machine learning model 3013 that incorporates these patterns. In some embodiments, artificial intelligence engine 3011 and/or training engine 309 may reside in separate components depicted in FIG. 23 (eg, assistant interface 3094, clinician interface 3020, etc.).

One or more machine learning models 3013 may include, for example, single-level linear or non-linear operations (eg, support vector machines (SVM)), and machine learning models 3013 may be deep networks, i.e., two or more (eg, 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 is the neuron's output signal to the inputs of the remaining neurons and to itself). For example, a machine learning model may include multiple layers and/or hidden layers that perform computations (eg, dot products) using various neurons.

System 3010 also includes a patient interface 3050 configured to communicate information to the patient and receive feedback from the patient. Specifically, the patient interface includes an input device 3052 and an output device 3054, which may be collectively referred to as patient user interfaces 3052,3054. Input devices 3052 may include one or more devices such as a keyboard, mouse, touch screen input, gesture sensors, and/or microphones and processors configured for speech recognition. Output device 3054 may take one or more different forms, including, for example, a computer monitor or display screen on a tablet, smartphone, or smartwatch. Output device 3054 may include other hardware and/or software components, such as projectors, virtual reality capabilities, augmented reality capabilities, and the like. Output device 3054 may incorporate a variety of different visual, audio, or other presentation technologies. For example, output device 3054 may provide non-visual displays such as audio signals that may include speech and/or other sounds such as tones, chimes, and/or melodies that may signal various conditions and/or directions. can contain. Output device 3054 may comprise one or more different display screens for presenting various data and/or interfaces or controls for use by the patient. The output device 3054 may include graphics, which may be presented by a web-based interface and/or by computer programs or applications (apps).

As illustrated generally in FIG. 23, patient interface 3050 may also be referred to as a remote communication interface configured to communicate with server 3030 and/or clinician interface 3020 via second network 3058. 2 communication interfaces 3056 . In some embodiments, second network 3058 may include a local area network (LAN), such as an Ethernet network. In some embodiments, second network 3058 may include the Internet, and communication between patient interface 3050 and server 3030 and/or clinician interface 3020 may use, for example, a virtual private network (VPN). It may be secured via encryption, such as by In some embodiments, the second network 3058 may include wired and/or wireless network connections such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like. In some embodiments, second network 3058 may be the same as first network 3034 and/or may be operatively coupled to first network 3034 .

The patient interface 3050 includes a second processor 3060 and a second machine-readable storage memory 3062 that retains second instructions 3064 for execution by the second processor 3060 to perform various actions of the patient interface 3050. and including. The second machine-readable storage memory 3062 also includes a local data store 3066 configured to hold data such as data relating to treatment plans and/or patient data such as data representing patient performance within treatment plans. . The patient interface 3050 also includes a local communication interface 3068 configured to communicate with various devices for use by the patient in the vicinity of the patient interface 3050. Local communication interface 3068 may include wired and/or wireless communications. In some embodiments, local communication interface 3068 may include local wireless networks, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like.

The system 3010 also includes a therapy device 3070 configured to be manipulated by the patient and/or to manipulate body parts of the patient to perform activities according to the treatment plan. In some embodiments, treatment device 3070 can take the form of an exercise and rehabilitation apparatus configured to implement and/or assist in the implementation of a rehabilitation regimen, which can be an orthopedic rehabilitation regimen, wherein treatment is , rehabilitation of patient body parts such as joints or bones or muscle groups. Treatment device 3070 is any suitable medical, rehabilitation, therapeutic, etc. apparatus configured to be remotely controlled via another computing device to treat and/or exercise a patient. could be. Treatment device 3070 can be an electromechanical machine, including one or more weights, electromechanical bicycles, electromechanical spin wheels, smart mirrors, treadmills, and the like. Body parts can include, for example, the spine, hands, feet, knees, or shoulders. A body part may include a portion of a joint, bone, or muscle group, such as one or more vertebrae, tendons, or ligaments. As generally illustrated in FIG. 23, treatment device 3070 includes controller 3072, which may include one or more processors, computer memory, and/or other components. Treatment device 3070 also includes a fourth communication interface 3074 configured to communicate with (eg, through, or beyond) patient interface 3050 via local communication interface 3068 . The therapeutic device 3070 also includes one or more internal sensors 3076 and actuators 3078, such as motors. Actuators 3078 may be used, for example, to move a patient's body part and/or resist force by the patient.

Internal sensors 3076 may measure one or more operational characteristics of therapeutic device 3070 such as, for example, force position, velocity, velocity, and/or acceleration. In some embodiments, internal sensor 3076 may include a position sensor configured to measure at least one of linear or angular motion of the patient's body part. For example, an internal sensor 3076 in the form of a position sensor can measure the distance a patient can move a portion of the treatment device 3070, such distance being the range of motion that a patient's body part can achieve. can correspond to In some embodiments, internal sensor 3076 may include a force sensor configured to measure force applied by the patient. For example, an internal sensor 3076 in the form of a force sensor may measure the force or load that a patient can apply to treatment device 3070 using a particular body part.

The system 3010 illustrated generally in FIG. 23 also includes a gait sensor 3082 that communicates with the server 3030 via the local communication interface 3068 of the patient interface 3050. Gait sensor 3082 may track and store the number of steps taken by the patient. In some embodiments, the gait sensor 3082 may take the form of a wristband, wristwatch, or smartwatch. In some embodiments, the gait sensor 3082 may be integrated within a phone such as a smart phone.

The system 3010 illustrated generally in FIG. 23 also includes a goniometer 3084 that communicates with server 3030 via local communication interface 3068 of patient interface 3050 . Goniometer 3084 measures the angle of the patient's body part. For example, goniometer 3084 may measure the angle of flexion of the patient's knee or elbow or shoulder.

System 3010 may also include one or more additional sensors (not shown) that communicate with server 3030 via local communication interface 3068 of patient interface 3050 . One or more additional sensors can measure other patient parameters, such as heart rate, body temperature, blood pressure, glucose levels, levels of other biomarkers, one or more vital signs, and the like. For example, the one or more additional sensors can be optical sensors that detect near-infrared reflections from circulating blood below skin level. Optical sensors may take the form of wristbands, wristwatches, or smartwatches and measure glucose levels, heart rate, blood oxygen saturation levels, one or more vital signs, and the like.

In some embodiments, one or more additional sensors may be located within the patient's body, within the room or physical space in which the treatment device 3070 is being used, or on a person's body (e.g., a skin patch). or included in the therapy device 3070, one or more additional sensors may measure various vital signs or other diagnostically relevant attributes (e.g., heart rate, sweat rate, body temperature, blood pressure , oxygen levels, any suitable vital signs, glucose levels, levels of another biomarker, etc.) may be measured. One or more additional sensors may transmit patient measurements to server 3030 for analysis and processing (eg, used to modify at least the patient's treatment plan based on the measurements).

The system 3010 illustrated generally in FIG. 23 also includes a pressure sensor 3086 that communicates with server 3030 via local communication interface 68 of patient interface 3050 . Pressure sensor 3086 measures the amount of pressure or load exerted by the patient's body part. For example, pressure sensor 86 may measure the amount of force applied by the patient's foot when pedaling a stationary bicycle.

The system 3010 illustrated generally in FIG. 23 also includes a supervisory interface 3090 that may be similar or identical to the clinician interface 3020. In some embodiments, supervisory interface 3090 may have enhanced functionality over that provided by clinician interface 3020 . Supervisory interface 90 may be configured for use by a person responsible for treatment planning, such as an orthopedic surgeon.

The system 3010 illustrated generally in FIG. 23 also includes a reporting interface 3092 that may be similar or identical to the clinician interface 3020. In some embodiments, the reporting interface 3092 may have less functionality than that provided by the clinician interface 3020. For example, reporting interface 3092 may not have the ability to modify treatment plans. Such reporting interface 3092 may be used, for example, by a claimant to determine use of system 3010 for billing purposes. In another example, reporting interface 3092 does not have the ability to display patient identifiable information, and for certain data fields about data subjects and/or for certain data fields about data subject quasi-identifiers, Only depersonalized and/or anonymized data may be presented. Such a reporting interface 3092 can be used, for example, by researchers to determine different effects of treatment regimens on different patients.

System 3010 can be used for medical providers, such as those described herein, to remotely communicate with (eg, through, or beyond) patient interface 3050 and/or treatment device 3070 . Includes assistant interface 3094 . Such remote communication may allow healthcare providers to use system 3010 to provide assistance or guidance to patients. More specifically, assistant interface 3094 transmits telemedicine signals 3096, 3097, 3098a, 3098b, 3099a, 3099b via a network connection, such as via first network 3034 and/or second network 3058, for example. configured to communicate with a patient interface 3050; Telemedicine signals 3096, 3097, 3098a, 3098b, 3099a, 3099b include audio signal 3096, audio-visual signal 3097, interface control signal 3098a for controlling the function of patient interface 3050, and monitoring the status of patient interface 3050. It includes one of an interface monitor signal 3098 b , a device control signal 99 a for changing operating parameters of the therapy device 3070 , and/or a device monitor signal 3099 b for monitoring the status of the therapy device 3070 . In some embodiments, each of the control signals 3098a, 3099a may be unidirectional and convey commands from the assistant interface 3094 to the patient interface 3050. In some embodiments, in response to successfully receiving the control signals 3098a, 3099a and/or conveying successful and/or unsuccessful implementation of the requested control action, the acknowledgment message is a patient It can be sent from interface 3050 to assistant interface 3094 . In some embodiments, each of the monitor signals 3098b, 3099b can be a one-way status information command from the patient interface 3050 to the assistant interface 3094. In some embodiments, an acknowledgment message may be sent from assistant interface 3094 to patient interface 3050 in response to successfully receiving one of monitor signals 3098b, 3099b.

In some embodiments, patient interface 3050 provides device control signals 3099a and device monitor signals 3099b between therapy device 3070 and one or more other devices, such as assistant interface 3094 and/or server 3030. It can be configured as a pass-through. For example, patient interface 3050 may be configured to transmit device control signals 3099a in response to device control signals 3099a in telemedicine signals 3096, 3097, 3098a, 3098b, 3099a, 3099b from assistant interface 3094.

In some embodiments, assistant interface 3094 may be presented on a shared physical device as clinician interface 3020 . For example, clinician interface 3020 may include one or more screens that implement assistant interface 3094 . Alternatively or additionally, clinician interface 3020 may include additional hardware components, such as video cameras, speakers, and/or microphones, for implementing aspects of assistant interface 3094 .

In some embodiments, one or more portions of the telemedicine signals 3096, 3097, 3098a, 3098b, 3099a, 3099b are sourced from pre-recorded sources (e.g., for example, audio recordings, video recordings, or animations). For example, a tutorial video can be streamed from server 3030 and presented on patient interface 3050 . Content from pre-recorded sources may be requested by the patient via patient interface 3050 . Alternatively, via controls on assistant interface 3094 , the healthcare provider may play content from pre-recorded sources on patient interface 3050 .

Assistant interface 3094 includes assistant input device 3022 and assistant display 3024, which may be collectively referred to as assistant user interfaces 3022,3024. Assistant input device 3022 may include one or more of a phone, keyboard, mouse, trackpad, or touch screen, for example. Alternatively or additionally, assistant input device 3022 may include one or more microphones. In some embodiments, the one or more microphones may take the form of a telephone handset, headset, or one or more wide area microphones configured for the healthcare provider to speak to the patient via the patient interface 3050. . In some embodiments, assistant input device 3022 comprises hardware and/or software configured to interpret commands spoken by a healthcare provider using one or more microphones; It can be configured to provide voice-based functionality. Assistant input device 3022 may be provided by or include functionality similar to existing voice-based assistants such as Siri from Apple, Alexa from Amazon, Google Assistant, or Bixby from Samsung. Assistant input device 3022 may include other hardware and/or software components. Assistant input device 3022 may include one or more general purpose and/or special purpose devices.

Assistant display 3024 can take one or more different forms, including, for example, a computer monitor or display screen on a tablet, smartphone, or smartwatch. Assistant display 3024 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities. Assistant display 3024 may incorporate a variety of different visual, audio, or other presentation technologies. For example, assistant display 3024 may include non-verbal signals, such as voice signals, which may include speech and/or other sounds, such as tones, chimes, melodies, and/or music, which may signal various conditions and/or directions. A visual display may be included. Assistant display 3024 may comprise one or more different display screens that present various data and/or interfaces or controls for use by the healthcare provider. Assistant display 3024 may include graphics, which may be presented by a web-based interface and/or by computer programs or applications (apps).

In some embodiments, system 3010 may provide computer translation of language from assistant interface 3094 to patient interface 3050 and/or vice versa. Computer conversion of language may include computer conversion of speech and/or computer conversion of text. Additionally or alternatively, system 3010 may provide speech recognition of text and/or vocalizations. For example, system 10 may convert speech to printed text and/or system 3010 may vocalize words from printed text. System 3010 may be configured to recognize words spoken by any or all of the patient, clinician, and/or healthcare provider. In some embodiments, the system 3010 can be configured to recognize and respond to requests or commands spoken by the patient. For example, system 3010 may automatically initiate a telemedicine session in response to a verbal command (which may be given in any one of several different languages) by the patient.

In some embodiments, server 3030 may generate aspects of assistant display 3024 for presentation by assistant interface 3094 . For example, server 3030 may include a web server configured to generate display screens for presentation on assistant display 3024 . For example, the artificial intelligence engine 3011 generates recommended treatment plans and/or excluded treatment plans for the patient and displays those recommended treatment plans and/or for presentation on the assistant display 3024 of the assistant interface 3094. Or it may generate a display screen containing an external treatment plan. In some embodiments, assistant display 3024 may be configured to present a virtualized desktop hosted by server 3030 . In some embodiments, server 3030 may be configured to communicate with (eg, or via) assistant interface 3094 via first network 3034 . In some embodiments, first network 3034 may include a local area network (LAN), such as an Ethernet network.

In some embodiments, first network 3034 may include the Internet, and communication between server 3030 and assistant interface 3094 is by using encryption, for example, over a virtual private network (VPN). may be secured via privacy-enhancing techniques such as; Alternatively or additionally, server 3030 communicates with assistant interface 3094 via one or more networks independent of first network 3034 and/or other means of communication such as direct wired or wireless communication channels. may be configured to communicate (eg, over, or beyond). In some embodiments, the patient interface 3050 and therapy device 3070 may each operate from a patient location that is geographically separated from the location of the assistant interface 3094. For example, patient interface 3050 and therapy device 3070 may be used as part of a home rehabilitation system, which may be assisted remotely by using assistant interface 3094 at a centralized location such as a clinic or call center.

In some embodiments, the assistant interface 3094 is a number of different terminals (e.g., computing devices) that can be grouped together, e.g., at one or more call centers or at one or more clinician's offices. can be one of In some embodiments, multiple assistant interfaces 3094 may be geographically distributed. In some embodiments, a person may work as a healthcare provider remotely from any conventional office infrastructure. Such remote work may be implemented, for example, where assistant interface 3094 takes the form of a computer and/or telephone. This telecommuting functionality may enable telecommuting arrangements, which may include part-time and/or flexible working hours for healthcare providers.

24-25 illustrate one embodiment of a therapeutic device 3070. FIG. More specifically, Figure 24 generally illustrates a therapeutic device 3070 in the form of a stationary cycling machine 3100, which may be referred to as a stationary bike for short. A stationary cycling machine 3100 includes a set of pedals 3102 each mounted on a pedal arm 3104 for rotation about an axis 3106 . In some embodiments, as generally illustrated in FIG. 24, the pedals 3102 are movable on the pedal arm 3104 to adjust the range of motion that the patient uses in pedaling. For example, locating the pedals inward toward the axis 3106 corresponds to a smaller range of motion than when the pedals are locating outward from the axis 3106 . In some embodiments, the pedals may be adjustable inwards and outwards from the plane of rotation. Such techniques may allow the patient's leg width to increase and decrease as the patient's leg pedals. A pressure sensor 3086 is attached to or embedded in one of the pedals 3102 to measure the amount of force applied to the pedals 3102 by the patient. Pressure sensor 3086 may communicate wirelessly to therapy device 3070 and/or to patient interface 3050 .

FIG. 26 generally illustrates a person (patient) using the therapeutic device of FIG. 24, showing sensors and various data parameters connected to the patient interface 3050. An exemplary patient interface 3050 is a tablet computer or smart phone, such as an iPad, iPhone, Android device, or Surface tablet, or a phablet that is manually held by the patient. In some other embodiments, patient interface 3050 may be embedded in or attached to therapeutic device 3070 .

FIG. 26 generally illustrates a patient wearing a gait sensor 3082 on the patient's wrist, and a note reading "Today's Steps 31355" indicates that the gait sensor 3082 records its step count. , indicating that it has been transmitted to the patient interface 3050 . FIG. 26 also generally illustrates the patient wearing the goniometer 3084 on the patient's right knee, and the notation reading "Knee Angle 72°" indicates that the goniometer 3084 measures that knee angle. , indicates that it is transmitting to the patient interface 3050 . FIG. 36 also shows “12.5 lbs force” on the right side of one of the pedals 3102 with pressure sensors 3086 that the right pedal pressure sensor 3086 measures that force reading and outputs to the patient interface 3050. It is generally exemplified to indicate that it is transmitting.

FIG. 26 also shows “27 pounds of force” on the left side of one of the pedals 4102 with pressure sensors 3086 , the left pedal pressure sensor 3086 measuring and transmitting that force measurement to the patient interface 3050 . It is generally exemplified to indicate that FIG. 26 also generally illustrates other patient data, such as the indication "session time 0:04:13" indicating that the patient has been using the therapy device 3070 for 4 minutes and 13 seconds. This session time may be determined by patient interface 3050 based on information received from therapy device 3070 . Figure 26 also generally illustrates an indicator labeled "Pain Level 3". Such pain levels can be obtained from patents in response to a petition, such as a question, presented on the patient interface 3050 .

FIG. 27 is an illustrative embodiment of overview display 3120 of assistant interface 3094 . Specifically, overview display 3120 presents several different controls and interfaces for a healthcare provider to remotely assist a patient using patient interface 3050 and/or treatment device 3070 . This remote assistance functionality may also be referred to as telemedicine or telehealth.

Specifically, overview display 3120 includes a patient profile display 3130 that presents historical information about the patient using therapy device 3070 . The patient profile display 3130 may take the form of a portion or region of the overview display 3120, as generally illustrated in FIG. 27, although the patient profile display 3130 may take other forms, such as a separate screen or pop-up window. can be taken.

In some embodiments, the patient profile display 3130 may include a limited subset of patient history information. More specifically, the data presented on patient profile display 3130 may depend on the healthcare provider's need for that information. For example, a healthcare provider assisting a patient with a medical issue may be provided with historical medical information about the patient, whereas a technician troubleshooting a treatment device 3070 issue may have more information about the patient. A limited set can be provided. For example, a technician may be given only the patient's name.

The patient profile display 3130 may include depersonalized and/or anonymized data or use any privacy-enhancing technology in ways that may violate patient confidentiality requirements; Sensitive patient data may be prevented from being communicated. Such privacy-enhancing technologies include, but are not limited to, the Health Insurance Portability and Accountability Act (HIPAA) or the General Data Protection Regulation (GDPR), where patients may be considered "data subjects." compliance with laws, regulations or other governance rules.

In some embodiments, the patient profile display 3130 may present information regarding the treatment plan to be followed when the patient uses the treatment device 3070. Such treatment plan information may be restricted to healthcare providers. For example, a healthcare provider assisting a patient with a problem with a treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting a problem with the treatment device 3070 may not have any information about the patient's treatment plan. May not be provided.

In some embodiments, one or more recommended treatment plans and/or excluded treatment plans may be presented to the healthcare provider in the patient profile display 3130. One or more recommended treatment plans and/or ruled out treatment plans may be generated by the artificial intelligence engine 3011 of the server 3030 and received in real-time from the server 3030, particularly during a telemedicine or telehealth session. An example of presenting one or more recommended and/or ruled out treatment plans is described below with reference to FIG.

The example overview display 3120 illustrated generally in FIG. 27 also includes a patient status display 3134 that presents status information regarding the patient using the therapy device. The patient status display 3134 may take the form of a portion or region of the overview display 3120, as generally illustrated in FIG. 27, although the patient status display 3134 may take other forms, such as a separate screen or pop-up window. can be taken.

The patient status display 3134 may be from one or more of the external sensors 3082, 3084, 3086 and/or one or more internal sensors 3076 of the therapy device 3070, and/or as previously described herein. or sensor data 3136 from one or more additional sensors (not shown). In some embodiments, the patient status display 3134 may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the therapy device 3070. The one or more wearable devices may include watches, bracelets, necklaces, chest bands, and the like. The one or more wearable devices monitor the patient's heart rate, body temperature, blood pressure, glucose levels, blood oxygen saturation, one or more vital signs, etc. while the patient is using the therapy device 3070. can be configured. In some embodiments, the patient status display 3134 may present other data 3138 about the patient, such as last reported pain level or progress within a treatment plan.

What data is available to view and/or modify for any or all of the user interfaces 3020, 3050, 3090, 3092, 3094 of the system 3010 using user access controls? can restrict access, including In some embodiments, user access controls may be used to control what information is available to any given person using system 3010 . For example, data presented on assistant interface 3094 may be controlled by user access controls, with permissions set according to the needs and/or entitlements of the healthcare provider/user to view that information.

The example overview display 3120 illustrated generally in FIG. 27 also includes a help data display 3140 that presents information for use by the healthcare provider in assisting the patient. Help data display 3140 may take the form of a portion or region of summary display 3120, as illustrated generally in FIG. Help data display 3140 may take other forms, such as a separate screen or pop-up window. Help data display 3140 may include, for example, presenting answers to frequently asked questions regarding use of patient interface 3050 and/or treatment device 3070 .

Help data display 3140 may also include research data or best practices. In some embodiments, the help data display 3140 may present scripts for answers or explanations in response to patient questions. In some embodiments, help data display 3140 may present flowcharts or walkthroughs for healthcare providers to use in determining root causes and/or solutions to patient problems.

In some embodiments, assistant interface 3094 presents two or more help data displays 3140, which may be the same or different, for simultaneous presentation of help data for use by a healthcare provider. obtain. For example, a first help data display can be used to present a troubleshooting flowchart for determining the root of a patient's problem, and a second help data display can be used by a healthcare provider to read to the patient. Script information, preferably information such as instructions for the patient to perform some action, may be presented, which may help narrow down or solve the problem. In some embodiments, the second help data display may automatically populate the script information based on the input to the troubleshooting flowchart in the first help data display.

The example overview display 3120 illustrated generally in FIG. 27 also provides information about the patient interface 3050 and/or patient interface controls 3150 for modifying one or more settings of the patient interface 3050. including. Patient interface controls 3150 may take the form of a portion or region of overview display 3120, as generally illustrated in FIG. Patient interface controls 3150 may take other forms, such as separate screens or pop-up windows. Patient interface control 3150 may present the communicated information to assistant interface 3094 via one or more of interface monitor signals 3098b.

As illustrated generally in FIG. 27, patient interface controls 3150 include a display feed 3152 of displays presented by patient interface 3050. FIG. In some embodiments, display feed 3152 may include a live copy of the display screen currently being presented to the patient by patient interface 3050 . In other words, display feed 3152 may present an image of what is presented on the display screen of patient interface 3050 .

In some embodiments, display feed 3152 may include informal information about the display screen currently being presented by patient interface 3050, such as screen name or screen number. Patient interface controls 3150 may include patient interface settings controls 3154 for a healthcare provider to adjust or control one or more settings or aspects of patient interface 3050 . In some embodiments, the patient interface settings control 3154 may cause the assistant interface 3094 to generate and/or transmit interface control signals 3098 to control features or settings of the patient interface 3050 .

In some embodiments, patient interface settings control 3154 may include collaborative browsing or co-browsing capabilities for healthcare providers to remotely view and/or control patient interface 3050 . For example, the patient interface settings control 3154 allows the healthcare provider to remotely enter text into one or more text entry fields on the patient interface 3050 and/or using the mouse or touch screen of the assistant interface 3094. to remotely control the cursor on the patient interface 3050 .

In some embodiments, using the patient interface 3050, the patient interface settings control 3154 may allow the healthcare provider to change settings that the patient cannot change. For example, the patient interface 3050 may be blocked from accessing language settings to prevent the patient from inadvertently switching the language used for display on the patient interface 3050, whereas the patient interface 3050 may be blocked from accessing language settings. Settings control 3154 may allow a healthcare provider to change the language setting of patient interface 3050 . In another example, the patient interface 3050 uses a font size to prevent the patient from inadvertently switching the font size used for display on the patient interface 3050 such that the display becomes unreadable to the patient. The patient interface settings control 3154 may allow the healthcare provider to change the font size setting of the patient interface 3050, whereas it may not be possible to change the settings to a smaller size.

The example overview display 3120 illustrated generally in FIG. 27 also includes a patient interface 3050 and one or more other devices 3070, 3082, such as a therapeutic device 3070, a gait sensor 3082, and/or a goniometer 3084. 3084 and an interface communications display 3156 that shows the status of communications between and. Interface communications display 3156 may take the form of a portion or region of overview display 3120, as generally illustrated in FIG.

Interface communication display 3156 may take other forms, such as a separate screen or pop-up window. Interface communication display 3156 may include controls for a healthcare provider to remotely modify communication with one or more of other devices 3070 , 3082 , 3084 . For example, the healthcare provider may choose to reset communication with one of the other devices 3070, 3082, 3084 or establish communication with a new one of the other devices 3070, 3082, 3084. , the patient interface 50 can be remotely commanded. This functionality is useful, for example, if the patient has a problem with one of the other devices 3070, 3082, 3084, or if the patient receives a new or replacement one of the other devices 3070, 3082, 3084. can be used if

The example overview display 3120 illustrated generally in FIG. 27 also includes device controls 3160 for the health care provider to view and/or control information regarding the treatment device 3070 . Device controls 3160 may take the form of a portion or region of overview display 3120, as generally illustrated in FIG. Device control 3160 may take other forms, such as a separate screen or pop-up window. The device controls 3160 may include a device status display 3162 with information regarding the current status of the device. Device status display 3162 may present information communicated to assistant interface 3094 via one or more of device monitor signals 3099b. Device status display 3162 may indicate whether therapy device 3070 is currently in communication with patient interface 3050 . Device status display 3162 may present other current and/or historical information regarding the status of therapy device 3070 .

Device controls 3160 may include device settings controls 3164 for a healthcare provider to adjust or control one or more aspects of treatment device 3070 . Device settings control 3164 provides assistant interface 3094 with operating parameters and/or one or more characteristics of therapy device 3070 (e.g., pedal radius setting, resistance setting, target RPM, other suitable characteristics of therapy device 3070, or A device control signal 3099 (eg, which as described may be referred to as a treatment plan input) may be generated and/or transmitted to change a combination thereof).

The device settings controls 3164 can include a mode button 3166 and a position control 3168 that allow the healthcare provider to put the actuator 3078 of the treatment device 3070 into manual mode and then use the position control 3168 to set the actuator to 3078 may be used together so that settings such as position or speed of 3078 can be changed. A mode button 3166 may provide settings, such as position, for switching between automatic and manual modes.

In some embodiments, one or more settings may be adjustable at any time and without an associated auto/manual mode. In some embodiments, a healthcare provider may change the operating parameters of the therapy device 3070, such as the pedal radius setting, while the patient is actively using the therapy device 3070. Such “on-the-fly” adjustments may or may not be available to the patient using patient interface 3050 .

In some embodiments, the device settings controls 3164 may allow the healthcare provider to change settings that the patient cannot change using the patient interface 3050. For example, the patient interface 3050 may be prevented from changing pre-configured settings, such as the height or tilt settings of the treatment device 3070, whereas the device settings control 3164 prevents the healthcare provider from It may be provided to change the height or tilt settings of the device 3070 .

The example overview display 3120 illustrated generally in FIG. 27 may also include a patient communication control 3170 for controlling an audio or audiovisual communication session with the patient interface 3050. A communication session with patient interface 3050 may include a live feed from assistant interface 3094 for presentation by an output device of patient interface 3050 . A live feed may take the form of an audio feed and/or a video feed. In some embodiments, patient interface 3050 may be configured to provide two-way audio or audiovisual communication with a person using assistant interface 3094 . Specifically, a communication session with patient interface 3050 may include an interactive (two-way) video feed or an audiovisual feed, with patient interface 3050 and assistant interface 3094 each presenting the other's video.

In some embodiments, the patient interface 3050 may present video from the assistant interface 3094 while the assistant interface 3094 presents audio only, or the assistant interface 3094 presents live audio from the patient interface 3050. No signal or visual signal is presented. In some embodiments, the assistant interface 3094 may present video from the patient interface 3050 while the patient interface 3050 presents audio only, or the patient interface 3050 presents live audio from the assistant interface 3094. No signal or visual signal is presented.

In some embodiments, an audio or audiovisual communication session with the patient interface 3050 may occur, at least in part, while the patient is performing a rehabilitation regimen on the body part. Patient communication control 3170 may take the form of a portion or region of overview display 3120, as generally illustrated in FIG. Patient communication control 3170 may take other forms, such as a separate screen or pop-up window.

Voice and/or audio-visual communication is used by the assistant interface 3094 and/or by one or more separate devices such as a telephone system or by the healthcare provider while the healthcare provider uses the assistant interface 3094. It may be processed and/or directed by a videoconferencing system. Alternatively or additionally, voice communication and/or audiovisual communication may include communication with a third party. For example, system 3010 allows a healthcare provider to initiate a three-way conversation with a patient and a subject matter expert, such as a healthcare provider or specialist, regarding the use of a particular piece of hardware or software. can make it possible. An exemplary patient communication control 3170, illustrated generally in FIG. 27, includes a call control 3172 for use by a healthcare provider in managing various aspects of voice or audiovisual communication with a patient. including. Call controls 3172 include a disconnect button 3174 for the healthcare provider to end the voice or audiovisual communication session. Call controls 3172 also include a mute button 3176 for temporarily muting audio or audiovisual signals from assistant interface 3094 . In some embodiments, call control 3172 may include other features such as a hold button (not shown).

Call controls 3172 also include one or more record/play buttons, such as record, play, and pause buttons, for controlling recording and/or playback of audio and/or video from a conference call session on patient interface 3050 . A playback control section 3178 is included. The call control 3172 also includes a video feed display 3180 for presenting still and/or video images from the patient interface 3050 and a self-video display 3182 showing current images of the healthcare provider using the assistant interface 3094. ,including. Self-video display 3182 may be presented as a picture-in-picture format within a section of video feed display 3180, as generally illustrated in FIG. Alternatively or additionally, selfie video display 3182 may be presented separately and/or independently from video feed display 3180 .

The example overview display 3120 illustrated generally in FIG. 27 also includes a third party communication control 3190 for use in conducting voice and/or audiovisual communications with a third party. Third party communication control 3190 may take the form of a portion or region of overview display 3120, as generally illustrated in FIG. Third party communication control 3190 may take other forms, such as a separate on-screen display or a pop-up window.

Third party communication control 3190 provides contact lists and/or It may include one or more controls, such as buttons or controls. Third party communication controls 190 may include teleconferencing capabilities for third parties to simultaneously communicate with both the healthcare provider via assistant interface 3094 and the patient via patient interface 3050 . For example, system 3010 may provide for a healthcare provider to initiate a three-way conversation with a patient and a third party.

FIG. 28 generally illustrates an exemplary block diagram for training a machine learning model 3013 to output a treatment plan 3602 for a patient based on data 3600 regarding the patient in accordance with this disclosure. Data regarding other patients may be received by server 3030 . Other patients may be performing treatment regimens using different treatment devices.

The data may include characteristics of other patients, details of treatment plans performed by other patients, and/or results of implementing treatment plans (e.g., recovery rate of a patient's body part, amount of recovery of a patient's body part, etc.). , increase or decrease in muscle strength of a portion of the patient's body, increase or decrease in range of motion of the portion of the patient's body, etc.).

Data are assigned to the various cohorts as depicted. Cohort A contains data of patients with similar first characteristics, first treatment regimens, and first outcomes. Cohort B includes data from patients with similar second characteristics, second treatment regimens, and second outcomes. For example, Cohort A may include first characteristics of patients in their twenties without any medical condition who have undergone surgery for a fractured limb, and the treatment plan for these patients may include a specific treatment protocol. (e.g., use the treatment device 70 for 30 minutes five times a week for 3 weeks, and the value of the properties, configurations, and/or settings of the treatment device 70 is X (where X is a number) for the first 2 weeks; The last week is set to Y (where Y is a number).

Cohort A and Cohort B may be included in the training dataset used to train the machine learning model 3013 . A machine learning model 3013 can be trained to match patterns between characteristics of each cohort and output a treatment plan that provides results. Thus, when new patient data 3600 is input to the trained machine learning model 3013, the trained machine learning model 3013 matches the traits contained in the data 3600 to the traits of either Cohort A or Cohort B. and output an appropriate treatment plan 3602 . In some embodiments, the machine learning model 3013 can be trained to output one or more excluded treatment plans that should not be performed by new patients.

FIG. 29 generally illustrates one embodiment of a summary display 3120 of the assistant interface 3094 presenting recommended and excluded treatment plans in real-time during a telemedicine session according to the present disclosure. As depicted, summary display 3120 only includes sections of patient profile 3130 and video feed display 3180 , including selfie video display 3182 . Any suitable configuration of the controls and interfaces of overview display 3120 described with reference to FIG. obtain.

A healthcare provider using an assistant interface 3094 (e.g., a computing device) during a telemedicine care session can view an overview display 3120 (e.g., on display screen 3024 of assistant interface 3094) that also presents video from the patient on video feed display 3180. may be presented in the self-video 3182 in a portion of the user interface presented in the . Additionally, the video feed display 3180 also allows the healthcare provider to share recommended and/or ruled out treatment plans with the patient on the patient interface 3050 in real time or near real time during the telemedicine session. may include graphical user interface (GUI) objects 3700 (eg, buttons) that allow A healthcare provider may select GUI object 3700 to share recommended and/or excluded treatment plans. As depicted, another portion of overview display 3120 includes patient profile display 3130 .

Patient profile display 3130 presents two exemplary recommended treatment plans 3600 and one exemplary excluded treatment plan 3602 . As described herein, treatment regimens may be recommended taking into account the characteristics of the patient being treated. Characteristics of the patient being treated and a cohort of other people who have used the treatment device 3070 to implement the treatment plan to generate a recommended treatment plan 3600 that the patient should follow to achieve the desired outcome. , can be matched by one or more machine learning models 3013 of the artificial intelligence engine 3011 . Each of the recommended treatment plans may be generated based on different desired results.

For example, as depicted, the patient profile display 3130 reads, "Patient characteristics match characteristics of use in Cohort A. The following treatment plan is based on the patient characteristics and the desired outcome. Recommended. The patient profile display 3130 then presents the recommended treatment plans from Cohort A, each treatment plan providing different results.

As depicted, treatment plan "A" states, "Patient X should use the treatment device for 3030 minutes per day for 4 days to achieve an increase in range of motion of Y%; , has type 2 diabetes, and patient X could be prescribed drug Z for pain control during the treatment plan (drug Z is approved for people with type 2 diabetes). )” is shown. Thus, the generated treatment plan achieves Y% range of motion increase. As can be appreciated, the treatment plan also provides a recommended drug (e.g., Drug Z) to prescribe to the patient for pain management given the patient's known medical condition (e.g., type 2 diabetes). include. That is, the recommended patient medication not only does not conflict with the patient's medical condition, but thereby improves the likelihood of a good patient outcome. This particular example and all such examples elsewhere herein indicate that the treatment regimen generated recommends multiple drugs, or confirms, evaluates, diagnoses and /or is not intended to be limited in any way to address treatment.

Recommended treatment regimen "B" may specify different treatment regimens, including different treatment protocols for treatment devices, different drug regimens, etc., based on different desired outcomes of the treatment regimens.

As depicted, patient profile display 3130 may also present treatment plans 3602 that are excluded. These types of treatment plans are presented to the health care provider using the assistant interface 3094 to warn the health care provider not to recommend certain parts of the treatment plan to the patient. For example, an excluded treatment plan may specify: "Patient X should not use the treatment device for more than 30 minutes a day for heart disease, and Patient X should has type 2 diabetes and patient X should not be prescribed drug M for pain control during the treatment plan (in this scenario, drug M would cause complications to people with type 2 can cause it)." Specifically, the excluded treatment plan points to a limitation of the treatment protocol that patient X should not exercise more than 30 minutes per day due to heart disease. The excluded treatment regimen also points out that patient X should not be prescribed drug M because drug M conflicts with the medical condition of type 2 diabetes.

A healthcare provider may select a treatment plan for the patient on summary display 3120 . For example, a healthcare provider may select from patient treatment plans 3600 using an input peripheral (eg, mouse, touch screen, microphone, keyboard, etc.). In some embodiments, during the telemedicine session, the healthcare provider may discuss the pros and cons of the recommended treatment plan 3600 with the patient.

In any event, the health care provider can select the treatment regimen that the patient should follow to achieve the desired outcome. The selected treatment plan may be transmitted to patient interface 3050 for presentation. The patient may view the selected treatment plan on patient interface 3050 . In some embodiments, health care providers and patients may review details (eg, treatment protocols using treatment devices 3070, dietary regimens, drug regimens, etc.) in real time or near real time during a telemedicine session. In some embodiments, the server 3030 may control the therapeutic device 3070 as the user uses the therapeutic device 3070 based on the selected treatment plan and during the telemedicine session.

FIG. 30 generally illustrates one embodiment of a summary display 3120 of an assistant interface 3094 that presents a recommended treatment plan that has changed as a result of changes in patient data in real-time during a telemedicine session, according to the present disclosure. do. As can be appreciated, the therapy device 3070 and/or any computing device (eg, patient interface 3050) may transmit data while the patient uses the therapy device 3070 to implement a treatment plan. The data may include updated patient characteristics and/or other treatment data. For example, updated characteristics may include new performance information and/or measurement information. The performance information may include velocity of the portion of the therapy device 3070, range of motion achieved by the patient, force exerted on the portion of the therapy device 3070, patient heart rate, patient blood pressure, patient respiratory rate, and the like.

In some embodiments, the data received at the server 3030 can be input to a trained machine learning model 3013, which characterizes the patient's progress towards the current treatment plan. It can be determined that Determining that the patient is on the current treatment plan may cause the trained machine learning model 3013 to adjust the parameters of the treatment device 3070. Adjustments may be based on subsequent steps in the treatment plan to further improve patient performance.

In some embodiments, data received at the server 3030 can be input to a trained machine learning model 3013, which predicts that the patient is not moving toward the current treatment plan (e.g., schedule rather than schedule). being delayed, unable to maintain speed, unable to achieve a certain range of motion, too much pain, etc.) or being ahead of schedule in the current treatment plan (e.g., a certain speed exercised for longer than a specified amount of time without pain, applying more force than specified, etc.).

A trained machine learning model 3013 may determine that the patient's characteristics are no longer consistent with the patient's characteristics within the cohort to which the patient is assigned. Thus, the trained machine learning model 3013 may reassign the patient to another cohort that includes the qualifying trait patient trait. Thus, the trained machine learning model 3013 can select new treatment plans from the new cohort and control the treatment device 3070 based on the new treatment plans.

In some embodiments, prior to controlling treatment device 3070 , server 3030 may provide new treatment plan 3800 to assistant interface 3094 for presentation within patient profile 3130 . As depicted, patient profile 3130 states, "Patient characteristics have changed to match those of use in Cohort B. Based on patient characteristics and desired outcome, the following treatment plan is recommended for patients”. The patient profile 3130 then presents a new treatment plan 3800 ("Patient X should use the treatment device for 10 minutes a day for 3 days to achieve an increase in range of motion of L% A healthcare provider may select a new treatment plan 3800 and the server 3030 may receive the selection.The server 3030 may control the treatment device 3070 based on the new treatment plan 3800. Several implementations. In form, the new treatment plan 3800 can be transmitted to the patient interface 3050 so that the patient can view details of the new treatment plan 3800 .

In some embodiments, the server 3030 may be configured to receive treatment data regarding users using treatment devices 3070 to implement treatment plans. A user may include a patient, user, or person using the therapy device 3070 to perform various exercises.

Treatment data may include various characteristics of the user, various baseline measurement information about the user, various measurement information about the user while the user is using the treatment device 3070, various characteristics of the treatment device 3070, treatment plans, other suitable data, or a combination thereof. In some embodiments, server 3030 may receive therapy data during a telemedicine session.

In some embodiments, at least a portion of the treatment data is received from one or more of the external sensors 3082, 3084, 3086 and/or while the user is performing a treatment plan using the treatment device 3070. Or may include sensor data 3136 from one or more internal sensors 3076 of the treatment device 3070 . In some embodiments, at least some of the therapy data may include sensor data from one or more sensors of one or more wearable devices worn by the patient while using the therapy device 3070. The one or more wearable devices may include watches, bracelets, necklaces, chest bands, and the like. One or more wearable devices may be configured to monitor the patient's heart rate, temperature, blood pressure, one or more vital signs, etc. while the patient is using the therapy device 3070 .

The various characteristics of the treatment device 3070 include one or more settings of the treatment device 3070, the current number of rotations per time period (e.g., one minute, etc.) of a rotating member (e.g., wheel, etc.) of the treatment device 3070, the treatment device It may include resistance settings of 3070, other suitable characteristics of treatment device 3070, or combinations thereof. The baseline information includes one or more of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, the user's blood pressure, the user's other suitable measurement information, while the user is at rest, or combinations thereof. The measurement information may include one or more of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, the user's blood pressure, the user's Other suitable measurement information, or combinations thereof, may be included.

In some embodiments, server 3030 may write therapy data to associated memory for access by artificial intelligence engine 3011 . Artificial intelligence engine 3011 may employ one or more machine learning models 3013 that may be configured to generate one or more predictions using at least a portion of the therapy data. For example, artificial intelligence engine 3011 may use machine learning model 3013 configured to receive treatment data corresponding to a user. Machine learning model 3013 may analyze at least one aspect of the therapy data and generate at least one prediction corresponding to at least one aspect of the therapy data.

At least one prediction may indicate one or more predicted characteristics of the user. The one or more predicted characteristics of the user are the user's predicted vital signs, the user's predicted respiratory rate, the user's predicted heart rate, the user's predicted body temperature, the user's predicted blood pressure, It may include the predicted outcome of the treatment plan being administered by the user, the user's predicted injury resulting from the user performing the treatment plan, or other suitable predicted characteristics of the user.

In some embodiments, server 3030 may receive one or more predictions from artificial intelligence engine 3011 . Server 3030 may identify a threshold corresponding to each prediction received from artificial intelligence engine 3011 . For example, server 3030 may identify one or more characteristics of the user indicated by each prediction.

Server 3030 may access a database, such as database 3044 or other suitable database, configured to associate thresholds with user characteristics and/or combinations of user characteristics. For example, database 3044 may include information relating a first threshold to the user's blood pressure. Additionally or alternatively, database 3044 may include information relating thresholds to the user's blood pressure and the user's heart rate. It should be appreciated that database 3044 may include any number of thresholds associated with any of a variety of user characteristics and/or any combination of user characteristics. In some embodiments, the threshold corresponding to each prediction may include a value or range of values, including upper and lower bounds.

In some embodiments, server 3030 may determine whether the prediction received from artificial intelligence engine 3011 falls within a corresponding threshold. For example, server 3030 may compare the predictions to corresponding thresholds. Server 3030 may determine whether the prediction is within a predetermined range of thresholds. For example, if the threshold includes a value, the predetermined range may include an upper limit above this value (e.g., expressed as a percentage of 0.5% or 1%, or e.g., 250 or 750 (unit of measure or other suitable numerical value); or other suitable upper limit) and a lower limit below this value (e.g. a percentage expression of 0.5% or 1%, or e.g. 250 or 750 (unit of measure or other suitable numerical value), or other suitable lower bound), and Similarly, if the threshold includes a range that includes a first upper bound and a first lower bound (eg, defining an acceptable range of one or more user characteristics corresponding to the prediction), then the predetermined range is the first a second upper limit (e.g., a percentage expression of 0.5% or 1%, or e.g., 250 or 750 (units of measure or other suitable numerical value), or other suitable numerical value) above the upper limit; a second lower limit below the lower limit (e.g., expressed as a percentage of 0.5% or 1%, or e.g., 250 or 750 (units of measure or other suitable numerical value), or other suitable lower limit); . It should be appreciated that the threshold may include any suitable predetermined range and may include any suitable form in addition to or in addition to those described herein.

If the server 3030 determines that the prediction is within the threshold range, the server 3030 communicates with an interface, such as a summary display 3120, on the healthcare provider's computing device assisting the user to provide prediction and treatment data. may communicate with (eg, or via). In some embodiments, server 3030 may use therapy data and/or predictions to generate therapy information. Treatment information may be used by a user to implement a treatment plan during use of the treatment device 3070 such that treatment data and/or predictions may be presented at a computing device of a healthcare provider responsible for implementation of the treatment plan by the user. may include a formatted summary of In some embodiments, patient profile display 3120 may include and/or display treatment information.

Server 3030 may be configured to provide treatment information in summary display 3120 . For example, server 3030 may store therapy information for access by summary display 3120 and/or communicate therapy information to summary display 3120 . In some embodiments, server 3030 provides treatment information to patient profile display 3130, or other suitable section, portion, or component of summary display 3120, or to any other suitable display or interface. obtain.

In some embodiments, the server 3030 modifies at least one aspect of the treatment plan in response to determining that the prediction is within the threshold and/or modifies one of the treatment devices 3070 based on the prediction. You can modify more than one characteristic. In some embodiments, the server 3030 may control the therapy device 3070 based on predictions generated while the user is using the therapy device 3070 during a telemedicine session. For example, server 3030 may control one or more characteristics of treatment device 3070 based on predictions and/or treatment plans.

A health care provider may be a health care practitioner (such as a doctor, nurse, therapist, etc.), an exercise professional (such as a coach, trainer, dietitian, etc.), or at least one of a medical and athletic attribute. It may include another professional (eg, exercise physiologist, physical therapist, occupational therapist, etc.) sharing one. As used herein, but not limited to the foregoing, a "medical provider" may be a human, robot, virtual assistant, virtual assistant in virtual and/or augmented reality, or a software program, integrated software and an artificial intelligence entity comprising hardware, or hardware alone.

In some embodiments, the interface may include a graphical user interface configured to provide treatment information and receive input from a healthcare provider. The interface may be activated and/or driven by a text input field, a drop-down selection input field, a radio button input field, a virtual switch input field, a virtual lever input field, by voice, by touch, by touch, by biometrics, or otherwise. may include one or more input fields, such as a default input field, other suitable input fields, or combinations thereof.

In some embodiments, a healthcare provider may review treatment information and/or predictions. A healthcare provider may determine whether to modify at least one aspect of the treatment plan and/or one or more characteristics of the treatment device 3070 based on the treatment information and/or prognostic review. For example, a healthcare provider may review treatment information. Based on the review of the treatment information, the health care provider may compare the treatment information with the treatment plan being followed by the user.

The healthcare provider may compare: (i) expected information about the user while the user uses the treatment device to implement the treatment plan; predictions about users while performing

The expected information may include one or more of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, the user's blood pressure, other suitable information of the user, or combinations thereof. A health care provider may determine that a treatment regimen is providing the desired effect if the prediction falls within an acceptable range associated with one or more corresponding portions or portions of expected information. Alternatively, the health care provider determines that the treatment regimen is not providing the desired effect if the prediction falls outside the tolerance associated with one or more corresponding parts or portions of expected information. can.

For example, the health care provider should ensure that the blood pressure value indicated by the prediction (e.g., systolic, diastolic, and/or pulse pressure) is within an acceptable range of expected blood pressure values indicated by the prognostic information. (eg, plus or minus 1%, plus or minus 5%, plus or minus 1 unit of measurement (or other suitable number), or any suitable range). The health care provider may determine that the treatment regimen is having the desired effect if the blood pressure values are within the expected blood pressure value range. Alternatively, if the blood pressure value is outside the range of expected blood pressure values, it may be determined that the treatment regimen is not having the desired effect.

In some embodiments, while a user is using treatment device 3070 to implement a treatment plan, a healthcare provider may use the expected characteristics of treatment device 3070 as indicated by treatment information and/or predictions. The characteristics of device 3070 can be compared. For example, a healthcare provider may compare the expected resistance setting of the treatment device 3070 to the actual resistance setting of the treatment device 3070 indicated by treatment information and/or predictions. The healthcare provider determines that the user has successfully performed the treatment plan if the actual characteristics of the treatment device 3070 indicated by the treatment information are within the corresponding ones of the expected characteristics of the treatment device 3070. It can be determined that Alternatively, the healthcare provider may notify the user if the actual characteristics of the treatment device 3070 indicated by the treatment information and/or predictions are outside the range of the corresponding ones of the expected characteristics of the treatment device 3070. may determine that the patient is not implementing the treatment plan properly.

If the health care provider determines that the prognostic and/or treatment information indicates that the user is performing the treatment plan properly and/or that the treatment plan is providing the desired effect, the health care provider , may decide not to modify at least one treatment plan and/or one or more characteristics of the treatment device 3070 . Alternatively, while the user is performing a treatment plan using the treatment device 3070, the health care provider may determine if the prognostic and/or treatment information is not correct or that the user is not properly performing the treatment plan. and/or indicates that the treatment regimen is not or has not provided the desired effect, the health care provider may use at least one aspect of the treatment regimen and/or the treatment device. It may be determined to modify one or more characteristics of 3070 .

In some embodiments, the healthcare provider interacts with the interface when the healthcare provider determines to modify at least one aspect of the treatment regimen and/or modify one or more characteristics of the treatment device. may provide treatment plan input indicating one or more modifications to the treatment plan and/or modify one or more characteristics of treatment device 3070 . For example, a healthcare provider may use the interface to provide input indicating an increase or decrease in the resistance setting of therapy device 3070 or other suitable modification to one or more characteristics of therapy device 3070 . Additionally or alternatively, a healthcare provider may use the interface to provide input indicating modifications to the treatment plan. For example, the healthcare provider uses the interface to indicate an increase or decrease in the amount of time the user is required to use the treatment device 3070 according to the treatment plan, or other suitable modifications to the treatment plan. You may provide input.

In some embodiments, server 3030 modifies at least one aspect of the treatment plan and/or one or more characteristics of treatment device 3070 based on one or more modifications indicated by the treatment plan input.

In some embodiments, the server 3030 may receive subsequent treatment data regarding the user while the patient uses the treatment device 3070 to implement the modified treatment plan. For example, after a healthcare provider provides input to modify one or more characteristics of a treatment plan and/or treatment device 3070 and/or server 3030 uses artificial intelligence engine 3011 to generate a treatment plan and/or Alternatively, after modifying one or more characteristics of treatment device 3070, the user may continue to use treatment device 3070 to implement the modified treatment plan. Subsequent treatment data may correspond to treatment data generated while the user uses the treatment device 3070 to implement the modified treatment plan. In some embodiments, a healthcare provider receives treatment information and determines not to modify the treatment plan and/or one or more characteristics of treatment device 3070 and/or server 3030 determines that artificial intelligence engine 3011 is used to determine that the treatment plan and/or one or more characteristics of the treatment device 3070 are not to be modified, subsequent treatment data may be collected while the user continues to use the treatment device 3070 to implement the treatment plan. may correspond to treatment data generated in In some embodiments, subsequent therapy data may include updated therapy data (eg, therapy data updated to include at least one prediction).

In some embodiments, server 3030 may use artificial intelligence engine 3011 using machine learning model 3013 to generate one or more subsequent predictions based on subsequent treatment data. Server 3030 may determine whether each subsequent prediction falls within a corresponding threshold. The server 3030 may communicate subsequent treatment data, subsequent treatment information, and/or predictions to the healthcare provider's computing device in response to determining that each subsequent prediction is within the threshold range. . In some embodiments, server 3030 may modify at least one aspect of the treatment plan and/or one or more characteristics of treatment device 3070 based on subsequent predictions.

In some embodiments, the server 3030 may receive subsequent treatment plan input from a healthcare provider's computing device. Based on subsequent treatment plan input received from the healthcare provider's computing device, server 3030 may further modify the treatment plan and/or control one or more characteristics of treatment device 3070 . Subsequent treatment plan input corresponds to input provided by a healthcare provider at the interface in response to receiving and/or reviewing subsequent treatment information and/or subsequent predictions corresponding to subsequent treatment data can. It should be appreciated that server 3030 may use artificial intelligence engine 3011 to continuously and/or periodically generate predictions based on treatment data. Based on therapy data received on an ongoing and/or periodic basis from sensors or other suitable sources described herein, server 3030 may transmit therapy information and/or predictions to the healthcare provider's computing device. device. Additionally or alternatively, server 3030 may continuously and/or periodically monitor characteristics of the user while the user uses treatment device 3070 to implement a treatment plan.

In some embodiments, the healthcare provider and/or server 3030 may continuously or periodically transfer treatment information, treatment data, and/or Predictions may be received and/or reviewed. Based on one or more trends indicated by the treatment information, treatment data, and/or predictions, the healthcare provider and/or server 3030 determines whether to modify the treatment plan and/or It may be determined whether to modify and/or control any of the above characteristics. For example, the one or more trends may be an increase in heart rate or other favorable indication that the user is not performing the treatment plan properly and/or that the user's implementation of the treatment plan is not having the desired effect. can show a trend.

In some embodiments, the server 3030 may control one or more characteristics of the treatment device 3070 based on predictions while the user uses the treatment device 3070 to implement a treatment plan. For example, server 3030 may determine that the prediction is outside the corresponding threshold. Based on the prediction, server 3030 may identify one or more characteristics of therapeutic device 3070 . Server 3030 may communicate signals to controller 3072 of therapeutic device 3070 indicating modifications to one or more properties of therapeutic device 3070 . Based on the signal, controller 3072 may modify one or more properties of therapeutic device 3070 .

In some embodiments, the treatment plan, including the configuration, settings, range of motion settings, pain levels, force settings, speed settings, etc. of the treatment device 3070 for various movements, is sent to the controller of the treatment device 3070. can be transmitted. In one example, if a user provides an indication via patient interface 3050 that they are experiencing a high level of pain in a particular range of motion, the controller may receive the indication. Based on the indication, the controller adjusts the pedals inwardly, outwardly, or along or about any suitable axis via one or more actuators, hydraulics, springs, electric motors, etc. can electronically adjust the range of motion of the pedal 3102 by . The treatment plan may define alternative range-of-motion settings for the pedals 3102 if the user exhibits a particular pain level during exercise. Thus, once the treatment plan has been uploaded to the controller of the therapy device 3070, the therapy device 3070 can continue to operate without further instructions, further external inputs, or the like. Note that the patient (via patient interface 3050) and/or assistant (via assistant interface 3094) may override any configuration or settings of therapy device 3070 at any time. For example, the patient may use the patient interface 3050 to immediately deactivate the therapy device 3070 if desired.

FIG. 31 illustrates a method 3900 for monitoring characteristics of a user while the user is using a therapeutic device 3070 based on therapeutic data received while the user is using the therapeutic device 3070, according to principles of the present disclosure. FIG. 4 is a generally exemplary flow diagram; Method 3900 is performed by processing logic that may include hardware (circuitry, dedicated logic, etc.), software (such as running on a general purpose computer system or dedicated machine), or a combination of both. Method 3900 and/or each of its individual functions, routines, subroutines, or acts may be executed by any component of FIG. ) may be performed by In some embodiments, method 3900 may be performed by a single processing thread. Alternatively, method 3900 may be performed by two or more processing threads, each thread implementing one or more individual functions, routines, subroutines, or method operations.

For ease of explanation, method 3900 is depicted and described as a series of acts. However, operations in accordance with this disclosure may occur in various orders and/or concurrently and/or with other operations not presented and described herein. For example, the acts depicted in method 3900 may be performed in combination with any other acts of any other methods disclosed herein. Moreover, not all illustrated acts may be required to implement method 3900 in accordance with the disclosed subject matter. Additionally, those skilled in the art will understand and appreciate that the method 3900 can alternatively be represented as a series of interrelated states via a state diagram or events.

At 3902 , a processing device may receive treatment data regarding a user implementing a treatment plan using a treatment device, such as treatment device 3070 . The treatment data includes characteristics of the user, baseline measurement information about the user, measurement information about the user while the user is using the treatment device 3070 to perform a treatment plan, characteristics of the treatment device 3070, at least one aspect of the treatment plan; Other suitable data, or combinations thereof, may be included.

At 3904 , the processing device may write therapy data to associated memory for access by an artificial intelligence engine, such as artificial intelligence engine 3011 . Artificial intelligence engine 3011 may be configured to use at least one machine learning model, such as machine learning model 3013 . Machine learning model 3013 may be configured to generate at least one prediction using at least one aspect of the treatment data.

At least one prediction may indicate one or more predicted characteristics of the user. The one or more predicted characteristics of the user are the user's predicted vital signs, the user's predicted respiratory rate, the user's predicted heart rate, the user's predicted body temperature, the user's predicted blood pressure, It may include the predicted outcome of the treatment plan being administered by the user, the user's predicted injury resulting from the user performing the treatment plan, or other suitable predicted characteristics of the user.

At 3906 , the processing device may receive at least one prediction from artificial intelligence engine 3011 .

At 3908, the processing device may identify a threshold corresponding to at least one prediction. For example, the processing device may identify one or more characteristics of the user indicated by each prediction. The processing device may access a database, such as database 3044 or other suitable database, configured to associate thresholds with user characteristics and/or combinations of user characteristics. For example, database 3044 may include information relating a first threshold to the user's blood pressure. Additionally or alternatively, database 3044 may include information relating thresholds to the user's blood pressure and the user's heart rate. The threshold value corresponding to each prediction may include a value or range of values, including upper and lower bounds.

At 3910, the processing device, in response to determining that the at least one prediction is within a corresponding threshold range, communicates with an interface at the healthcare provider's computing device to provide at least one prediction and treatment data. can. For example, the processing device may compare at least one prediction and/or one or more characteristics of the user indicated by the prediction with corresponding thresholds identified by the processing device. If the processing device determines that the prediction is within the threshold range, the processing device may communicate at least one prediction and/or treatment data to the healthcare provider's computing device.

At 3912, the processing device may update therapy data associated with the user to indicate the at least one prediction in response to determining that the at least one prediction is outside the corresponding threshold. The processing device may store the updated therapy data in associated memory.

FIG. 32 illustrates a method 31000 for monitoring characteristics of a user while the user is using a therapeutic device 3070 based on therapeutic data received while the user is using the therapeutic device 3070, according to principles of the present disclosure. FIG. 4 is a generally exemplary flow diagram; Method 31000 includes acts performed by a processor of a computing device (eg, any component of FIG. 23, such as server 3030 running artificial intelligence engine 3011). In some embodiments, one or more operations of method 31000 are implemented in computer instructions stored on a memory device and executed by a processing device. Method 31000 may be performed in the same or similar manner as described above with respect to method 3900 . The acts of method 31000 may be performed in any combination with the acts of any of the methods described herein.

At 31002, the processing device may receive treatment data regarding a user implementing a treatment plan using a treatment device or apparatus, such as treatment device 3070, during a telemedicine session. The treatment data includes characteristics of the user, baseline measurement information about the user, measurement information about the user while the user is using the treatment device 3070 to perform a treatment plan, characteristics of the treatment device 3070, at least one aspect of the treatment plan; Other suitable data, or combinations thereof, may be included.

At 31004 , the processing device may write therapy data to associated memory for access by an artificial intelligence engine, such as artificial intelligence engine 3011 . Artificial intelligence engine 3011 may be configured to use at least one machine learning model, such as machine learning model 3013 . Machine learning model 3013 may be configured to generate at least one prediction using at least one aspect of the treatment data.

At least one prediction may indicate one or more predicted characteristics of the user. The one or more predicted characteristics of the user are the user's predicted vital signs, the user's predicted respiratory rate, the user's predicted heart rate, the user's predicted body temperature, the user's predicted blood pressure, It may include the predicted outcome of the treatment plan being administered by the user, the user's predicted injury resulting from the user performing the treatment plan, or other suitable predicted characteristics of the user.

At 31006 , the processing device may receive at least one prediction from artificial intelligence engine 11 .

At 31008, the processing device may identify a threshold corresponding to at least one prediction. For example, the processing device may identify one or more characteristics of the user indicated by each prediction. The processing device may access a database, such as database 3044 or other suitable database, configured to associate thresholds with user characteristics and/or combinations of user characteristics. For example, database 3044 may include information relating a first threshold to the user's blood pressure. Additionally or alternatively, database 3044 may include information relating thresholds to the user's blood pressure and the user's heart rate. The threshold value corresponding to each prediction may include a value or range of values, including upper and lower bounds.

At 31010, the processing device, in response to determining that the at least one prediction is within a corresponding threshold range, communicates with an interface at the healthcare provider's computing device to provide at least one prediction and treatment data. can. For example, the processing device may compare at least one prediction and/or one or more characteristics of the user indicated by the prediction with corresponding thresholds identified by the processing device. If the processing device determines that the prediction is within the threshold range, the processing device may communicate at least one prediction and/or treatment data to the healthcare provider's computing device.

At 31012, the processing device receives from the healthcare provider's computing device at least one treatment plan input indicative of at least one modification to at least one aspect of the treatment plan and any other aspect of the treatment plan. can receive.

At 31014, the processing device may use the treatment plan input to modify at least one aspect of the treatment plan and any other aspect of the treatment plan.

At 31016, the processing device modifies at least one of at least one aspect of the treatment plan or any other aspect of the treatment plan during the telemedicine session while the user is using the treatment device 3070. Based on this, the therapeutic device 3070 may be controlled.

FIG. 33 illustrates a method 31100 for monitoring characteristics of a user while the user is using the therapeutic device 3070 based on therapeutic data received while the user is using the therapeutic device 3070, according to principles of the present disclosure. FIG. 4 is a generally exemplary flow diagram; Method 31100 includes acts performed by a processor of a computing device (eg, any component of FIG. 23, such as server 3030 running artificial intelligence engine 3011). In some embodiments, one or more operations of method 31100 are implemented in computer instructions stored on a memory device and executed by a processing device. Method 31100 may be performed in the same or similar manner as described above with respect to method 3900 and/or method 31000. The acts of method 31100 may be performed in any combination with the acts of any of the methods described herein.

At 31102 , the processing device may receive treatment data regarding a user implementing a treatment plan using a treatment device, such as treatment device 3070 . The treatment data includes characteristics of the user, baseline measurement information about the user, measurement information about the user while the user is using the treatment device 3070 to perform a treatment plan, characteristics of the treatment device 3070, at least one aspect of the treatment plan; Other suitable data, or combinations thereof, may be included.

At 31104 , the processing device may write therapy data to associated memory for access by an artificial intelligence engine, such as artificial intelligence engine 3011 . Artificial intelligence engine 3011 may be configured to use at least one machine learning model, such as machine learning model 3013 . Machine learning model 3013 may be configured to generate at least one prediction using at least one aspect of the treatment data.

At least one prediction may indicate one or more predicted characteristics of the user. The one or more predicted characteristics of the user are the user's predicted vital signs, the user's predicted respiratory rate, the user's predicted heart rate, the user's predicted body temperature, the user's predicted blood pressure, It may include the predicted outcome of the treatment plan being administered by the user, the user's predicted injury resulting from the user performing the treatment plan, or other suitable predicted characteristics of the user.

At 31106 , the processing device may receive at least one prediction from artificial intelligence engine 3011 .

At 31108, the processing device may generate treatment information using at least one prediction. The treatment information may include a summary of the performance of the treatment plan and at least one prediction by the user while the user uses the treatment device 3070 to perform the treatment plan. The treatment information may be formatted such that the treatment data and/or at least one prediction is presentable at a computing device of a healthcare provider responsible for implementing the treatment plan by the user.

At 31110, the processing device outputs treatment information and/or at least one prediction for access by at least one of the healthcare provider's computing device and the machine learning model executed by the artificial intelligence engine 3011. can be written to the associated memory.

At 31112, the processing device may receive treatment plan input in response to the treatment information. The treatment plan input may indicate at least one aspect of the treatment plan and/or at least one modification to any other aspect of the treatment plan. In some embodiments, treatment plan input may be provided by a healthcare provider as described. In some embodiments, based on the treatment information, artificial intelligence engine 3011 executing machine learning model 3013 may generate treatment plan inputs.

At 31114, the processing device may determine whether the treatment plan input indicates at least one aspect of the treatment plan and/or at least one modification to any other aspect of the treatment plan.

If the processing device determines that the treatment plan input does not indicate at least one aspect of the treatment plan and/or at least one modification to any other aspect of the treatment plan, the processing device returns to 31102 and the user Continue to receive therapy data about the user while the therapy device 3070 is used to implement the therapy plan. If the processing device determines that the treatment plan input indicates at least one aspect of the treatment plan and/or at least one modification to any other aspect of the treatment plan, the processing device continues at 31116 .

At 31116, the processing device may selectively modify at least one aspect of the treatment plan and/or any other aspect of the treatment plan. For example, the processing device may determine whether the treatment data indicates that the treatment regimen is giving the desired effect. The processing device, in response to determining that the treatment plan does not provide the desired effect, extracts at least one of the treatment plans to attempt to achieve the desired effect, and if not possible, at least a portion of the desired effect. One aspect can be modified.

At 31118 , the processing device may control the treatment device 3070 while the user is using the treatment device 3070 based on the treatment plan and/or the modified treatment plan.

FIG. 34 illustrates an exemplary method 31200 for receiving an optimal treatment plan selection and controlling a treatment device based on the optimal treatment plan while a patient uses a treatment device according to the present disclosure. Embodiments are generally illustrated. Method 31200 includes acts performed by a processor of a computing device (eg, any component of FIG. 23, such as server 3030 running artificial intelligence engine 3011). In some embodiments, one or more operations of method 31200 are implemented in computer instructions stored on a memory device and executed by a processing device. Method 31200 may be performed in the same or similar manner as described above with respect to method 3900 . The acts of method 31200 may be performed in any combination with the acts of any of the methods described herein.

Various optimal treatment plans may be generated by one or more trained machine learning models 3013 of the artificial intelligence engine 3011 before the method 31200 is executed. For example, based on a set of treatment plans for a patient's medical condition, one or more trained machine learning models 3013 can generate an optimal treatment plan. Various treatment plans may be transmitted to one or more computing devices of the patient and/or medical personnel.

At 31202 of method 31200, the processing device may receive selected optimal treatment plans from some or all of the optimal treatment plans. Selections may be entered on a user interface that presents the optimal treatment plan on patient interface 3050 and/or assistant interface 3094 .

At 31204, the processing device may control the treatment device 3070 while the patient is using the treatment device 3070 based on the selected optimal treatment plan. In some embodiments, controlling may be performed remotely by server 3030 . For example, if the selection is made using the patient interface 3050, one or more control signals are transmitted from the patient interface 3050 to the treatment device 3070 to configure the settings of the treatment device 3070 according to the selected treatment plan. , may control the operation of the treatment device 3070 . Additionally, when a selection is made using the patient interface 3094, one or more control signals are transmitted from the assistant interface 3094 to the therapy device 3070 to configure the settings of the therapy device 3070 according to the selected treatment plan. , may control the operation of the treatment device 3070 .

Note that as the patient uses the therapy device 3070, the sensors 3076 may transmit measurement data to the processing device. The processing device may dynamically control the processing device 3070 according to the treatment plan by modifying the settings of the processing device 3070 based on sensor measurements. For example, if the force measured by sensor 3076 indicates that the user is not applying sufficient force to pedal 3102, a treatment plan may indicate reducing the amount of force required for the exercise.

Note that when the patient uses the treatment device 3070, the user may use the patient interface 3050 to enter input regarding the level of pain the patient experiences as the patient implements the treatment plan. For example, a user may input severe pain while pedaling with pedals 3102 set to a particular range of motion on treatment device 3070 . The pain level entered by the user may be within a range or level that may allow the range of motion to be dynamically adjusted based on the treatment plan. For example, a treatment plan may specify alternative range-of-motion settings if the user exhibits a particular pain level while performing exercises targeting a particular range of motion.

FIG. 35 generally illustrates an example computer system 31300 capable of performing any one or more of the methods described herein, in accordance with one or more aspects of the disclosure. In one example, the computer system 31300 includes a computing device and includes a support interface 3094, a reporting interface 3092, a supervisor interface 3090, a clinician interface 3020, a server 3030 (including AI engine 3011), a patient interface 3050, and a gait sensor 3082. , goniometer 3084, therapeutic device 3070, pressure sensor 3086, or any suitable component of FIG. Computer system 31300 may be capable of executing instructions implementing one or more machine learning models 3013 of artificial intelligence engine 3011 of FIG. The computer system may be connected (eg, networked) to other computer systems within a LAN, intranet, extranet, or Internet, including via a cloud or peer-to-peer network.

A computer system can operate in the capacity of a server in a client-server network environment. Computer systems include personal computers (PCs), tablet computers, wearables (e.g., wristbands), set-top boxes (STBs), personal digital assistants (PDAs), mobile phones, cameras, camcorders, Internet of Things (IoT) devices. , or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, although only a single computer system is illustrated, the term "computer" can also refer to a set of instructions (or sets of instructions) for performing any one or more of the methods discussed herein. ), individually or jointly, to include any collection of computers.

The computer system 31300 includes a processing device 31302, a main memory 31304 (e.g., dynamic random memory such as read only memory (ROM), flash memory, solid state drive (SSD), synchronous DRAM (SDRAM), etc.) in communication with each other via bus 31310. access memory (DRAM)), static memory 31306 (eg, flash memory, solid state drive (SSD), static random access memory (SRAM)), and data storage devices 31308 .

Processing device 31302 represents one or more general purpose processing devices such as microprocessors, central processing units, and the like. More specifically, the processing device 31302 implements a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or other instruction set. , or a processor that implements a combination of instruction sets. Processing device 31302 may also be one or more dedicated processing devices such as an application specific integrated circuit (ASIC), system-on-chip, field programmable gate array (FPGA), digital signal processor (DSP), network processor, etc. may Processing device 31302 is configured to execute instructions to perform any of the operations and steps discussed herein.

Computer system 31300 may further include network interface device 31312 . Computer system 31300 also includes a video display 31314 (e.g., liquid crystal display (LCD), light emitting diode (LED), organic light emitting diode (OLED), quantum LED, cathode ray tube (CRT), shadow mask CRT, aperture grill CRT, monochrome CRT), one or more input devices 31316 (eg, keyboard and/or mouse, or game-like controls), and one or more speakers 31318 (eg, speakers). In one illustrative example, video display 31314 and input device 31316 may be combined into a single component or device (eg, an LCD touchscreen).

Data storage device 31316 may include computer readable media 31320 having instructions 31322 stored thereon that embody any one or more of the methods, acts, or functions described herein. Instructions 31322 may also reside completely or at least partially within main memory 31304 and/or processing device 31302 during execution of instructions 31322 by computer system 31300 . Thus, main memory 31304 and processing device 31302 also constitute computer-readable media. Instructions 31322 may also be transmitted or received over a network via network interface device 31312 .

Although computer-readable storage medium 31320 is generally illustrated in illustrative examples as being a single medium, the term "computer-readable storage medium" refers to a single unit that stores one or more sets of instructions. It should be understood to include a medium or multiple mediums (eg, centralized or distributed databases and/or associated caches and servers). The term "computer-readable storage medium" also means any medium capable of storing, encoding or carrying a set of instructions for execution by a machine and causing the machine to perform any one or more of the methodologies of the present disclosure. shall be construed to include the medium of Accordingly, the term "computer-readable storage medium" shall be taken to include, but not be limited to, solid state memory, optical media, and magnetic media.

Article 45. A method for providing, by an artificial intelligence engine, an optimal treatment plan for use with a treatment device, comprising:

Receiving from a data source clinical information about the results of implementing a particular treatment regimen for people with particular characteristics using a therapy device, the clinical information having a first data format , receiving and

converting a portion of the clinical information from a first data format into a medical descriptive language for use by an artificial intelligence engine;

Determining, based on the portion of clinical information described by the medical description language and a plurality of characteristics about the patient, an optimal treatment plan to be followed by the patient using the treatment device to achieve a desired outcome. ,

providing an optimal treatment plan for presentation on a healthcare professional's computing device.

Article 46. converting the portion of clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
interpreting clinical information;
identifying portions of the clinical information having values associated with the target information based on keywords representing the target information in the clinical information;
Generating a canonical form defined by a medical description language, the canonical form including a tag identifying a value of the target information. described method.

Article 47. A method as in any clause herein, wherein a tag is an attribute that describes a particular characteristic of the target information.

Article 48. Providing the optimal treatment plan as presented on the healthcare worker's computing device
Further comprising causing the optimal treatment plan to be presented on a user interface of the healthcare professional's computing device during the telemedicine session, wherein the optimal treatment plan is presented on a display screen of the computing device. A method according to any clause herein, wherein such display screen is configured for use by a patient during a telemedicine session.

Article 49. Excluded treatments that the patient should not be recommended to follow when using the treatment device to achieve desired results based on the portion of the clinical information described by the medical descriptive language and multiple characteristics about the patient deciding on a plan;
The method according to any clause herein, further comprising providing the excluded treatment plan as presented on a healthcare professional's computing device.

Clause 50. A second optimal treatment to follow when the patient uses the treatment device to achieve a second desired result based on the portion of the clinical information described by the medical description language and a plurality of characteristics about the patient. determining a plan, wherein a desired outcome is associated with recovery outcome and a second desired outcome is associated with recovery time;
providing a second optimal treatment plan for presentation on a healthcare professional's computing device;
receiving a selected treatment plan, either the optimal treatment plan or a second optimal treatment plan;
transmitting the selected treatment plan to the patient's computing device for presentation on a user interface of the patient's computing device.

Article 51. A desired result includes obtaining a particular result within a particular time period, the particular result being
the range of motion the patient achieves using the therapy device;
the amount of force the patient exerts on a portion of the therapy device;
the amount of time the patient exercises using the therapy device;
A method according to any clause herein comprising the distance the patient travels using the treatment device, or any combination thereof.

Article 52. The specific characteristics of the people are the first drug prescribed to the people, the first injury of the people, the first medical procedure performed on the people, the first measurement of the people, the first allergies, people's first medical condition, people's first historical information, people's first vital signs, people's first symptoms, people's first family medical information, people's first population demographic information, first geographic information of people, first measurement-based or test-based information of people, first medical history information of people, first etiological information of people, first cohort of people Related information, people's first differential diagnostic information, people's first surgical information, people's first physiotherapy information, people's first pharmacological information, people's first recommended others treatment of, or any combination thereof,
The plurality of characteristics of the patient includes: a second drug of the patient; a second injury of the patient; a second medical procedure performed on the patient; a second measurement of the patient; a second allergy of the patient; a second medical condition of the patient; a second historical information of the patient; a second vital sign of the patient; a second symptom of the patient; a second family medical information of the patient; second geographic information of the patient; second measurement-based or test-based information of the patient; second historical medical information of the patient; second etiological information of the patient; second cohort-related information of the patient; patient second differential diagnostic information; patient second surgical information; patient second physiotherapy information; patient second pharmacological information; patient second other treatment recommended; or any combination thereof.

Article 53. Clinical information is written by a person with specific professional qualifications and includes journal articles, clinical trials, evidence-based guidelines, meta-analyses, or any combination thereof, as described in any of the methods herein. .

Article 54. Based on a portion of the clinical information described by the medical description language and a plurality of characteristics about the patient, determining an optimal treatment plan to be followed by the patient using the treatment device to achieve a desired outcome. ,
Matching patterns between portions of clinical information described by a medical description language to a plurality of characteristics of a patient, where the patterns are associated with optimal treatment plans leading to desired outcomes. A method according to any clause herein, further comprising.

Article 55. The optimal treatment plan is
a medical procedure performed on a patient;
a treatment protocol for a patient using a treatment device;
the patient's dietary regimen,
the patient's drug regimen,
A method according to any section herein, comprising the patient's sleep regimen, or any combination thereof.

Article 56. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
Receiving from a data source clinical information about the results of implementing a particular treatment regimen for people with particular characteristics using a therapy device, the clinical information having a first data format , receiving and
converting a portion of the clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
Determining, based on a portion of the clinical information described by the medical description language and a plurality of characteristics about the patient, an optimal treatment plan to be followed by the patient using the treatment device to achieve a desired outcome. ,
A computer readable medium for providing and causing an optimal treatment plan to be presented on a healthcare professional's computing device.

Article 57. converting the portion of clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
interpreting clinical information;
identifying portions of the clinical information having target information values based on keywords representing the target information in the clinical information;
Generating a canonical form defined by a medical description language, the canonical form including a tag identifying a value of the target information. A computer readable medium as described.

Article 58. Providing the optimal treatment plan as presented on the healthcare worker's computing device
during the telemedicine session, causing the optimal treatment plan to be presented on a user interface of the healthcare professional's computing device; A computer-readable medium as described in any section herein that is not presented on a user interface of the device.

Article 59. The processing device
A second optimal treatment to follow when the patient uses the treatment device to achieve a second desired result based on the portion of the clinical information described by the medical description language and a plurality of characteristics about the patient. determining a plan, wherein a desired outcome is associated with recovery outcome and a second desired outcome is associated with recovery time;
providing a second optimal treatment plan for presentation on a healthcare professional's computing device;
receiving a selected treatment plan, either the optimal treatment plan or a second optimal treatment plan;
and transmitting the selected treatment plan to the patient's computing device.

Article 60. A desired result includes obtaining a particular result within a particular time period, the particular result being
the range of motion the patient achieves using the therapy device;
the amount of force the patient exerts on a portion of the therapy device;
the amount of time the patient exercises using the therapy device;
A computer readable medium according to any clause herein, comprising: a distance traveled by a patient using a therapy device, or any combination thereof.

Article 61. The specific characteristics of the people are the first drug prescribed to the people, the first injury of the people, the first medical procedure performed on the people, the first measurement of the people, the first allergies, people's first medical condition, people's first historical information, people's first vital signs, people's first symptoms, people's first family medical information, people's first population demographic information, first geographic information of people, first measurement-based or test-based information of people, first medical history information of people, first etiological information of people, first cohort of people Related information, people's first differential diagnostic information, people's first surgical information, people's first physiotherapy information, people's first pharmacological information, people's first recommended others treatment of, or any combination thereof,
The plurality of characteristics of the patient includes: a second drug of the patient; a second injury of the patient; a second medical procedure performed on the patient; a second measurement of the patient; a second allergy of the patient; a second medical condition of the patient; a second historical information of the patient; a second vital sign of the patient; a second symptom of the patient; a second family medical information of the patient; second geographic information of the patient; second measurement-based or test-based information of the patient; second historical medical information of the patient; second etiological information of the patient; second cohort-related information of the patient; patient second differential diagnostic information; patient second surgical information; patient second physiotherapy information; patient second pharmacological information; patient second other treatment recommended; or any combination thereof.

Article 62. The clinical information is written by a person with a particular professional qualification and includes journal articles, clinical trials, evidence-based guidelines, or any combination thereof, computer readable media as described in any section of this specification.

Article 63. a system,
a memory device storing instructions;
a processing device communicatively coupled to the memory device, the processing device executing the instructions to
Receiving from a data source clinical information about the results of implementing a particular treatment regimen for people with particular characteristics using a therapy device, the clinical information having a first data format , receiving and
converting a portion of the clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
Determining an optimal treatment plan to follow when a patient uses a treatment device to achieve a desired outcome based on a portion of the clinical information described by the medical description language and a plurality of characteristics about the patient. ,
A system that: and provides an optimal treatment plan for presentation on a healthcare professional's computing device.

Article 64. converting the portion of clinical information from the first data format into a medical descriptive language for use by the artificial intelligence engine;
interpreting clinical information;
identifying portions of the clinical information having target information values based on keywords representing the target information described by the clinical information;
Generating a canonical form defined by a medical description language, the canonical form including a tag identifying a value of the target information. System as described.

Methods and systems for using a virtual avatar associated with a healthcare professional during an exercise session. Trial-based, medical history, etiological, cohort-related, differential diagnosis, surgical, physiotherapeutic, pharmacological, and other recommended treatments, arterial blood gas and/or oxygenation levels or rates, psychology, etc. ) can be a technically challenging problem. For example, a large amount of information can be considered when determining a treatment plan, which can lead to inefficiencies and inaccuracies in the treatment plan selection process. In a rehabilitation setting, some of the many pieces of information considered may include patient characteristics, such as personal information, performance information, and measurement information. Personal information may include, for example, demographic, psychological scientific or other information. The performance information may include, for example, the elapsed time using the treatment device, the amount of force exerted on the portion of the treatment device, the range of motion achieved with the treatment device, the speed of movement of the portion of the treatment device, the number of pains using the treatment device. It may include indications of levels, or some combination thereof. Measurement information may include, for example, vital signs, respiratory rate, heart rate, body temperature, blood pressure, arterial blood gases and/or oxygenation levels or rates, glucose levels or levels of other biomarkers, or some combination thereof. It may be desirable to process the characteristics of a large number of patients, the treatment plans to be administered to those patients, and the results of the treatment plans for those patients.

Furthermore, another technical problem may involve remotely treating a patient via a computing device during a telemedicine or telehealth session from a location different from where the patient is located. An additional technical problem is to control or allow control of the therapy device used by the patient at the location where the patient is located from different locations. In many cases, when a patient undergoes rehabilitation surgery (e.g., knee surgery), a physical therapist or other health care professional will prescribe a treatment protocol at the patient's home or any mobile or temporary residence. The patient may be prescribed a therapy device to use to perform. Health care providers can refer to doctors, physician assistants, nurses, chiropractors, dentists, physical therapists, acupuncturists, physical trainers, and the like. A medical practitioner may refer to any person having a qualification, license, position, etc. in the field of medicine, physical therapy, rehabilitation, or the like.

The physical therapist or other health care professional uses the therapy device to monitor the patient's actual progress, as the physical therapist or other health care worker is located in a different location than the patient and the therapy device. modifying the treatment plan according to the patient's progress (as opposed to relying on the patient's words about the patient's progress), adapting the treatment device to the patient's personal characteristics as the patient implements the treatment plan It can be technically difficult to do so.

Accordingly, some embodiments of the present disclosure relate to using artificial intelligence and/or machine learning to dynamically control therapy devices based on assignments during adaptive telemedicine sessions. In some embodiments, multiple treatment devices may be provided to the patient. The therapy device may be used by the patient to carry out the treatment plan at any suitable location, including the patient's home, gym, rehabilitation center, hospital, work site, or permanent or temporary residence. In some embodiments, the therapy device may be communicatively coupled to the server. Patient characteristics may be collected before, during, and/or after the patient implements a treatment plan. For example, personal information, performance information, and measurement information may be collected before, during, and/or after a person performs a treatment plan. The results of performing each exercise (eg, improved performance or reduced performance) can be collected from the treatment device throughout the treatment plan and after the treatment plan has been delivered. Treatment device parameters, settings, configurations, etc. (eg, pedal position, amount of resistance, etc.) may be collected before, during, and/or after the treatment plan is implemented.

Each characteristic of the patient, each result, and each parameter, setting, configuration, etc. may be time-stamped and correlated with a particular step of the treatment plan. Such techniques can help determine which steps in the treatment plan lead to the desired outcome (e.g., improvement in strength, range of motion, etc.) and which steps lead to reduced recovery (e.g., continue exercising after 3 minutes). may allow one to determine whether a condition actually slows or impairs recovery.

Data may be collected over time by the treatment device and/or any suitable computing device (e.g., clinician interface or patient interface, etc.) as the patient performs various treatment regimens using the treatment device. may be collected from the computing device on which the Data that may be collected may include patient characteristics, treatment plans administered by the patient, and results of treatment plans.

In some embodiments, data may be processed to group specific people into cohorts. People may be grouped by people who have certain or selected similar characteristics, treatment regimens, and results of implementing treatment regimens. For example, athletes without a medical condition who implement a treatment regimen (e.g., use the treatment device for 30 minutes a day, five times a week for three weeks) and who fully recover may receive first can be grouped into cohorts of Older adults classified as obese and following a treatment regimen (e.g., using a treatment regimen of 10 minutes a day three times a week for four weeks) and improving their range of motion by 75 percent were second Can be grouped into cohorts.

In some embodiments, an artificial intelligence engine may include one or more machine learning models trained using cohorts. For example, one or more machine learning models can be trained to receive input of new patient characteristics and to output patient treatment plans that produce desired results. A machine learning model may match patterns between the new patient's characteristics and at least one patient of the patients included in a particular cohort. When patterns are matched, a machine learning model can assign new patients to specific cohorts and select treatment plans associated with at least one patient. The artificial intelligence engine may be configured to remotely control the treatment device based on the treatment plan while the new patient uses the treatment device to implement the treatment plan.

As can be appreciated, the characteristics of a new patient may change as the new patient uses the treatment device to implement a treatment plan. For example, patient performance may improve faster than expected for those within the cohort to which new patients are currently assigned. Thus, a machine learning model can be trained to dynamically reassign new patients to different cohorts containing people with similar characteristics to the currently modified characteristics as new patients, based on the modified characteristics. . For example, a clinically obese patient may lose weight and no longer meet the weight criteria of the initial cohort, resulting in the patient's weight being reassigned to different cohorts with different weight criteria. A different treatment plan may be selected for the new patient, the treatment device may be remotely controlled based on the different treatment plan, and the treatment device while the new patient uses the treatment device to perform the treatment plan. Such technology may provide a technical solution to remotely control the treatment device. In addition, these techniques provide faster recovery times and/or better patient outcomes because the treatment plan that most accurately matches the patient's characteristics is selected and implemented in real-time at any given time. can lead to results. Real time may refer to 2 seconds or less. Near real-time can refer to any interaction of a time sufficiently short to allow two individuals to engage in interaction via such a user interface, generally less than 10 seconds, but will exceed 2 seconds. As described herein, the term "outcome" can refer to a medical result or medical outcome. Outcomes and outcomes can refer to responses to medical intervention.

In some embodiments, the treatment plan may be presented to a healthcare professional during a telemedicine or telehealth session. A medical practitioner may select a particular treatment plan, have the patient transmit the treatment plan to the patient, and/or control the treatment device based on the treatment plan. In some embodiments, the artificial intelligence engine is remote from the patient and treatment device to facilitate telehealth or telemedicine applications, including remote diagnosis, treatment plan determination, and rehabilitation and/or pharmacological prescription. may receive and/or operate on. In such cases, the recommended treatment plan and/or the excluded treatment plan are displayed in real-time or near-real-time on the patient's video during a telemedicine or telehealth session on the user interface of the healthcare professional's computing device. can be presented at the same time. Videos may also be accompanied by audio, text, and other multimedia information.

Presenting the treatment plan generated by the artificial intelligence engine at the same time as presenting the patient video allows the healthcare professional to visually and/or otherwise communicate with the patient while also reviewing the treatment plan on the same user interface. can provide an enhanced user interface. An enhanced user interface may improve the experience of healthcare professionals using the computing device and may encourage healthcare professionals to reuse user interfaces. Such techniques also benefit from computing resources (e.g., processing , memory, network). The artificial intelligence engine dynamically provides treatment plans and excluded treatment plans on the fly.

In some embodiments, the treatment regimen may be modified by a medical professional. For example, certain treatments may be added, modified, or removed. In telehealth scenarios, there are certain procedures that may not be performed due to the remote nature of medical personnel using computing devices at different physical locations than the patient.

A potential technical problem may relate to receiving information about a patient's medical condition in disparate formats. For example, the server may retrieve a patient's medical Status information may be received. That is, some sources used by various healthcare practitioner entities may be installed on the local computing devices of those entities and may additionally and/or alternatively use proprietary formats. Accordingly, some embodiments of the present disclosure may use an API to obtain the format used by the source via interfaces exposed by the API used by the source. In some embodiments, when information is received from a source, the API converts the format used by the source into a standardized (i.e., canonical) format, language and/or encoding used by the artificial intelligence engine. ("format" as used herein will encompass all of these terms). Further, the information converted to the standardized form used by the artificial intelligence engine is accessed by the artificial intelligence engine when it is performing any of the techniques disclosed herein. can be stored in a database. Using the information transformed into a standardized form may allow for more accurate determination of the treatment to perform for the patient and/or the billing sequence to use for the patient.

Various embodiments disclosed herein may provide technical solutions to technical problems related to patient medical condition information being received in disparate formats. For example, the server may retrieve a patient's medical Status information may be received. Information may be transformed from the format used by the source into a standardized format used by the artificial intelligence engine. Further, the information converted to the standardized form used by the artificial intelligence engine may be stored in a database accessed by the artificial intelligence engine when implementing any of the techniques disclosed herein. good. The standardized information may enable optimal treatment plans to be generated, based on treatment plans associated with the standardized information, the amount of monetary value, and the set of constraints. The optimal treatment plan can be provided in a standardized format that can be processed by various applications (eg, telehealth) running on various computing devices of healthcare professionals and/or patients.

One technical problem may include the challenge of allowing a single medical practitioner to treat multiple patients simultaneously. One technical solution to this technical problem may involve the apportionment of powers of at least one health care worker or group of health care workers so that one health care worker may Participating, different healthcare professionals may participate at different times. As used herein, the term "single health care worker" (or "one health care worker" or equivalent) shall be deemed to encompass all of the scenarios listed above. and For example, in group therapy or recovery sessions, it may be desirable for a single medical practitioner to see, monitor, treat, manage, diagnose, etc. two or more patients simultaneously from remote locations. Accordingly, in some embodiments of the present disclosure, a virtual avatar is used to guide each patient through the exercise sessions of the treatment plan. Healthcare professionals may use computing devices to view, monitor, treat, manage, and diagnose patients all at once or close in time. When a triggering event occurs, such as the user indicating that he is in a significant amount of pain, a telemedicine session is initiated, either selectively or electronically. The telemedicine session replaces the virtual avatar on the patient's computing device with a multimedia feed from the healthcare worker's computing device. In some embodiments, the medical practitioner may include, as needed (e.g., medical The practitioner may choose to intervene and/or discontinue this when the practitioner determines the sensor measurements are undesirable, the patient is not performing as needed, etc.), while other patients may choose to Follow the avatar to continue conducting exercise sessions.

In some embodiments, the therapy device may be adaptive and/or individualized, as the properties, configuration, and location of the therapy device may be adapted to the needs of a particular patient. For example, the pedal may be used to increase or decrease range of motion to comply with a treatment plan designed for the user (e.g., via a telemedicine session or upon detection of certain measurements). In response, it can be dynamically adjusted on the fly (based on programmed configuration). In some embodiments, the medical practitioner may remotely adapt the therapy device to the patient's needs by having control instructions transmitted from the server to the therapy device during the telemedicine session. Such adaptive properties may improve patient recovery outcomes, further advance the goals of personalized medicine, and allow individualization of treatment regimens on an individual basis.

FIG. 36 shows a block diagram of a computer-implemented system 10 for managing treatment plans, hereinafter referred to as the "system." Managing treatment plans may include using an artificial intelligence engine to recommend treatment plans and/or provide excluded treatment plans that should not be recommended to the patient.

System 4010 also includes a server 4030 configured to store and provide data related to managing treatment plans. Server 4030 may include one or more computers and may take the form of one or more distributed and/or virtualized computers. Server 4030 also includes a first communication interface 4032 configured to communicate with clinician interface 4020 via first network 4034 . In some embodiments, first network 4034 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like. The server 4030 includes a first processor 4036 and a first machine-readable storage memory 4038, which may be abbreviated as "memory" and implemented by the first processor 4036. holds first instructions 4040 for performing various actions of the server 30 for Server 4030 is configured to store data relating to treatment plans. For example, memory 4038 includes a system data store 4042 configured to hold system data, such as data regarding treatment plans for treating one or more patients.

System data store 4042 may be configured to hold data related to billing procedures, including rules and restrictions relating to billing codes, orders, timing, insurance schemes, laws, regulations, or some combination thereof. System data store 4042 stores claims sequences and various parameters (e.g., amount of monetary value generated, patient outcomes, reimbursement plans, fees, payment plans for amounts owed by patients, payments to insurance providers). amount of revenue, etc.). System data store 4042 is configured to store optimal treatment plans generated based on different treatment plans for users with similar medical conditions, the amount of monetary value generated by the treatment plans, and constraints. can be Any of the data stored in system data store 4042 may be accessed by artificial intelligence engine 4011 when performing any of the techniques described herein.

Server 4030 is also configured to store data relating to patient performance in accordance with the treatment plan. For example, memory 4038 includes a patient data store 4044 configured to hold patient data, such as data relating to one or more patients, including data representing each patient's performance within a treatment plan.

In addition, characteristics of people (e.g., personal, practice, measurements, etc.), treatment plans followed by people, levels of compliance with treatment plans, and outcomes of treatment plans may be correlated and other statistical or probabilistic measures. may be used to divide treatment plans into databases corresponding to different patient cohorts in the patient data store 4044, or to divide treatment plans into this database. For example, a first similar injury, a first similar medical condition, a first similar medical procedure performed, a first treatment regimen followed by a first patient, and a first outcome of the treatment regimen. Data relating to the first cohort of the first patient having can be stored in the first patient database. a second similar injury, a second similar medical condition, a second similar medical procedure performed, a second treatment regimen followed by the second patient, and a second outcome of the treatment regimen; Data for a second cohort of a second patient can be stored in a second patient database. Any single characteristic or any combination of characteristics may be used to segregate cohorts of patients. In some embodiments, 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, except as limited by mathematical combinations and/or partitioning theory.

This characteristic data, treatment plan data, and outcome data may be obtained over time from multiple therapy devices and/or computing devices and stored in a patient data store. Characteristic data, treatment plan data, and outcome data may be correlated in a patient-cohort database within patient data store 4044 . People characteristics may include personal information, performance information, and/or measurement information.

In addition to historical information about other people stored in patient cohort equivalent databases, real-time or near real-time information based on current patient characteristics about current patients being treated is stored in appropriate patient cohort equivalent databases. can be stored. A patient's characteristics may be determined to match or resemble characteristics of another person in a particular cohort (eg, Cohort A), and the patient may be assigned to that cohort.

In some embodiments, the server 4030 uses one or more machine learning models 4013 to run an artificial intelligence (AI) engine 4011 that implements at least one of the embodiments disclosed herein. can be implemented. Server 4030 may include training engine 409 capable of generating one or more machine learning models 4013 . Machine learning model 4013 assigns people to specific cohorts based on their characteristics, selects treatment plans using real-time and historical data correlation with patient cohort counterparts, and treats devices 4070, among other things. can be trained to control The machine learning model 4013 may generate a billing sequence and/or various parameters (e.g., fees owed to healthcare workers, patient payment plans to pay off amounts owed, reimbursement plans, insurance It can be trained to generate a treatment plan tailored to the amount of revenue to be paid to the provider, or some combination thereof. A machine learning model 4013 can be trained to generate optimal treatment plans tailored to various parameters (eg, amount of monetary value generated, patient outcome, risk, etc.) based on constraints. One or more machine learning models 4013 may be generated by training engine 9 and implemented in computer instructions executable by one or more processing devices of training engine 409 and/or server 4030 . Training engine 409 may train one or more machine learning models 4013 to generate one or more machine learning models 4013 . One or more machine learning models 4013 may be used by artificial intelligence engine 4011 .

The training engine 409 can be a rackmount server, router computer, personal computer, portable digital assistant, smart phone, laptop computer, tablet computer, netbook, desktop computer, Internet of Things (IoT) device, or any other desired computing device. device, or any combination of the above. Training engine 409 may be a cloud-based or real-time software platform and may include privacy software or protocols and/or security software or protocols.

To train one or more machine learning models 4013, the training engine 409 compiles a corpus of information (e.g., characteristics, medical diagnostic codes, etc.) about the medical conditions of people who use the treatment devices 4070 to implement treatment plans. training data set, details of treatment plans to be performed by people using treatment device 4070 (e.g., exercise, amount of time to perform exercise, instructions to be followed by patient, frequency to perform exercise, schedule of exercise, treatment treatment protocol (including parameters/configurations/settings, etc. of the treatment device 4070 throughout each step of the plan), results of the treatment plan performed by the person, a set of monetary value quantities associated with the treatment plan, constraints (e.g., rules for billing codes associated with sets of treatment plans, laws, regulations, etc.), sets of billing procedures (e.g., rules for billing codes, orders, timing, and constraints), sets of parameters (e.g., Fees owed to healthcare providers, patient payment plans to pay off amounts owed, reimbursement plans, amounts of revenues owed to insurance providers, or any combination thereof, treatment plans, money generated value, risk, etc.), insurance regimen, etc. may be used.

One or more machine learning models 4013 can be trained to match patterns of patient characteristics with characteristics of other people assigned to a particular cohort. The term "match" can refer to an exact match, a relative match, a substantial match, and the like. One or more machine learning models 4013 can be trained to receive patient characteristics as input, map the characteristics to characteristics of people assigned to a cohort, and select treatment plans from that cohort. One or more machine learning models 4013 may also be trained to control the machine learning device 4070 based on the treatment plan.

The one or more machine learning models 4013 may be configured based on a first set of parameters (e.g., a treatment plan for a patient with a medical condition, a set of amounts of monetary value associated with the treatment plan, patient outcomes, and/or A set of constraints) can be trained to match a pattern of constraints against a second set of parameters associated with an optimal treatment plan. One or more machine learning models 4013 receive as input the first set of parameters and map the characteristics to a second set of parameters associated with the optimal treatment plan, the optimal treatment plan as the treatment plan. It can be trained to choose. One or more machine learning models 4013 may also be trained to control the machine learning device 4070 based on the treatment plan.

The one or more machine learning models 4013 may use a first set of parameters (eg, information about a medical condition, a treatment plan for a patient with the medical condition, a set of amounts of monetary value associated with the treatment plan, a patient outcomes, instructions to be followed by the patient in the treatment plan, a set of billing procedures associated with the instructions, and/or a set of constraints)) as a second parameter associated with the claim sequence and/or the optimal treatment plan. can be trained to match against a set of One or more machine learning models 4013 receive as input a first set of parameters and translate the first set of parameters into a second set of parameters associated with a claim sequence and/or an optimal treatment plan. It may be mapped or otherwise correlated or algorithmically correlated and trained to select the patient's billing sequence and/or optimal treatment plan. In some embodiments, one or more optimal treatment plans may be selected to be provided to the healthcare professional and/or the patient's computing device. One or more machine learning models 4013 may also be trained to control the machine learning device 4070 based on the treatment plan.

Different machine learning models 4013 can be trained to recommend different treatment plans for different desired outcomes. For example, one machine learning model may be trained to recommend a treatment plan with the greatest amount of monetary value generated, while another machine learning model may be trained based on patient outcomes or the amount of monetary value generated. It can be trained to recommend treatment regimens based on any combination of dose and patient outcome, or based on these and/or additional goals. Also, different machine learning models 4013 may be trained to recommend different billing sequences tailored to different parameters. For example, one machine learning model may be trained to recommend a billing sequence for the maximum fee to be paid to a healthcare worker, while another machine learning model may be trained to recommend a billing sequence based on a reimbursement plan. may be trained to

Using training data, including training inputs and corresponding target outputs, one or more machine learning models 4013 may reference model artifacts created by training engine 409 . The training engine 409 can find patterns in the training data that map training inputs to target outputs and produce a machine learning model 4013 that incorporates these patterns. In some embodiments, artificial intelligence engine 4011, database, and/or training engine 409 may reside in separate components depicted in FIG. 36 (eg, assistant interface 4094, clinician interface 4020, etc.).

One or more machine learning models 4013 may include, for example, single-level linear or non-linear operations (e.g., support vector machines (SVMs)), and machine learning models 4013 may be deep networks, i.e., multi-level It may be a machine learning model including 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 is the neuron's output signal to the inputs of the remaining neurons and to itself). For example, a machine learning model may include multiple layers and/or hidden layers that perform computations (eg, dot products) using various neurons.

System 4010 also includes a patient interface 4050 configured to communicate information to the patient and receive feedback from the patient. Specifically, the patient interface includes an input device 4052 and an output device 4054, which may be collectively referred to as patient user interfaces 4052,4054. Input devices 4052 may include one or more devices such as a keyboard, mouse, touch screen input, gesture sensors, and/or microphones and processors configured for speech recognition. Output device 4054 may take one or more different forms, including, for example, a computer monitor or display screen on a tablet, smartphone, or smartwatch. Output device 4054 may include other hardware and/or software components, such as projectors, virtual reality capabilities, augmented reality capabilities, and the like. Output device 4054 may incorporate a variety of different visual, audio, or other presentation technologies. For example, the output device 4054 may provide non-visual displays such as audio signals that may include speech and/or other sounds such as tones, chimes, and/or melodies that may signal various conditions and/or directions. can contain. 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 computer programs or applications (apps).

In some embodiments, the output device 4054 may present a user interface that may present the patient with recommended treatment plans, billing sequences, and the like. A user interface may include one or more graphical elements that allow a user to select which treatment plan to administer. In response to receiving selection of a graphical element (eg, a “start” button) associated with a treatment plan via input device 4054 , patient interface 4050 communicates control signals to controller 4072 of therapy device 4070 . The control signal then causes the therapy device 4070 to begin executing the selected treatment plan. As described below, the control signal causes actuation of the actuator 4078 (e.g., causes the motor to drive the rotation of the therapy device's pedals at a constant rate) based on the selected treatment regimen; The therapy device 4070 may be controlled, such as by having measurements obtained via the sensor 4076 . Patient interface 4050 may communicate control signals to therapy device 4070 via local communication interface 4068 .

As shown in FIG. 36, patient interface 4050 is a second communication interface, also referred to as a remote communication interface, configured to communicate with server 4030 and/or clinician interface 4020 via second network 4058. 4056 included. In some embodiments, second network 58 may include a local area network (LAN), such as an Ethernet network. In some embodiments, second network 4058 may include the Internet, and communication between patient interface 4050 and server 4030 and/or clinician interface 4020 uses, for example, a virtual private network (VPN). It may be secured, such as possibly through encryption. In some embodiments, the second network 4058 may include wired and/or wireless network connections, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like. In some embodiments, second network 4058 may be the same as first network 4034 and/or may be operatively coupled to first network 4034 .

The patient interface 4050 includes a second processor 4060 and a second machine-readable storage memory 4062 that retains second instructions 64 for execution by the second processor 4060 to perform various actions of the patient interface 4050. and including. The second machine-readable storage memory 4062 also includes a local data store 66 configured to hold patient data, such as data relating to treatment plans and/or data representative of patient performance within treatment plans. . 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. Local communication interface 4068 may include wired and/or wireless communications. In some embodiments, local communication interface 4068 may include local wireless networks, such as Wi-Fi, Bluetooth, ZigBee, Near Field Communication (NFC), cellular data networks, and the like.

The system 4010 also includes a therapy device 4070 configured to be manipulated by the patient and/or to manipulate body parts of the patient to perform activities according to the treatment plan. In some embodiments, the therapy device 4070 can take the form of an exercise and rehabilitation device configured to implement and/or assist in the implementation of a rehabilitation regimen, which can be an orthopedic rehabilitation regimen, wherein the treatment is , rehabilitation of patient body parts such as joints or bones or muscle groups. Therapy device 4070 is any suitable medical, rehabilitation, therapeutic, etc. device configured to be remotely controlled via another computing device to treat and/or exercise a patient. could be. Therapy device 4070 can be an electromechanical machine, including one or more weights, electromechanical bicycles, electromechanical spin wheels, smart mirrors, treadmills, vibratory devices, and the like. Body parts can include, for example, the spine, hands, feet, knees, or shoulders. A body part may include a portion of a joint, bone, or muscle group, such as one or more vertebrae, tendons, or ligaments. As shown in FIG. 36, therapy device 4070 includes controller 4072, which may include one or more processors, computer memory, and/or other components. Therapy device 4070 also includes a fourth communication interface 4074 configured to communicate with patient interface 4050 via local communication interface 4068 . The therapeutic device 4070 also includes one or more internal sensors 4076 and actuators 4078, such as motors. Actuators 4078 may be used, for example, to move a patient's body part and/or resist force by the patient.

Internal sensors 4076 may measure one or more motion characteristics of therapy device 4070 such as, for example, force position, velocity, velocity, and/or acceleration. In some embodiments, internal sensor 4076 may include a position sensor configured to measure at least one of linear or angular motion of the patient's body part. For example, an internal sensor 4076 in the form of a position sensor can measure the distance a patient can move a portion of the therapy device 4070, such distance being the range of motion that the patient's body part can achieve. can correspond to In some embodiments, internal sensor 4076 may include a force sensor configured to measure force applied by the patient. For example, an internal sensor 4076 in the form of a force sensor may measure the force or load that a patient can apply to therapy device 4070 using a particular body part.

The system 4010 shown in FIG. 36 also includes a gait sensor 4082 that communicates with the server 4030 via the local communication interface 4068 of the patient interface 4050 . Gait sensor 4082 may track and store the number of steps taken by the patient. In some embodiments, the gait sensor 4082 may take the form of a wristband, wristwatch, or smartwatch. In some embodiments, the gait sensor 4082 may be integrated within a phone such as a smart phone. In some embodiments, the gait sensor 4082 may be integrated into articles of clothing, such as shoes, belts, and/or pants.

System 4010 shown in FIG. 36 also includes goniometer 4084 in communication with server 4030 via local communication interface 4068 of patient interface 4050 . Goniometer 4084 measures the angle of the patient's body part. For example, goniometer 4084 may measure the angle of flexion of the patient's knee or elbow or shoulder.

The system 4010 shown in FIG. 36 also includes a pressure sensor 4086 that communicates with the server 4030 via the local communication interface 4068 of the patient interface 4050. Pressure sensor 4086 measures the amount of pressure or load exerted by the patient's body part. For example, pressure sensor 4086 may measure the amount of force applied by the patient's foot when pedaling a stationary bicycle.

The system 4010 shown in FIG. 36 also includes a supervisory interface 4090 that may be similar or identical to the clinician interface 4020. In some embodiments, supervisory interface 4090 may have enhanced functionality over that provided by clinician interface 4020 . Supervisory interface 4090 may be configured for use by a person responsible for treatment planning, such as an orthopedic surgeon.

The system 4010 shown in FIG. 36 also includes a reporting interface 4092 that may be similar or identical to the clinician interface 4020. In some embodiments, the reporting interface 4092 may have less functionality than that provided by the clinician interface 4020. For example, reporting interface 4092 may not have the ability to modify treatment plans. Such reporting interface 4092 may be used, for example, by a claimant to determine use of system 4010 for billing purposes. In another example, reporting interface 4092 does not have the ability to display patient identifiable information, and for certain data fields about data subjects and/or for certain data fields about data subject quasi-identifiers, Only depersonalized and/or anonymized data may be presented. Such a reporting interface 4092 can be used, for example, by researchers to determine different effects of treatment regimens on different patients.

System 4010 includes an assistant interface 4094 for an assistant, such as a doctor, nurse, physical therapist, or technician, to communicate remotely with patient interface 4050 and/or therapy device 4070 . Such remote communication may allow an assistant to use the system 4010 to provide assistance or guidance to the patient. More specifically, assistant interface 4094 transmits telemedicine signals 4096, 4097, 4098a, 4098b, 4099a, 4099b via a network connection, such as via first network 4034 and/or second network 4058. configured to communicate with a patient interface 4050; Telemedicine signals 4096, 4097, 4098a, 4098b, 4099a, 4099b include audio signal 4096, audio-visual signal 4097, interface control signal 4098a for controlling the function of patient interface 4050, It includes one of an interface monitor signal 4098 b , a device control signal 4099 a for changing operating parameters of the therapy device 4070 , and/or a device monitor signal 4099 b for monitoring the status of the therapy device 4070 . In some embodiments, each of the control signals 4098a, 4099a may be unidirectional and convey commands from the assistant interface 4094 to the patient interface 4050. In some embodiments, the acknowledgment message is a patient It can be sent from interface 4050 to assistant interface 4094 . In some embodiments, each of the monitor signals 4098b, 4099b can be a one-way status information command from the patient interface 4050 to the assistant interface 4094. In some embodiments, an acknowledgment message may be sent from assistant interface 4094 to patient interface 4050 in response to successfully receiving one of monitor signals 4098b, 4099b.

In some embodiments, patient interface 4050 provides device control signals 4099a and device monitor signals 4099b between therapy device 4070 and one or more other devices, such as assistant interface 4094 and/or server 4030. It can be configured as a pass-through. For example, patient interface 4050 may be configured to transmit device control signals 4099a in response to device control signals 4099a in telemedicine signals 4096, 4097, 4098a, 4098b, 4099a, 4099b from assistant interface 4094.

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

In some embodiments, one or more portions of telemedicine signals 4096, 4097, 4098a, 4098b, 4099a, 4099b are sourced from pre-recorded sources (such as for example, audio recordings, video recordings, or animations). For example, a tutorial video can be streamed from server 4030 and presented on patient interface 4050 . Content from pre-recorded sources may be requested by the patient via patient interface 4050 . Alternatively, via controls on assistant interface 4094 , the assistant may play content from pre-recorded sources on patient interface 4050 .

Assistant interface 4094 includes assistant input device 4022 and assistant display 4024, which may be collectively referred to as assistant user interfaces 4022, 4024. Assistant input device 4022 may include one or more of a phone, keyboard, mouse, trackpad, or touch screen, for example. Alternatively or additionally, 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 one or more wide area microphones configured for the assistant to speak to the patient via the patient interface 4050. In some embodiments, the assistant input device 4022 includes hardware and/or software configured to interpret instructions spoken by the assistant by using one or more microphones and is voice-based. can be configured to provide the functionality of Assistant input device 4022 may be provided by or include functionality similar to existing voice-based assistants such as Siri from Apple, Alexa from Amazon, Google Assistant, or Bixby from Samsung. Assistant input device 4022 may include other hardware and/or software components. Assistant input device 4022 may include one or more general purpose and/or special purpose devices.

Assistant display 4024 can take one or more different forms, including, for example, a computer monitor or display screen on a tablet, smartphone, or smartwatch. Assistant display 4024 may include other hardware and/or software components such as projectors, virtual reality capabilities, or augmented reality capabilities. Assistant display 4024 may incorporate a variety of different visual, audio, or other presentation technologies. For example, assistant display 4024 may include non-verbal signals, such as voice signals, which may include speech and/or other sounds, such as tones, chimes, melodies, and/or music, which may signal various conditions and/or directions. A visual display may be included. Assistant display 4024 may comprise one or more different display screens that present 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 computer programs or applications (apps).

In some embodiments, system 4010 may provide computer translation of language from assistant interface 4094 to patient interface 4050 and/or vice versa. Computer conversion of language may include computer conversion of speech and/or computer conversion of text. Additionally or alternatively, system 4010 may provide speech recognition of text and/or vocalizations. For example, system 4010 may convert speech to printed text, and/or system 4010 may vocalize words from printed text. System 4010 may be configured to recognize words spoken by any or all of the patient, clinician, and/or assistant. In some embodiments, the system 4010 can be configured to recognize and respond to requests or commands spoken by the patient. For example, system 4010 may automatically initiate a telemedicine session in response to a verbal command (which may be given in any one of several different languages) by the patient.

In some embodiments, server 4030 may generate aspects of assistant display 4024 for presentation by assistant interface 4094 . For example, server 4030 may include a web server configured to generate display screens for presentation on assistant display 4024 . For example, the artificial intelligence engine 4011 generates treatment plans, billing sequences, and/or excluded treatment plans for the patient and renders those treatment plans, billing sequences for presentation on the assistant display 4024 of the assistant interface 4094. A display screen may be generated that includes sequences and/or treatment plans that are excluded. In some embodiments, assistant display 4024 may be configured to present a virtualized desktop hosted by server 4030 . In some embodiments, server 4030 may be configured to communicate with assistant interface 4094 via first network 4034 . In some embodiments, first network 4034 may include a local area network (LAN), such as an Ethernet network. In some embodiments, first network 4034 may include the Internet, and communication between server 4030 and assistant interface 4094 is by using encryption, for example, over a virtual private network (VPN). may be secured via privacy-enhancing techniques such as; Alternatively or additionally, server 4030 communicates with assistant interface 4094 via one or more networks independent of first network 4034 and/or other means of communication such as direct wired or wireless communication channels. may be configured to communicate. In some embodiments, patient interface 4050 and therapy device 4070 may each operate from a patient location that is geographically remote from the location of assistant interface 4094 . For example, patient interface 4050 and therapy device 4070 may be used as part of a home rehabilitation system, which may be assisted remotely by using assistant interface 4094 at a centralized location such as a clinic or call center.

In some embodiments, the assistant interface 4094 is a number of different terminals (e.g., computing devices) that can be grouped together, e.g., at one or more call centers or at one or more clinician's offices. can be one of In some embodiments, multiple assistant interfaces 4094 may be geographically distributed. In some embodiments, a person may work as an assistant remotely from any conventional office infrastructure. Such remote work may be implemented, for example, when assistant interface 94 takes the form of a computer and/or telephone. This telecommuting functionality may enable telecommuting arrangements, which may include part-time and/or flexible working hours for assistants.

37-38 illustrate one embodiment of a therapeutic device 4070. FIG. More specifically, FIG. 37 generally shows a therapeutic device 4070 in the form of a stationary cycling machine 4100, which may be called a stationary bike for short. A stationary cycling machine 4100 includes a set of pedals 4102 each mounted on a pedal arm 4104 for rotation about an axis 4106 . In some embodiments, as shown in FIG. 37, pedals 4102 are movable on pedal arms 4104 to adjust the range of motion that the patient uses in pedaling. For example, locating the pedals inward toward the axis 4106 corresponds to a smaller range of motion than when the pedals are locating outward from the axis 4106 . Pressure sensors 4086 are attached to or embedded in one or more of the pedals 4102 to measure the amount of force applied to the pedals 4102 by the patient. Pressure sensor 4086 may communicate wirelessly to therapy device 4070 and/or to patient interface 4050 .

FIG. 39 shows a person (patient) using the therapy device of FIG. 37 showing sensors and various data parameters connected to the patient interface 4050. FIG. An exemplary patient interface 4050 is a tablet computer or smart phone, such as an iPad, iPhone, Android device, or Surface tablet, or a phablet that is manually held by the patient. In some other embodiments, patient interface 4050 may be embedded in or attached to therapeutic device 4070 . FIG. 39 shows the patient wearing a gait sensor 4082 on the patient's wrist and a note reading "Today's Steps 41355" indicates that the gait sensor 4082 records its step count and 4050 indicates that it has been transmitted. FIG. 39 also shows the patient wearing a goniometer 4084 on the patient's right knee and the notation reading "Knee Angle 72°" indicates that the goniometer 4084 measures that knee angle and 4050 indicates that it is transmitting. 39 also shows that the right side of one of the pedals 4102 with a pressure sensor 4086 is at “12.5 lbs of force” and the right pedal pressure sensor 4086 measures and transmits that force measurement to the patient interface 4050. Indicates that the FIG. 39 also shows “27 lbs force” left side of one of pedals 4102 with pressure sensor 4086 , left pedal pressure sensor 4086 measuring and transmitting that force measurement to patient interface 4050 . indicates that FIG. 36 also shows other patient data such as the indication "Session Time 0:04:13" indicating that the patient has been using the therapy device 4070 for 4 minutes and 13 seconds. This session time may be determined by patient interface 4050 based on information received from therapy device 4070 . Figure 36 also shows an index labeled "Pain Level 3". Such pain levels may be obtained from patents in response to a petition, such as a question, presented on patient interface 4050 .

FIG. 40 is an illustrative embodiment of overview display 4120 of assistant interface 4094 . Specifically, overview display 4120 presents several different controls and interfaces for the assistant to remotely assist the patient using patient interface 4050 and/or therapy device 4070 . This remote assistance functionality may also be referred to as telemedicine or telehealth.

Specifically, overview display 4120 includes patient profile display 4130 that presents historical information about the patient using therapy device 4070 . The patient profile display 4130 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40, although the patient profile display 4130 may take other forms such as a separate screen or pop-up window. . In some embodiments, the patient profile display 4130 may include a limited subset of patient history information. More specifically, the data presented on the patient profile display 4130 may depend on the Assistant's need for that information. For example, a medical practitioner assisting a patient with a medical issue may be provided with historical medical information about the patient, whereas a technician troubleshooting a problem with the therapy device 4070 may have more information about the patient. A limited set can be provided. For example, a technician may be given only the patient's name. The patient profile display 4130 may include depersonalized and/or anonymized data, or use any privacy-enhancing technology in ways that may violate patient confidentiality requirements; Sensitive patient data may be prevented from being communicated. Such privacy-enhancing technologies include, but are not limited to, the Health Insurance Portability and Accountability Act (HIPAA) or the General Data Protection Regulation (GDPR), where patients may be considered "data subjects." compliance with laws, regulations or other governance rules.

In some embodiments, the patient profile display 4130 may present information regarding the treatment regimen to be followed when the patient uses the treatment device 4070. Such treatment plan information may be restricted to medical assistants, such as doctors or physical therapists. For example, a medical practitioner assisting a patient with a problem with a treatment regimen may be provided with treatment plan information, whereas a technician troubleshooting a problem with the treatment device 4070 may not have any information regarding the patient's treatment plan. May not be provided.

In some embodiments, one or more recommended and/or ruled out treatment plans may be presented to the assistant in the patient profile display 4130. 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 in real time from the server 4030, particularly during a telemedicine or telehealth session. An example of presenting one or more recommended and/or ruled out treatment plans is described below with reference to FIG.

In some embodiments, one or more treatment plans and/or billing sequences associated with the treatment plans may be presented to the assistant in the patient profile display 4130. 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 in real-time from the server 4030 during telehealth sessions, among other things. An example of presenting one or more treatment plans and/or billing sequences associated with treatment plans is described below with reference to FIG.

In some embodiments, one or more treatment plans and associated monetary value generation, patient outcomes, and risks associated with the treatment plans may be presented to the assistant in patient profile display 4130. One or more treatment plans and associated monetary value yields, patient outcomes, and risks associated with the treatment plans are generated by the artificial intelligence engine 4011 of the server 4030 and, in particular, are generated by the server 4030 during a telehealth session. can be received in real time from An example presenting one or more treatment plans and associated monetary value generation, patient outcomes, and risks associated with the treatment plans is described below with reference to FIG.

The exemplary overview display 4120 shown in FIG. 40 also includes a patient status display 4134 that presents status information regarding the patient using the therapy device. The patient status display 4134 may take the form of a portion or region of the overview display 4120, as shown in FIG. 40, although the patient status display 4134 may take other forms, such as a separate screen or pop-up window. Patient status display 4134 includes sensor data 4136 from one or more of external sensors 4082 , 4084 , 4086 and/or from one or more internal sensors 4076 of therapy device 4070 . In some embodiments, the patient status display 4134 may present other data 4138 about the patient, such as last reported pain level or progress within a treatment plan.

What data is available to view and/or modify for any or all of the user interfaces 4020, 4050, 4090, 4092, 4094 of the system 4010 using user access controls? can restrict access, including In some embodiments, user access controls may be used to control what information is available to any given person using system 4010 . For example, data presented on assistant interface 4094 may be controlled by user access controls, with permissions set according to the assistant/user's need and/or entitlement to view the information.

The example overview display 4120 shown in FIG. 40 also includes a help data display 4140 that presents information for use by the assistant in assisting the patient. Help data display 4140 may take the form of a portion or region of overview display 4120, as shown in FIG. Help data display 4140 may take other forms, such as a separate screen or pop-up window. Help data display 4140 may include, for example, presenting answers to frequently asked questions regarding the use of patient interface 4050 and/or therapy device 4070 . 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 a flow chart or walkthrough for the assistant to use in determining the root cause and/or solution to the patient's problem. In some embodiments, assistant interface 4094 may present two or more help data displays 4140, which may be the same or different, to simultaneously present help data for use by the assistant. For example, a first help data display can be used to present a troubleshooting flow chart for determining the root of a patient's problem, and a second help data display can be used for the assistant to read out to the patient: Script information may be presented, preferably information such as instructions for the patient to perform some action, which may help narrow down or solve the problem. In some embodiments, the second help data display may automatically populate the script information based on the input to the troubleshooting flowchart in the first help data display.

The example overview display 4120 shown in FIG. 40 also includes patient interface controls 4150 for presenting information about the patient interface 4050 and/or for modifying one or more settings of the patient interface 4050 . Patient interface controls 4150 may take the form of a portion or region of overview display 4120, as shown in FIG. Patient interface controls 4150 may take other forms, such as separate screens or pop-up windows. Patient interface control 4150 may present the communicated information to assistant interface 4094 via one or more of interface monitor signals 4098b. As shown in FIG. 40, patient interface controls 4150 include a display feed 4152 of displays presented by patient interface 4050 . In some embodiments, the display feed 4152 may contain a live copy of the display screen currently being presented to the patient by the patient interface 4050. In other words, display feed 4152 may present an image of what is presented on the display screen of patient interface 4050 . In some embodiments, display feed 4152 may include informal information about the display screen currently being presented by patient interface 4050, such as screen name or screen number. Patient interface controls 4150 may include patient interface settings controls 4154 for an assistant to adjust or control one or more settings or aspects of patient interface 4050 . In some embodiments, the patient interface settings control 4154 may cause the assistant interface 4094 to generate and/or transmit interface control signals 4098 to control features or settings of the patient interface 4050 .

In some embodiments, patient interface settings controls 4154 may include collaborative browsing or co-browsing capabilities for assistants to view and/or control patient interface 4050 remotely. For example, the patient interface settings control 4154 allows the assistant to remotely enter text into one or more text entry fields on the patient interface 4050 and/or using the mouse or touch screen of the assistant interface 4094 to It may allow the cursor on the patient interface 4050 to be controlled remotely.

In some embodiments, using the patient interface 4050, the patient interface settings control 4154 may allow the assistant to change settings that the patient cannot change. For example, the patient interface 4050 may be blocked from accessing language settings to prevent the patient from inadvertently switching the language used for display on the patient interface 4050, whereas the patient interface 4050 may be blocked from accessing language settings. Settings control 4154 may allow the assistant to change the language setting of patient interface 4050 . In another example, the patient interface 4050 uses a font size to prevent the patient from inadvertently switching the font size used for display on the patient interface 4050 such that the display becomes unreadable to the patient. The patient interface settings control 4154 may allow the Assistant to change the font size setting of the patient interface 4050, whereas it may not be possible to change the setting to a smaller size.

The example overview display 4120 shown in FIG. 40 also includes a patient interface 4050 and one or more other devices 4070, 4082, 4084, such as a therapy device 4070, a gait sensor 4082, and/or a goniometer 4084. includes an interface communications display 4156 that indicates the status of communications between the Interface communications display 4156 may take the form of a portion or region of overview display 4120, as shown in FIG. Interface communication display 4156 may take other forms, such as a separate screen or pop-up window. Interface communications display 4156 may include controls for the Assistant to remotely modify communications with one or more of other devices 4070 , 4082 , 4084 . For example, the assistant may reset communication with one of the other devices 4070, 4082, 4084, or establish communication with a new one of the other devices 4070, 4082, 4084. The patient interface 4050 can be commanded remotely. This functionality is useful, for example, if the patient has a problem with one of the other devices 4070, 4082, 4084, or if the patient receives a new or replacement one of the other devices 4070, 4082, 4084. can be used if

The example overview display 4120 shown in FIG. 40 also includes device controls 4160 for the assistant to view and/or control information about the therapy device 4070. Device controls 4160 may take the form of a portion or region of overview display 4120, as shown in FIG. Device control 4160 may take other forms, such as a separate screen or pop-up window. Device control 4160 may include device status display 4162 with information about the current status of the device. Device status display 4162 may present information communicated to assistant interface 4094 via one or more of device monitor signals 4099b. Device status display 4162 may indicate whether therapy device 4070 is currently in communication with patient interface 4050 . Device status display 4162 may present other current and/or historical information regarding the status of therapy device 4070 .

Device controls 4160 may include device settings controls 4164 for the Assistant to adjust or control one or more aspects of therapy device 4070 . The device settings control 4164 may cause the assistant interface 4094 to generate and/or transmit device control signals 4099 to change operating parameters of the therapy device 4070 (eg, pedal radius settings, resistance settings, target RPM, etc.). The device settings controls 4164 can include a mode button 4166 and a position control 4168 that allow the assistant to put the actuator 4078 of the therapy device 4070 into manual mode and then use the position control 4168 to set the actuator 4078 to It can be used in conjunction so that settings such as position or speed can be changed. A mode button 4166 may provide settings, such as position, for switching between automatic and manual modes. In some embodiments, one or more settings may be adjustable at any time and without an associated auto/manual mode. In some embodiments, the assistant may change the operating parameters of the therapy device 4070, such as the pedal radius setting, while the patient is actively using the therapy device 4070. Such “on-the-fly” adjustments may or may not be available to the patient using patient interface 4050 . In some embodiments, the device settings control 4164 may use the patient interface 4050 to allow the assistant to change settings that the patient cannot change. For example, the patient interface 4050 may be prevented from changing pre-configured settings, such as the height or tilt settings of the therapy device 4070, whereas the device settings control 4164 prevents the assistant from changing the therapy device 4070 may offer to change the height or tilt settings of the .

The exemplary overview display 4120 shown in FIG. 40 may also include patient communication controls 4170 for controlling an audio or audiovisual communication session with the patient interface 4050. A communication session with patient interface 4050 may include a live feed from assistant interface 4094 for presentation by an output device of patient interface 4050 . A live feed may take the form of an audio feed and/or a video feed. In some embodiments, patient interface 4050 may be configured to provide two-way audio or audiovisual communication with a person using assistant interface 4094 . Specifically, a communication session with patient interface 4050 may include an interactive (two-way) video feed or an audiovisual feed, with patient interface 4050 and assistant interface 4094 each presenting the other's video. In some embodiments, the patient interface 4050 may present video from the assistant interface 4094 while the assistant interface 4094 presents audio only, or the assistant interface 4094 presents live audio from the patient interface 4050. No signal or visual signal is presented. In some embodiments, the assistant interface 4094 may present video from the patient interface 4050 while the patient interface 4050 presents audio only, or the patient interface 4050 presents live audio from the assistant interface 4094. No signal or visual signal is presented.

In some embodiments, an audio or audiovisual communication session with the patient interface 4050 may occur, at least in part, while the patient is performing a rehabilitation regimen on the body part. Patient communication control 4170 may take the form of a portion or region of overview display 4120, as shown in FIG. Patient communication control 4170 may take other forms, such as a separate screen or pop-up window. Voice and/or audio-visual communication may be via the assistant interface 4094 and/or via one or more separate devices such as a telephone system or videoconferencing system used by the assistant while the assistant is using the assistant interface 4094. , may be processed and/or directed. Alternatively or additionally, voice communication and/or audiovisual communication may include communication with a third party. For example, the system 4010 allows an assistant to initiate a three-way conversation with a patient and a subject matter expert, such as a healthcare professional or specialist, regarding the use of a particular piece of hardware or software. obtain. The example patient communication control 4170 shown in FIG. 40 includes a call control 4172 for use by a healthcare provider in managing various aspects of voice or audiovisual communication with a patient. Call controls 4172 include a disconnect button 4174 for the Assistant to end a voice or audiovisual communication session. Call controls 4172 also include a mute button 4176 for temporarily muting audio or audiovisual signals from assistant interface 4094 . In some embodiments, call control 4172 may include other features such as a hold button (not shown). The call controls 4172 also include one or more record/play buttons, such as record, play, and pause buttons, for controlling the recording and/or playback of audio and/or video from the conference call session on the patient interface 4050. A playback control unit 4178 is included. The call control 4172 also includes a video feed display 4180 for presenting still and/or video images from the patient interface 4050 and a self video display 4182 showing a current image of the assistant using the assistant interface. . Self-video display 4182 may be presented as a picture-in-picture format within a section of video feed display 4180, as shown in FIG. Alternatively or additionally, selfie video display 4182 may be presented separately and/or independently from video feed display 4180 .

The example overview display 4120 shown in FIG. 40 also includes a third party communication control 4190 for use in conducting voice and/or audiovisual communications with a third party. Third party communication control 4190 may take the form of a portion or region of overview display 4120, as shown in FIG. Third party communication control 4190 may take other forms, such as a separate on-screen display or a pop-up window. Third party communication control 4190 provides contact lists and/or It may include one or more controls, such as buttons or controls. Third party communication controls 4190 may include teleconferencing capabilities for a third party to communicate with both the assistant via assistant interface 4094 and the patient via patient interface 4050 simultaneously. For example, system 4010 may provide for assistants to initiate three-way conversations with patients and third parties.

FIG. 41 shows an exemplary block diagram of training a machine learning model 4013 to output a treatment plan 4602 for a patient based on data 4600 about the patient in accordance with this disclosure. Data regarding other patients may be received by server 4030 . Other patients may be performing treatment plans using different treatment devices. The data may include characteristics of other patients, details of treatment plans performed by other patients, and/or results of implementing treatment plans (e.g., recovery rate of a patient's body part, amount of recovery of a patient's body part, etc.). , increase or decrease in muscle strength of a portion of the patient's body, increase or decrease in range of motion of the portion of the patient's body, etc.).

Data are assigned to the various cohorts as depicted. Cohort A contains data of patients with similar first characteristics, first treatment regimens, and first outcomes. Cohort B includes data from patients with similar second characteristics, second treatment regimens, and second outcomes. For example, Cohort A may include first characteristics of patients in their twenties without any medical condition who have undergone surgery for a fractured limb, and the treatment plan for these patients may include a specific treatment protocol. (e.g., using the therapy device 4070 for 30 minutes five times a week for 3 weeks, and the value of the therapy device 4070 property, configuration, and/or setting is X (where X is a number) for the first 2 weeks; The last week is set to Y (where Y is a number).

Cohort A and Cohort B may be included in the training data set used to train machine learning model 4013 . A machine learning model 4013 can be trained to match patterns between characteristics of each cohort and output treatment plans that provide results. Thus, when new patient data 4600 is input to the trained machine learning model 4013, the trained machine learning model 4013 matches the traits contained in the data 4600 to the traits of either Cohort A or Cohort B. and output an appropriate treatment plan 4602 . In some embodiments, machine learning model 4013 may be trained to output one or more excluded treatment plans that should not be performed by new patients.

FIG. 42 illustrates one embodiment of a summary display of a patient interface 4050 presenting a virtual avatar 4700 guiding the patient through an exercise session in accordance with the present disclosure. Virtual avatar 4700 may be presented on output device 4054 (eg, display screen) of patient interface 4050 . As depicted, virtual avatar 4700 may represent a person. In some embodiments, a person may be an actual individual, such as a healthcare professional, professional athlete, patient, relative, friend, sibling, celebrity, etc. In other embodiments, a person may be a fictional person. can be built, for example, can be a superhero or the like. As discussed further herein, a virtual avatar can be any person, object, building, animal, being, alien, robot, or the like. For example, children may connect more strongly with animal animations to guide them through their exercise sessions. Virtual avatar 4700 may be selected by the patient and/or healthcare professional from a library of virtual avatars stored on a database at server 4030 . In some embodiments, virtual avatar 4700 may be able to be uploaded to a database or for private use by patients and/or medical personnel. For example, a library of virtual avatars can be stored in system data store 4042 and/or patient data store 4044 . When virtual avatar 4700 is selected for a patient, the identifier of virtual avatar 4700 can be associated with the patient's identifier in system data store 4042 and/or patient data store 4044 .

Virtual avatar 4700 may perform one or more exercises specified in an exercise session of the patient's treatment plan. As used throughout this disclosure, and for the avoidance of doubt, "exercise" means, for example, rehabilitation exercise, high-intensity interval training, strength training, range-of-motion training, or performed on a treatment device specified in a treatment plan. any bodily or physical exercise that can be performed (including modifications or amendments or revisions thereto) or that can be reasonably substituted with a different treatment device. For example, one exercise may involve pedaling a stationary bicycle, and virtual avatar 4700 may be animated as pedaling the bicycle in a manner desired for the patient. In some embodiments, virtual avatar 4700 may include an actual video of a person performing an exercise session. As such, virtual avatar 4700 may include audio, video, audiovisual, and/or multimedia data generated by server 4030 and/or of a real person conducting an exercise session. In some embodiments, virtual avatar 4700 may represent a medical practitioner, such as a physical therapist, coach, trainer, and the like.

In some embodiments, virtual avatar 4700 may be controlled by one or more machine learning models 4013 of artificial intelligence engine 4011 . For example, one or more machine learning models 4013 may be trained based on historical data and real-time or near-time data. The data used to train the machine learning model 4013 includes previous feedback received from the user (e.g. pain levels), patient characteristics at various points in the treatment plan (e.g. heart rate, blood pressure, temperature , sweat rate, etc.), sensor measurements received as the patient performed the patient's treatment plan (e.g., pedal pressure, range of motion, motor speed of the therapy device 4070, etc.), and/or specific movements Results achieved by the patient after being performed (e.g., starting a telemedicine session with the healthcare worker's multimedia feed, replacing the virtual avatar 4700 with the healthcare worker's multimedia feed, uttering a specific auditory statement). presenting certain visuals on the output device 4054, changing parameters of the exercise session (eg, reducing the amount of resistance provided by the therapy device 4070), etc.).

The output device 54 also presents a self-video section 4702 that presents the patient's video obtained from the patient interface 4050 camera. Self-video is used to verify whether the patient is using proper form, tone, consistency, or any other observable or measurable quality or quantity while performing an exercise session. , can be used by the patient. Video obtained from the camera of patient interface 4050 can be transmitted to assistant interface 4094 for presentation while the patient conducts an exercise session. A healthcare professional views an assistant interface 4094 that presents a video of the patient, whether to intervene by speaking to the patient via the patient's patient interface 4050, and/or a virtual avatar 4700 from the assistant interface 4094. You can decide whether to replace it with a feed.

Output device 4054 also presents graphical user interface (GUI) objects 4704 . GUI object 4704 may be an element that allows a user to provide feedback to server 4030 . For example, GUI object 4704 may present a scale of values representing the level of pain the patient is currently experiencing, and GUI object 4704 may allow the patient to select a value representing the patient's level of pain. . This selection may cause the message to be transmitted to server 4030 . In some embodiments, the message including the pain level may relate to the triggering event, as described further herein. The server 4030 may determine whether the level of pain experienced by the patient exceeds a certain threshold severity level. If the level of pain exceeds a certain threshold severity level, the server 4030 suspends the virtual avatar 4700 and/or redirects the virtual avatar 4700 to an audio feed, visual feed, audiovisual feed, or multimedia feed.

FIG. 43 illustrates one embodiment of an overview display 4120 of an assistant interface 4094 that receives notifications regarding a patient and allows an assistant (e.g., a healthcare professional) to initiate a telemedicine session in real time according to the present disclosure. show. As depicted, overview display 4120 includes a patient profile 4130 section. Patient profile 4130 presents information regarding the treatment plan being performed by patient “John Doe”. Treatment plan 4800 states: "John Doe is cycling 5 miles on a treatment machine. The treatment machine pedals are configured to provide a 45 degree range of motion." Summary display 4120 also includes notifications 4802 received due to triggering events. The notification presents "John Doe has indicated that he is experiencing high levels of pain while exercising." Summary display 4120 also includes prompt 4804 for the healthcare professional to use assistant interface 4094 . The prompt asks, "Would you like to start a telemedicine session?" Overview display 4120 is graphically configured to allow healthcare professionals to choose to initiate a telemedicine session using wired or wireless input peripherals (e.g., touch screen, mouse, keyboard, microphone, etc.). Contains elements (eg, buttons) 4806 . Although the above example details a summary display 4120 of the assistant interface 94 that presents information in the form of text, alternative or additional ways of presenting that information are in the form of graphs, charts, or the like. could be.

An assistant (e.g., a healthcare professional) using assistant interface 94 (e.g., a computing device) during a telemedicine session may view summary display 4120 (e.g., a user interface presented on display screen 4024 of assistant interface 4094). Self-video 4182 in part may be presented. Assistant interface 4094 may also present video from the patient (eg, self-video 4182) on video feed display 4180 in the same portion of overview display 4120 as the self-video. Additionally, the video feed display 4180 also allows medical personnel to share treatment plans with the patient on the patient interface 4050 in real time or near real time during a telemedicine session, control operating parameters of the treatment device 4070, etc. may include graphical user interface (GUI) objects 4808 (eg, buttons) that

FIG. 44 shows one embodiment of a summary display presented by output device 4054 of patient interface 4050 . The output device 4054 presents a feed 4900 (eg, multimedia, preferably including audio, video, or both) of a healthcare worker replacing a virtual avatar according to the present disclosure in real-time during a telemedicine session. In some embodiments, the virtual avatar may remain presented on the patient interface 4050, but in a paused state, and the feed may preferably be restricted to only when the healthcare worker speaks to the patient. In some embodiments, feed 4900 may replace a virtual avatar, as depicted. The feed allows the healthcare provider and the patient to talk to the patient, understand the patient's pain level, patient characteristics (e.g., heart rate, sweat rate, etc.), and/or one or more sensor readings (e.g., pressure on pedals). , range of motion, etc.).

It should be appreciated that the medical practitioner may be viewing, monitoring, treating, diagnosing, etc. multiple patients simultaneously on the assistant interface 94 . For example, each patient may be presented in a respective portion of the user interface of assistant interface 4094, as discussed further below. Each respective portion may present a variety of information regarding each patient. For example, each portion may present the feed of a patient performing an exercise session using the therapy device, patient characteristics, patient treatment plan, sensor readings, and the like. The user interface of the assistant interface 4094 allows the healthcare worker to select one or more patients and a virtual avatar guiding the one or more patients through exercise is paused in real time or near real time and/or or can be configured to allow it to be replaced.

Once the telemedicine session between patient interface 4050 and assistant interface 4094 is complete, the feed from the healthcare worker's computing device can be replaced by a virtual avatar on patient interface 4050 . The virtual avatar may continue to guide the patient through the exercise session wherever and/or when the exercise session is paused due to the initiation of the trigger event. The assistant interface 4094 may resume displaying the patient's feed and/or information about the patient performing the exercise session.

FIG. 45 illustrates an exemplary embodiment of a method 41000 for substituting a virtual avatar with a healthcare worker's feed based on triggering events that occur in accordance with this disclosure. Method 41000 is performed by processing logic that may include hardware (circuitry, special purpose logic, etc.), software (such as running on a general purpose computer system or special purpose machine), or a combination of both. Method 41000 and/or each of its individual functions, routines, other methods, scripts, subroutines, or actions may be implemented on one or more processors of a computing device (e.g., server 4030 running artificial intelligence engine 4011). 36 optional component). In particular implementations, method 41000 may be performed by a single processing thread. Alternatively, method 41000 may be performed by two or more processing threads, each thread implementing one or more individual functions or routines, or other methods, scripts, subroutines, or method operations.

For ease of explanation, method 41000 is depicted and described as a series of acts. However, operations in accordance with this disclosure may occur in various orders and/or concurrently and/or with other operations not presented and described herein. For example, the acts depicted in method 41000 may be performed in combination with any other acts of any other methods disclosed herein. Moreover, not all illustrated acts may be required to implement method 41000 in accordance with the disclosed subject matter. Additionally, those skilled in the art will appreciate that method 41000 can alternatively be represented as a series of interrelated states via state diagrams, directed graphs, deterministic finite-state automata, non-deterministic finite-state automata, Markov diagrams, or event diagrams. will understand and recognize

At 41002, the processing device may provide the patient's computing device (eg, patient interface 4050) with a virtual avatar that is presented on the patient's computing device. A virtual avatar may be configured to guide the patient through an exercise session using a virtual representation of therapy device 4070 . The virtual avatar may be configured to guide the patient through the exercise session using audio, video, haptic feedback, or some combination thereof. Audio, video, tactile feedback, or some combination thereof may be provided by the patient's computing device. In some embodiments, the processing device may determine the exercise session to be performed based on the patient's treatment plan. Therapy device 4070 may be configured for use by a patient undergoing an exercise session.

In some embodiments, prior to providing the virtual avatar, the processing device may transmit a notification to the patient's computing device to initiate an exercise session. Notifications may include push notifications, text messages, calls, emails, or some combination thereof. Notifications may be transmitted based on the schedule specified in the treatment plan. The schedule can include dates and times for performing exercise sessions, durations for performing exercise sessions, exercises to perform during exercise sessions, configuration of parts of therapy device 4070 (e.g., pedals, seats, etc.), etc. . The processing device may receive a selection from the patient's computing device to initiate an exercise session to use the therapy device 4070 . The processing device may transmit control signals to therapy device 4070 to cause therapy device 70 to initiate an exercise session. In response to transmitting the control signal, the processing device may provide the virtual avatar to the patient's computing device.

A virtual avatar may be associated with a medical practitioner, such as a medical practitioner who prescribed or generated a treatment plan to be administered by the patient. In some embodiments, a treatment plan may be designed in whole or in part by a medical professional. In some embodiments, the treatment plan may be generated in whole or in part by the artificial intelligence engine 4011, and a healthcare professional may review the treatment plan and/or instruct the patient for the treatment plan to be implemented. Treatment plans may be modified before being transmitted.

A virtual avatar may represent a surrogate medical practitioner and may be a person, entity, thing, software or electronic bot, object, etc. that guides one or more patients through a treatment plan. A virtual avatar can guide a number of patients through a treatment plan at various stages such as patient rehabilitation, pre-operative rehabilitation, and recovery. At any point in time, the virtual avatar may be replaced by a healthcare worker feed (eg, live audio, audiovisual, etc.), which feeds directly or indirectly from the assistant interface 4094 to the patient interface 4050. Note that either is transmitted through server 4030 . A feed may be a stream of data packets (eg, audio, video, or both) obtained via a camera and/or microphone associated with assistant interface 4094 in real time or near real time. The feed may be presented on user interface 4054 of patient interface 4050 .

In some embodiments, a virtual avatar may be initially selected by a healthcare professional. A virtual avatar may be a file stored in a virtual avatar library from which a healthcare worker may select a virtual avatar. For example, a virtual avatar can be a live representation of a person (eg, male, female, non-binary, or any other gender that the person identifies with). In some embodiments, the virtual avatar may be a live-action or virtual representation of an animal (eg, tiger, lion, unicorn, rabbit, etc.), which is more fun for young people (eg, children). , and can be motivating. In some embodiments, the virtual avatar may be a lifelike or virtual representation of a robot, alien, or the like.

In some embodiments, a healthcare professional may design their own virtual avatar. For example, a healthcare worker may be provided a user interface on the healthcare worker's assistant interface 4094, which may provide user interface elements that allow configuration of the virtual avatar. A healthcare professional may use the user interface to generate a virtual avatar that looks like the healthcare professional or any suitable person.

In some embodiments, the virtual avatar may be selected by the patient. In some embodiments, the selected virtual avatar may be associated with the patient (eg, via patient identifier and virtual avatar identifier) and stored in a database. For example, some patients may prefer certain virtual avatars over other virtual avatars. In some embodiments, different virtual avatars may guide the patient through the same or different treatment plans. With respect to any treatment plan referred to herein, different aspects, parts, or configurations of the treatment plan may be further guided by two or more virtual or physical avatars, each such avatar being associated with the treatment application. To the extent that it is associated with unconnected aspects, portions or structures of the plan, it is associated with a particular aspect, portion or structure of the treatment plan and any other avatar. In other embodiments, two or more avatars, physical and/or virtual, may exist simultaneously but perform different functions within a particular aspect, portion, or configuration of a treatment plan. In group therapy sessions, multiple patients may be undergoing the same treatment plan, with each patient having a unique patient interface that simultaneously presents one or more virtual avatars that are the same or different to guide the patient through the treatment plan. 4050.

As a patient implements a treatment plan, a healthcare professional may be able to view multiple patients in various tiles on the user interface of assistant interface 4094 . The term "tile" refers to a feed from the patient's respective patient interface 4050, a feed of patient characteristics (e.g., heart rate, blood pressure, temperature, etc.), measurements (e.g., heart rate, blood pressure, temperature, etc.), measurements (e.g., For example, it may refer to a square that includes a feed of pressure exerted on a pedal, range of motion determined by a goniometer, number of steps, speed of the motor of the therapy device 4070, etc.), or some combination thereof. Thus, using the disclosed embodiments, medical personnel may be able to manage, monitor, and/or treat multiple patients at once. A single healthcare professional can treat multiple patients simultaneously, using only the assistant interface 4094 to view, treat, manage, monitor, etc., as they perform their treatment plans. Computing resources may be saved by doing so.

At 41004, the processing device may receive a message regarding the triggering event from the patient's computing device. In some embodiments, the message may include data regarding the patient's pain level, patient characteristics, sensor readings, or some combination thereof. The triggering event may be data related to patient pain level, patient characteristics (e.g., heart rate, blood pressure, body temperature, sweat rate, etc.), sensor readings (e.g., pressure, range of motion, velocity, etc.), or some combination thereof. can refer to any event associated with

In some instances, the virtual avatar may guide the patient through the treatment plan as a pre-recorded animation, and the health care worker actively engages in a telemedicine session with the patient as the patient implements the treatment plan. do not have to engage in In some embodiments, upon the occurrence of a triggering event, a notification may be transmitted to a healthcare worker's computing device, and such device alerts the healthcare practitioner to the notification. A healthcare professional may use wired or wireless input peripherals (eg, touch screen, mouse, keyboard, microphone) to select notifications and initiate telemedicine sessions with the patient's computing device. Such techniques reduce the use of one or more computing resources, such as network resources (e.g., bandwidth), by initiating a telemedicine session only when a notification is selected, rather than throughout the treatment plan. and/or the cost and/or risk profile may be minimized or otherwise optimized. In other embodiments, the healthcare provider computing device and the patient computing device and the healthcare provider computing device continue the telemedicine session as one or more patients administer the treatment plan. can be engaged continuously or intermittently. The triggering event may allow the medical personnel to intervene and/or replace the virtual avatar, suspend the virtual avatar, or both. In some embodiments, while the patient is administering a treatment plan, a healthcare professional selectively selects one or more of the patients to use one or more of those patient's computing devices. The virtual avatar above may be replaced, paused, etc. Such technology may allow healthcare professionals to intervene in some, but not all, of the patient as the patient implements a treatment plan. For example, a healthcare professional selects a patient who has not reached a target threshold (e.g., pressure, range of motion, velocity, etc.) in the treatment plan, selects a patient who indicates that the patient is experiencing a threshold level of pain. etc.

In some embodiments, the virtual avatar displays sensor data (e.g., pressure measurements from pedals, range of motion measurements from goniometers, speed data, etc.), patient characteristics (e.g., sweat rate, heart rate, blood pressure, body temperature, arterial blood gases and/or oxygenation levels or percentages), real-time feedback from the patient or other patients (e.g. indication of pain level), or some combination thereof. It can be controlled in real time or near real time by one or more machine learning models. One or more machine learning models may generate output that controls the virtual avatar. For example, the output may indicate that the way the virtual avatar performs a particular exercise (e.g., pedaling faster or slower on therapy device 70) is an encouraging phrase (e.g., "You can do it", "Good luck", etc.). ) and so on. Modifications can be based on training data for other patients, such training data indicating that the modification will achieve desired patient performance and/or results for other patients or an increased probability of achieving desired patient performance or results. result in For example, the training data may provide specific audio and/or video when specific sensor data is detected to allow the patient to exert more force on the pedals, thereby exercising the patient's leg muscles in accordance with the treatment plan. It can be shown that it can lead to training.

At 41006, the processing device may determine whether the severity level of the triggering event exceeds a threshold severity level. A severity threshold level can be any suitable quantity, value, indicator, or the like. For example, in one embodiment, a threshold severity level may be a particular level of pain experienced by a patient. At any time during the exercise session, the patient may use any of the input peripherals of patient interface 4050 to express the patient's level of pain. For example, the patient may touch a button on the touch screen of the patient interface 4050, and the button may indicate that the patient is experiencing a pain level of 8 on a scale of 1-10, where 1 is the lowest amount of pain and 10 is the highest amount of pain. In this example, the threshold severity level may be 5 pain levels. Therefore, the level of pain experienced by the patient (8) exceeds the threshold severity level (5). In some embodiments, the threshold severity level is the amount of force the patient is exerting on the pedals, the range of motion the patient can achieve while pedaling, the speed the patient can achieve, the range of motion and/or velocity duration, etc. For example, a threshold severity level may be configured based on the patient pedaling through a specified range of motion for a specified period of time, the patient pedaling through a specified range of motion, or of such a goal, for a specified period of time during an exercise session. A threshold severity level may be exceeded if no change is achieved.

At 41008, in response to determining that the severity level of the triggering event exceeds the threshold severity level, the processing device presents a presentation of the virtual avatar on the patient's computing device to the healthcare worker's computing device. (eg, assistant interface 4094). In some embodiments, replacing a virtual avatar with a multimedia feed may initiate a telemedicine session between a patient and a healthcare professional. The processing device may receive a second message from the patient's or healthcare worker's computing device indicating that the telemedicine session is complete, and the processing device presents the multimedia feed on the patient's computing device. can be replaced by the presentation of a virtual avatar. A virtual avatar may be configured to continue to guide the patient through the exercise session to completion. That is, the exercise session and/or virtual avatar may be paused at a particular timestamp when the healthcare worker's multimedia feed replaces the virtual avatar, and the exercise session and/or virtual avatar may be paused at a particular time when the telemedicine session is completed. Playback can be started at the timestamp.

In some embodiments, in response to determining that the severity level of the triggering event does not exceed the threshold severity level at 41010, the processing device directs control of the virtual avatar to the healthcare worker's computing device. A medical practitioner may remotely control a virtual avatar to interact with a patient. In some embodiments, in response to determining that the severity level of the triggering event does not exceed the threshold severity level, the processing device may provide controls for the virtual avatar to the healthcare worker's computing device. , which allows a medical practitioner to remotely control a virtual avatar to interact with a patient.

In some embodiments, the processing device may determine the exercise session to be performed based on a second treatment plan for a second patient, where performance by the second patient is performed by the second patient. treatment device. The processing device may present on a second patient's second computing device (e.g., patient interface 50) a virtual avatar configured to guide the patient through an exercise session to use the therapy device. . In some embodiments, the presentation of the virtual avatar is replaced on the patient's computing device with the presentation of a multimedia feed from the healthcare worker's computing device, but the virtual avatar is the second patient's second patient. may remain presented on two computing devices. In some embodiments, the presentation of the virtual avatar is replaced on the patient's computing device with the presentation of a multimedia feed from the healthcare worker's computing device, but the virtual avatar is the second patient's second patient. On two computing devices, the multimedia feed from the healthcare worker's computing device may be replaced.

FIG. 46 illustrates an exemplary embodiment of a method for providing virtual avatars according to this disclosure. Method 41100 includes acts performed by a processor of a computing device (eg, any component of FIG. 36, such as server 4030 running artificial intelligence engine 4011). In some embodiments, one or more operations of method 41100 are implemented in computer instructions stored on a memory device and executed by a processing device. Method 41100 may be performed in the same or similar manner as described above with respect to method 41000. The acts of method 41100 may be performed in any combination with the acts of any of the methods described herein. Method 41100 may include further acts associated with 41002 within method 41000 related to providing the virtual avatar to the patient's computing device.

At 41102, the processing device may obtain data associated with the exercise session. The data may include instructions implementing a virtual model that animates one or more movements associated with the exercise session. A virtual model can be two-dimensional, three-dimensional, or n-dimensional (for animation or projection onto a 3D virtual environment or 2D layout). In some embodiments, the virtual model may be mesh model animation, contour animation, virtual human animation, skeletal animation, and the like. For example, a virtual model can be a surface representation (referred to as a mesh) used to draw a character (eg, a medical worker) and a set of hierarchical interconnected parts. The virtual model can animate (eg, pose and keyframe) the mesh using the virtual armature. As used herein, an armature may refer to a kinematic chain used in computer animation to simulate the motion of a virtual human or virtual animal character (eg, virtual avatar). Various types of virtual armatures can be used, such as keyframing (stop motion) armatures and real-time (manipulating) armatures.

At 41104, the processing device may obtain data associated with the virtual avatar. Data associated with the virtual avatars may include which virtual avatars are selected by the patient and/or medical personnel, such selections being made to guide the patient through the exercise session. In some embodiments, data associated with the virtual avatar may include an identifier associated with the virtual avatar. The identifier may be used to retrieve data associated with the virtual avatar from the database. For example, the patient may have selected a superhero as the patient's virtual avatar. Accordingly, data regarding a particular superhero (eg, gender, outfit, appearance, etc.) may be obtained from the database.

At 41106, the processing device may map data associated with the virtual avatar onto a virtual model that animates one or more movements associated with the exercise session. For example, the appearance and shape of the virtual avatar is mapped onto the mesh of the virtual model (e.g., facing the head portion of the mesh) and manipulated and/or animated according to instructions associated with the exercise session and/or the virtual avatar. obtain. In some embodiments, the virtual avatar may use therapy device 4070 to perform one or more exercises. Exercise performance by the virtual avatar may be animated and/or presented on the display screen of the patient's computing device to guide the patient through the exercise session. As disclosed herein, at any time the virtual avatar may be replaced and/or paused to allow presentation of the healthcare worker's multimedia feed.

FIG. 47 illustrates an exemplary computer system 41200 capable of implementing any one or more of the methods described herein, in accordance with one or more aspects of the disclosure. In one example, the computer system 41200 includes a computing device, a support interface 4094, a reporting interface 4092, a supervisory interface 4090, a clinician interface 4020, a server 4030 (including AI engine 4011), a patient interface 4050, a gait sensor 4082. , goniometer 4084, therapy device 4070, pressure sensor 4086, or any suitable component of FIG. Computer system 41200 may be capable of executing instructions implementing one or more machine learning models 4013 of artificial intelligence engine 4011 of FIG. The computer system may be connected (eg, networked) to other computer systems within a LAN, intranet, extranet, or Internet, including via a cloud or peer-to-peer network. A computer system can operate in the capacity of a server in a client-server network environment. Computer systems include personal computers (PCs), tablet computers, wearables (e.g., wristbands), set-top boxes (STBs), personal digital assistants (PDAs), mobile phones, cameras, camcorders, Internet of Things (IoT) devices. , or any device capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that device. Further, although only a single computer system is illustrated, the term "computer" can also refer to a set of instructions (or sets of instructions) for performing any one or more of the methods discussed herein. ), individually or jointly, to include any collection of computers.

The computer system 41200 includes a processing device 41202, a main memory 41204 (e.g., dynamic random memory such as read only memory (ROM), flash memory, solid state drive (SSD), synchronous DRAM (SDRAM), etc.) that communicate with each other via bus 41210. access memory (DRAM)), static memory 41206 (eg, flash memory, solid state drive (SSD), static random access memory (SRAM)), and data storage device 41208.

Processing device 41202 represents one or more general purpose processing devices such as microprocessors, central processing units, and the like. More specifically, the processing device 41202 implements a Complex Instruction Set Computing (CISC) microprocessor, Reduced Instruction Set Computing (RISC) microprocessor, Very Long Instruction Word (VLIW) microprocessor, or other instruction set. , or a processor that implements a combination of instruction sets. Processing device 41202 may also be one or more dedicated processing devices such as an application specific integrated circuit (ASIC), system-on-chip, field programmable gate array (FPGA), digital signal processor (DSP), network processor, etc. may Processing device 41202 is configured to execute instructions to perform any of the operations and steps discussed herein.

Computer system 41200 may further include network interface device 41212 . Computer system 41200 also includes a video display 41214 (e.g., liquid crystal display (LCD), light emitting diode (LED), organic light emitting diode (OLED), quantum LED, cathode ray tube (CRT), shadow mask CRT, aperture grill CRT, monochrome). CRT), one or more input devices 41216 (eg, keyboard and/or mouse, or game-like controls), and one or more speakers 41218 (eg, speakers). In one illustrative example, the video display 41214 and input device 41216 may be combined into a single component or device (eg, LCD touchscreen).

Data storage devices 41216 may include computer readable media 41220 having instructions 41222 stored thereon that embody any one or more of the methods, acts, or functions described herein. Instructions 41222 may also reside completely or at least partially within main memory 41204 and/or processing device 41202 during execution of instructions 41222 by computer system 41200 . Thus, main memory 41204 and processing device 41202 also constitute computer-readable media. Instructions 41222 may also be transmitted or received over a network via network interface device 41212 .

Although computer-readable storage medium 41220 is shown in the illustrative embodiment to be a single medium, the term "computer-readable storage medium" refers to a single medium that stores one or more sets of instructions. or multiple media (eg, centralized or distributed databases and/or associated caches and servers). The term "computer-readable storage medium" also means any medium capable of storing, encoding or carrying a set of instructions for execution by a machine and causing the machine to perform any one or more of the methodologies of the present disclosure. shall be construed to include the medium of Accordingly, the term "computer-readable storage medium" shall be taken to include, but not be limited to, solid state memory, optical media, and magnetic media.

Article 65. A computer-implemented system comprising:
a therapy device configured to be operated by a patient while performing an exercise session;
A patient interface configured to receive a virtual avatar, the patient interface comprising an output device configured to present the virtual avatar, the virtual avatar using a virtual representation of the therapy device to perform exercise a patient interface that guides the patient through the session and the virtual avatar is associated with a healthcare professional;
A server computing device,
providing a patient interface with a virtual avatar of the patient;
receiving from the patient interface a message about the triggering event, the message including a severity level of the triggering event;
determining whether the severity level of the trigger event exceeds a threshold severity level;
Replacing presentation of the virtual avatar on the patient interface with presentation of a multimedia feed from the healthcare worker's computing device in response to determining that the severity level of the triggering event exceeds the threshold severity level. and a server computing device configured to perform:

Article 66. A server computing device
In response to determining that the severity level of the triggering event does not exceed the threshold severity level,
Providing control of a virtual avatar to a healthcare worker's computing device so that the healthcare professional can remotely control, provide, or control the virtual avatar to interact with the patient. to the healthcare worker's computing device, whereby the healthcare professional further remotely controls and continues to provide a virtual avatar to interact with the patient A computer-implemented system according to any clause herein, wherein:

Article 67. The virtual avatar can, in real time or near real time,
receiving input including sensor data, patient characteristics, real-time feedback from the patient or other patients, or some combination thereof;
A computer-implemented system according to any clause herein, controlled by one or more machine learning models trained to generate output to control a virtual avatar.

Article 68. Providing virtual avatars
obtaining data associated with the exercise session, the data including instructions implementing a virtual model that animates one or more movements associated with the exercise session;
obtaining data associated with the virtual avatar;
A computer implementation according to any clause herein, further comprising mapping data associated with the virtual avatar onto a virtual model that animates one or more movements associated with the exercise session. system.

Article 69. Prior to serving the virtual avatar, the server computing device:
transmitting to the patient interface a notification to initiate an exercise session, the notification being transmitted based on a schedule specified in the treatment plan;
receiving from the patient interface a selection to initiate an exercise session to use the therapy device;
transmitting a control signal to the therapy device to cause the therapy device to initiate an exercise session;
A computer-implemented system according to any clause herein, further adapted to: provide a virtual avatar to the patient interface in response to transmitting the control signal.

Clause 70. A computer-implemented system according to any clause herein, wherein notifications comprise push notifications, text messages, phone calls, emails, or any combination thereof.

Article 71. A server computing device
determining an exercise session to be performed based on a second treatment plan for a second patient, the exercise session performed by the second patient using a second therapy device;
presenting on the second patient interface of the second patient a virtual avatar configured to guide the patient through the exercise session to use the therapy device;
The presentation of the virtual avatar is replaced on the patient interface by the presentation of multimedia feeds from the healthcare worker's computing device, while the virtual avatar remains presented on a second patient interface, or the virtual The presentation of the avatar is replaced on the patient interface by the presentation of the multimedia feed from the healthcare worker's computing device, while the virtual avatar is replaced on the second patient interface by the presentation of the multimedia feed from the healthcare worker's computing device. A computer-implemented system according to any clause herein replaced by a multimedia feed.

Article 72. A server computing device
receiving a selection of a virtual avatar from a library of virtual avatars from the patient interface;
Storing in a database the virtual avatar associated with the patient.

Article 73. A method according to any clause herein, wherein the virtual avatar is configured to guide the patient through the exercise session using audio, video, haptic feedback, or some combination thereof.

Article 74. a method,
Providing the patient's computing device with a virtual avatar to be presented on the patient's computing device, the virtual avatar using a virtual representation of the therapy device to guide the patient through the exercise session. the virtual avatar is associated with a healthcare worker, configured to provide;
receiving a message about the trigger event from the patient's computing device;
determining whether the severity level of the trigger event exceeds a threshold severity level;
Presenting a virtual avatar on the patient's computing device and presenting a multimedia feed from the healthcare worker's computing device in response to determining that the severity level of the triggering event exceeds the threshold severity level. and replacing with.

Article 75. In response to determining that the severity level of the triggering event does not exceed the threshold severity level,
Providing control of a virtual avatar to a healthcare worker's computing device so that the healthcare professional can remotely control, provide, or control the virtual avatar to interact with the patient. to the healthcare professional's computing device, whereby the healthcare professional remotely controls and provides a virtual avatar to interact with the patient , a method as described in any clause herein.

Article 76. The virtual avatar can, in real time or near real time,
receiving input including sensor data, patient characteristics, real-time feedback from the patient or other patients, or some combination thereof;
A method according to any clause herein, controlled by one or more machine learning models trained to generate an output that controls a virtual avatar.

Article 77. Providing virtual avatars
obtaining data associated with the exercise session, the data including instructions implementing a virtual model that animates one or more movements associated with the exercise session;
obtaining data associated with the virtual avatar;
A method according to any clause herein, further comprising mapping data associated with the virtual avatar onto a virtual model that animates one or more movements associated with the exercise session.

Article 78. Prior to providing the virtual avatar, the method
transmitting to the patient's computing device a notification to start an exercise session, the notification being transmitted based on a schedule specified in the treatment plan;
receiving a selection from the patient's computing device to initiate an exercise session to use the therapy device;
transmitting a control signal to the therapy device to cause the therapy device to initiate an exercise session;
The method according to any clause herein, further comprising providing a virtual avatar to the patient's computing device in response to transmitting the control signal.

Article 79. A method according to any clause herein, wherein the notification comprises a push notification, text message, phone call, email, or any combination thereof.

Article 80. determining an exercise session to be performed based on a second treatment plan for a second patient, the exercise session performed by the second patient using a second therapy device;
presenting on a second computing device of a second patient a virtual avatar configured to guide the patient through an exercise session to use the therapy device;
The presentation of the virtual avatar is replaced on the patient's computing device with the presentation of the multimedia feed from the healthcare worker's computing device, while the virtual avatar remains presented on the second computing device. or The presentation of the virtual avatar is replaced on the patient's computing device by the presentation of a multimedia feed from the healthcare worker's computing device, while the virtual avatar is presented on the second computing device by the medical A method according to any clause herein that is replaced by a multimedia feed from the employee's computing device.

Article 81. receiving a selection of a virtual avatar from a library of virtual avatars from the patient's computing device;
A method according to any clause herein, further comprising storing the virtual avatar associated with the patient in a database.

Article 82. A method according to any clause herein, wherein the virtual avatar is configured to guide the patient through the exercise session using audio, video, haptic feedback, or some combination thereof.

Article 83. A method according to any clause herein, wherein the message comprises data relating to patient pain levels, patient characteristics, sensor readings, or some combination thereof.

Article 84. Replacing a virtual avatar with a multimedia feed initiates a telemedicine session between a patient and a healthcare professional and the method is
receiving a second message from the patient or healthcare worker computing device indicating that the telemedicine session is complete;
replacing the presentation of the multimedia feed with the presentation of a virtual avatar on the patient's computing device, the virtual avatar configured to continue to guide the patient through the exercise session to completion; The method according to any clause herein, further comprising

Article 85. Any of the methods herein further comprising determining an exercise session to be performed based on the patient's treatment plan, wherein the treatment device is configured for use by the patient performing the exercise session. the method described in paragraph 1.

Article 86. A non-transitory, tangible computer-readable medium storing instructions that, when executed, cause a processing device to:
Providing the patient's computing device with a virtual avatar to be presented on the patient's computing device, the virtual avatar using a virtual representation of the therapy device to guide the patient through the exercise session. the virtual avatar is associated with a healthcare worker, configured to provide;
receiving a message about the trigger event from the patient's computing device;
determining whether the severity level of the trigger event exceeds a threshold severity level;
Presenting a virtual avatar on the patient's computing device and presenting a multimedia feed from the healthcare worker's computing device in response to determining that the severity level of the triggering event exceeds the threshold severity level. A computer-readable medium for replacing with and causing to be performed.

Article 87. In response to determining that the severity level of the triggering event does not exceed the threshold severity level, the processing device:
Providing control of a virtual avatar to a healthcare worker's computing device so that the healthcare professional can remotely control, provide, or control the virtual avatar to interact with the patient. to the healthcare worker's computing device, whereby the healthcare professional further remotely controls and continues to provide a virtual avatar to interact with the patient A computer-readable medium according to any clause of this specification, wherein the computer-readable medium comprises:

Article 88. The virtual avatar can, in real time or near real time,
receiving input including sensor data, patient characteristics, real-time feedback from the patient or other patients, or some combination thereof;
A computer-readable medium according to any clause herein, controlled by one or more machine learning models trained to generate output that controls a virtual avatar.

Article 89. Providing virtual avatars
obtaining data associated with the exercise session, the data including instructions implementing a virtual model that animates one or more movements associated with the exercise session;
obtaining data associated with the virtual avatar;
The computer readable according to any clause herein, further comprising mapping data associated with the virtual avatar onto a virtual model that animates one or more movements associated with the exercise session. medium.

Article 90. Prior to providing the virtual avatar, the processing device
transmitting to the patient's computing device a notification to start an exercise session, the notification being transmitted based on a schedule specified in the treatment plan;
receiving a selection from the patient's computing device to initiate an exercise session to use the therapy device;
transmitting a control signal to the therapy device to cause the therapy device to initiate an exercise session;
The computer-readable medium according to any clause herein, further adapted to provide a virtual avatar to the patient's computing device in response to transmitting the control signal.

Article 91. a system,
a memory device storing instructions;
a processing device communicatively coupled to the memory device, the processing device executing the instructions to
Providing the patient's computing device with a virtual avatar to be presented on the patient's computing device, the virtual avatar using a virtual representation of the therapy device to guide the patient through the exercise session. the virtual avatar is associated with a healthcare worker, configured to provide;
receiving a message about the trigger event from the patient's computing device;
determining whether the severity level of the trigger event exceeds a threshold severity level;
Presenting a virtual avatar on the patient's computing device and presenting a multimedia feed from the healthcare worker's computing device in response to determining that the severity level of the triggering event exceeds the threshold severity level. The system that does and replaces with .

Article 92. In response to determining that the severity level of the triggering event does not exceed the threshold severity level, the processing device:
Providing control of a virtual avatar to a healthcare worker's computing device so that the healthcare professional can remotely control, provide, or control the virtual avatar to interact with the patient. to the healthcare worker's computing device, whereby the healthcare professional further remotely controls and continues to provide a virtual avatar to interact with the patient A system according to any clause herein, wherein:

Article 93. The processing device
obtaining data associated with the exercise session, the data including instructions implementing a virtual model that animates one or more movements associated with the exercise session;
obtaining data associated with the virtual avatar;
any clause herein further adapted to map data associated with the virtual avatar onto a virtual model that animates one or more movements associated with the exercise session. The system described in .

Article 94. Prior to providing the virtual avatar, the processing device
transmitting to the patient's computing device a notification to start an exercise session, the notification being transmitted based on a schedule specified in the treatment plan;
receiving a selection from the patient's computing device to initiate an exercise session to use the therapy device;
transmitting a control signal to the therapy device to cause the therapy device to initiate an exercise session;
The system according to any clause herein, further adapted to provide a virtual avatar to the patient's computing device in response to transmitting the control signal.

Various aspects, embodiments, implementations or features of the described embodiments may be used separately or in any combination. The embodiments disclosed herein are modular in nature and can be used in conjunction with or combined with other embodiments.

Consistent with the disclosure above, the examples of assemblies listed in the following clauses are specifically contemplated and are intended as a non-limiting set of examples.

Claims (30)

A computer-implemented system comprising:
a therapy device configured to be operated by a user while the user performs a treatment plan, the therapy device comprising at least one pedal;
a patient interface associated with the therapy device, the patient interface comprising an output configured to present telemedicine information associated with a telemedicine session;
a computing device,
Receiving therapy data relating to the user using the therapy device to implement the treatment plan, wherein the therapy data includes characteristics of the user, characteristics of the user while the user is using the therapy device. receiving, including at least one of measurement information, characteristics of the treatment device, and at least one aspect of the treatment plan;
generating therapy information using the therapy data;
writing the therapy information to an associated memory for access at a healthcare provider's computing device;
communicating with an interface at the computing device of the healthcare provider, the interface configured to receive treatment plan input;
said at least one aspect of said treatment plan in response to receiving treatment plan input including at least one modification to at least one of said at least one aspect and any other aspect of said treatment plan; and a computing device configured to modify said at least one of and any other aspect. the computing device, during use of the treatment device by the user and based on the modified at least one of the at least one aspect and any other aspects of the treatment plan; 3. The computer-implemented system of claim 1, further configured to control the therapeutic device. The computing device updates the modified at least one of the at least one aspect and any other aspects of the treatment plan during the user's use of the therapy device during the telemedicine session. 2. The computer implemented system of claim 1, further configured to control the therapeutic device based on one. The computer of claim 1, wherein the measurement information includes at least one of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, and the user's blood pressure. implementation system. 3. The computer-implemented system of Claim 1, wherein at least some of the therapy data corresponds to at least some sensor data from a sensor associated with the therapy device. 2. The method of claim 1, wherein at least some of the therapy data corresponds to at least some sensor data from sensors associated with a wearable device worn by the user while the user uses the therapy device. computer-implemented system. a method,
receiving treatment data about a user using a treatment device to implement a treatment plan, said treatment data comprising characteristics of said user, measurement information about said user while said user is using said treatment device; receiving, including at least one of characteristics of the treatment device and at least one aspect of the treatment plan;
generating therapy information using the therapy data, wherein the therapy device comprises a stationary cycling machine ;
writing the therapy information to an associated memory for access at a healthcare provider's computing device;
communicating with an interface at the computing device of the healthcare provider, the interface configured to receive treatment plan input;
said at least one aspect of said treatment plan in response to receiving treatment plan input including at least one modification to at least one of said at least one aspect and any other aspect of said treatment plan; and any other aspect. controlling the treatment device while the user uses the treatment device and based on the modified at least one of the at least one aspect and any other aspect of the treatment plan; 8. The method of claim 7 , further comprising: during the user's use of the therapy device during the telemedicine session and based on the modified at least one of the at least one aspect and any other aspects of the treatment plan; 8. The method of claim 7, further comprising controlling said therapeutic device. 8. The method of claim 7 , wherein the measurement information includes at least one of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, and the user's blood pressure. . 8. The method of claim 7 , wherein at least a portion of said therapy data corresponds to at least a portion of said sensor data from a sensor associated with said therapy device. 8. The method of claim 7 , wherein at least a portion of the therapy data corresponds to at least a portion of the sensor data from a sensor associated with a wearable device worn by the user while the user uses the therapy device. described method. 8. The method of claim 7 , further comprising receiving subsequent therapy data for the user while the user uses the therapy device to implement the therapy plan. in response to receiving subsequent treatment plan input including at least one further modification to the modified at least one of the at least one aspect and any other aspect of the treatment plan; further comprising modifying the modified at least one of the at least one aspect and any other aspect of the treatment plan, wherein the subsequent treatment plan input comprises the treatment data and the subsequent treatment plan input; 14. The method of claim 13 , based on at least one of: therapeutic data. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
receiving treatment data about a user using a treatment device to implement a treatment plan, said treatment data comprising characteristics of said user, measurement information about said user while said user is using said treatment device; at least one of properties of the treatment device and at least one aspect and any other aspect of the treatment regimen , the treatment device including at least one pedal , receiving and
generating therapy information using the therapy data;
writing the therapy information to an associated memory for access at a healthcare provider's computing device;
communicating with an interface at the computing device of the healthcare provider, the interface configured to receive treatment plan input;
said at least one aspect of said treatment plan in response to receiving treatment plan input including at least one modification to said at least one of said at least one aspect of said treatment plan and any other aspect of said treatment plan; modifying said at least one of aspects and any other aspects. The processing device, during use of the treatment device by the user, and based on the modified at least one of the at least one aspect and any other aspects of the treatment plan, 16. The computer readable medium of Claim 15 , further configured to control a therapeutic device. The processing device controls the modified at least one of the at least one aspect and any other aspect of the treatment plan and during the user's use of the treatment device during the telemedicine session. 16. The computer readable medium of claim 15 , further configured to control the treatment device based on: 16. The computer of claim 15 , wherein the measurement information includes at least one of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, and the user's blood pressure. readable medium. 16. The computer-readable medium of Claim 15 , wherein at least some of the therapy data corresponds to at least some sensor data from sensors associated with the therapy device. 16. The method of claim 15 , wherein at least some of the therapy data corresponds to at least some sensor data from sensors associated with a wearable device worn by the user while the user uses the therapy device. computer readable medium. 16. The computer readable of claim 15 , wherein the processing device is further configured to receive subsequent therapy data regarding the user while the user uses the therapy device to implement the treatment plan. medium. The processing device receiving subsequent treatment plan input including at least one further modification to the modified at least one of the at least one aspect and any other aspect of the treatment plan. further configured to modify the modified at least one of the at least one aspect and any other aspect of the treatment plan in response to the subsequent treatment plan input of 22. The computer-readable medium of claim 21 , based on at least one of: , the treatment data and the subsequent treatment data. a system,
a memory device storing instructions;
a processing device communicatively coupled to the memory device, the processing device executing the instructions to
receiving treatment data about a user using a treatment device to implement a treatment plan, said treatment data comprising characteristics of said user, measurement information about said user while said user is using said treatment device; and at least one of said at least one aspect and any other aspect of said treatment regimen , said characteristics of said treatment device comprising said receiving, including at least a resistance setting of at least one component of the treatment device ;
generating therapy information using the therapy data;
writing the therapy information to at least one of the memory device and another associated memory device for access at a healthcare provider's computing device;
communicating with an interface at the computing device of the healthcare provider, the interface configured to receive treatment plan input;
at least one of said at least one aspect and any other aspect of said treatment regimen to said at least one of said at least one aspect and any other aspect of said treatment regimen; modifying in response to receiving a treatment plan input including one modification. The processing device, during use of the treatment device by the user, and based on the modified at least one of the at least one aspect and any other aspects of the treatment plan, 24. The system of claim 23 , further configured to control a therapeutic device. The processing device controls the modified at least one of the at least one aspect and any other aspect of the treatment plan and during the user's use of the treatment device during the telemedicine session. 24. The system of claim 23 , further configured to control the therapeutic device based on: 24. The system of claim 23 , wherein the measurement information includes at least one of the user's vital signs, the user's respiratory rate, the user's heart rate, the user's body temperature, and the user's blood pressure. . 24. The system of Claim 23 , wherein at least some of the therapy data corresponds to at least some sensor data from a sensor associated with the therapy device. 24. The method of claim 23 , wherein at least some of the therapy data corresponds to at least some sensor data from sensors associated with a wearable device worn by the user while the user uses the therapy device. system. 24. The system of claim 23 , wherein the processing device is further configured to receive subsequent therapy data regarding the user while the user uses the therapy device to implement the treatment plan. The processing device receiving subsequent treatment plan input including at least one further modification to the modified at least one of the at least one aspect and any other aspect of the treatment plan. further configured to modify the modified at least one of the at least one aspect and any other aspect of the treatment plan in response to the subsequent treatment plan input of , the treatment data and the subsequent treatment data.

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