WO2018101935A1 - Moteur de modification de protocole d'assurance - Google Patents

Moteur de modification de protocole d'assurance Download PDF

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Publication number
WO2018101935A1
WO2018101935A1 PCT/US2016/064244 US2016064244W WO2018101935A1 WO 2018101935 A1 WO2018101935 A1 WO 2018101935A1 US 2016064244 W US2016064244 W US 2016064244W WO 2018101935 A1 WO2018101935 A1 WO 2018101935A1
Authority
WO
WIPO (PCT)
Prior art keywords
patient
treatment
information
therapy
identifying
Prior art date
Application number
PCT/US2016/064244
Other languages
English (en)
Inventor
John Cronin
Seth Melvin CRONIN
Patrick Walsh
Joseph George BODKIN
Angus KNOWLES
Jeff Padgett
Michael Glynn D'ANDREA
Original Assignee
Ipcreate, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ipcreate, Inc. filed Critical Ipcreate, Inc.
Priority to PCT/US2016/064244 priority Critical patent/WO2018101935A1/fr
Publication of WO2018101935A1 publication Critical patent/WO2018101935A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising

Definitions

  • the present invention is generally directed to systems and method for cross identifying factors that may benefit a patient. More specifically, the present invention identifies actions that are most likely to result in a desired patient benefit. Description of the Related Art:
  • the presently claimed invention relates to systems and methods for identifying best methods for treating a specific patient based on ailments, conditions, or symptoms that correspond to attribute information related to the specific patient.
  • a method of the presently claimed invention may receive information regarding a patient that may relate to an attribute associated with the patient and with a medical issue that the patient may be suffering from. Methods of the presently claimed invention may also receive information from a database that corresponds to the patient medical issue, identify a probability associated with a treatment regimen based on a correspondence of the patient medical issue with at least some of the received information, identify a preferred treatment regimen to recommend to a caregiver based on the probability associated with the treatment regimen, and provide the preferred treatment regimen to a caregiver for administration the patient.
  • FIG. 1 illustrates an exemplary healthcare treatment network communicating with medical information networks and with insurance company networks when performing steps consistent with a method of the present disclosure.
  • FIG. 2 illustrates an exemplary flow chart that may be executed by a patient attribute therapy outcome correlating software module when identifying recommended treatment regimens by systems consistent with the present disclosure.
  • FIG. 3 illustrates two different sets of best fit curves that correspond to likelihoods of success or failure of two differing treatment regimens versus a patient's age.
  • FIG. 4 illustrates steps that may be performed by an exemplary treatment adjustment authorization software module consistent with the present disclosure.
  • FIG. 5 illustrates an exemplary series of steps that may be performed by a treatment authorization software module consistent with the present disclosure.
  • FIG. 6 illustrates and exemplary charts of information that may be reviewed by an insurance company.
  • FIG. 7 illustrates exemplary treatment charts that may include information used to identify treatment recommendations from an insurance company.
  • FIG. 8 illustrates an exemplary table that may be stored in a database that may be referenced when systems of the present disclosure correlate how likely a particular treatment therapy will meet or not meet a desired treatment outcome.
  • FIG. 9 illustrates an exemplary computing system that may be used to implement all or a portion of a device for use with the present technology.
  • the present disclosure is directed to systems and methods for identifying best methods for treating a specific patient based on ailments, conditions, or symptoms that correspond to attribute information related to the specific patient.
  • Methods and systems consistent with the present disclosure may associate probabilities that a particular treatment is likely to provide or that is not likely to provide specific benefits to the specific patient.
  • Methods and systems of the present disclosure may also provide alternate treatment regimens to caregivers when those alternate treatment regimens are associated with a success level that is
  • FIG. 1 illustrates an exemplary healthcare treatment network
  • FIG. 1 includes a healthcare facility network communicating over the internet with medical information network and insurance company networks.
  • FIG. 1 also illustrates the health care facility network communicating with an electronic device operated by a caregiver (i.e. a caregiver device).
  • the insurance company networks of FIG. 1 may include computers that store and retrieve data from the insurance therapy database of FIG. 1.
  • the medical information network of FIG. 1 includes a historical patient profile database and a medical research efficacy database.
  • the healthcare facility network of FIG. 1 includes a real-time electronic health record database, a patient attribute therapy outcome correlation database, a patient attribute therapy outcome correlation software, and treatment adjustment authorization software & authorization module.
  • a patient attribute therapy outcome correlation database e.g., a patient attribute therapy outcome correlation software
  • treatment adjustment authorization software & authorization module e.g., a patient attribute therapy outcome correlation software
  • information that relates directly a patient or indirectly via a patient's similarity with other patients may be used when identifying appropriate treatments.
  • insurance information may be collected from remote sources when a healthcare facility network identifies treatments that may best service the needs of a patient.
  • data stored locally may be combined with data received from remote sources when identifying best effective patient treatment recommendations and when providing those treatment recommendations to a caregiver via a caregiver device therapy recommendation GUI.
  • FIG. 2 illustrates an exemplary flow chart that may be executed by a patient attribute therapy outcome correlating software module when identifying recommended treatment regimens by systems consistent with the present disclosure.
  • Step 205 of the flow chart of FIG. 2 may poll an electronic health records EHR database for patient event data. After the EHR database is polled for information, new information may be received from the EHR database in step 210 of FIG. 2.
  • Step 215 may then cross reference individual hospital patient data with the newly received evet data when associating particular patients to specific data events.
  • Step 220 may then identify a list of medications, provide a set of recommended treatments, and identify possible diagnoses that should be considered when identifying one or more possible treatment regimens for specific patients at the hospital.
  • the patient attribute therapy outcome correlating software may then retrieve patient data that is resident at a historic patient profile database in step 225 of FIG. 2, and retrieve medications, treatments, or diagnosis from a medical research efficacy database that may be relevant to identifying treatments that may be effective or that may not be effective for treating specific ailments of specific patients that appear to be relevant in step 230 of FIG. 2.
  • step 235 of FIG. 2 may perform calculations and associations that relate to a first attribute of a plurality of attributes of a patient. Information relating to that first attribute may be stored in a patient attribute therapy outcome correlation database in step 240. This may be done after the calculations and associations of step 235 have been performed.
  • steps 235 and 240 of FIG. 2 may be repeated iteratively for a series of different patient attributes when calculating likelihoods of success or of failure.
  • steps 245 and step 255 include similar calculations and associations as step 235 of FIG. 2.
  • steps 250 and 260 also store information resulting from an immediately preceding calculation/association step like step 240 of FIG. 2.
  • program flow moves from step 235 to step 240, then to step 245, 250, 255, and to step 360.
  • step 360 program flow moves from step 360 back to step 305 where the steps of FIG. 2 may be repeated.
  • a first patient attribute is age and a second patient attribute is white blood cell count when identifying likelihoods that a particular treatment will be successful.
  • the patient's age is a factor when identifying a likelihood that a treatment will not be successful.
  • calculations and associations may be performed that relate to the patient's age and to the patient's white blood cell count may be performed when identifying overall likelihood that a treatment will be successful or not.
  • the calculations and associations performed in steps 235, 245, and 255 may identify how likely the application of an external beam radiotherapy treatment will result in a greater than (>) 20% decrease in a measure of prostate specific antigen (PSA) for a given patient attribute when forecasting a success probability.
  • PSA prostate specific antigen
  • a forecasted goal associated with this treatment may correspond to decreasing a measure of PSA by more than 20%.
  • the calculations and associations of steps 235, 245, and 255 may include associating patient attribute data with a best fit curve and may include performing calculations and identifying correlation coefficients that may be used when likelihoods of treatment success or failure are identified.
  • the data stored in steps 240, 250, and 260 may be stored only when certain conditions are met.
  • data derived from steps 235, 245, and 255 may be stored in the patient attribute therapy outcome correlation database only when there is at least an 80% chance that a particular therapy will be successful.
  • data may be written to the database for each attribute evaluated.
  • data written to a database may correspond to a likelihood of the treatment failing to reach a certain probability threshold.
  • FIG. 3 illustrates two different sets of best fit curves that correspond to likelihoods of success or failure of two differing treatment regimens versus a patient's age. These two different best fit curves graph include several data points that are indicated by various geometric shapes (i.e. the circles, diamond, pentagon, square, triangle, and hexagon shapes). Note that a first best fit curve of FIG. 3 corresponds to a primary treatment regimen where a patient receives external beam radiotherapy treatment for reducing a measure of a prostate specific antigen (PSA). A vertical axis of the first best fit curve of FIG. 3 relates to patient age and a horizontal axis of this cure relates to a measured percentage reduction of PSA.
  • PSA prostate specific antigen
  • the first best fit curve that relates to the primary treatment of external beam radiotherapy may have been associated with patient data retrieved from a plurality of different databases. Note that this best fit curve indicates that the effectivity of external beam radiotherapy increases with patient age, as such, patients of greater age are more likely to receive benefit from external beam radiotherapy treatment. Note also that patient 1 has an age of 36 years old and has a relatively low likelihood of receiving significant benefit from receiving external beam radiotherapy.
  • This first best fit curve may correspond to best fit factors, such as, an R squared factor, an R squared factor or a 1/(R squared).
  • the second best fit curve of FIG. 3 corresponds to patient data for patients receiving a treatment that is alternate to external beam radiotherapy.
  • the vertical axis of this alternate treatment best fit curve also corresponds to patient age, yet the horizontal axis corresponds to a measured percentage reduction of PSA after receiving the alternate treatment.
  • the second best fit curve indicates that the likelihood that the alternate treatment will be successful reduces with age.
  • the data presented in the second best fit curve indicates that alternate treatment regimen should provide patient 1 with a greater chance of success as the primary treatment regimen of external beam radiotherapy.
  • FIG. 4 illustrates steps that may be performed by an exemplary treatment adjustment authorization software module consistent with the present disclosure.
  • step 4 begins with a first step 410 where an electronic health records (EHR) system may be polled for information relating to a diagnosis event.
  • EHR electronic health records
  • Next step 415 may receive data relating to the diagnosis event from the EHR in real time, after which data relating to one or more identified patients may also be retrieved in step 420.
  • Step 425 of FIG. 4 may then retrieve guidelines relating to the diagnosis event from an insurance therapy database, after which a patient attribute therapy outcome correlation database may be queried for patient information in step 430.
  • Step 430 of FIG. 4 may also identify fields of patient information that correspond to factors that affect predicting that a desired result is likely not to be achieved by a particular therapy.
  • step 430 program flow moves to determination step 435 that identifies if the received patient information corresponds to the particular therapy not achieving the desired results, when no, program flow moves to step 440 where the particular therapy is sent for display on a therapy recommendation GUI.
  • step 435 identifies that the particular therapy includes data indicating that the particular therapy will not provide the desired results, program flow, the method moves to step 445.
  • Step 445 of the flow chart of FIG. 4 may then compare patient information to patient predictive data from a patient attribute therapy outcome correlation database that relate to predicting whether a desired outcome is likely or not.
  • determination step 440 may identify whether the patient predictive data affects the likelihood of an identified therapy not achieving a desired outcome, when no, program flow moves to step 440 where the identified therapy may be output on the GUI.
  • step 440 When determination step 440 identifies that the patient probability data indicates that the identified treatment will likely not achieve the desired result, program flow flows from step 450 to determination step 455. Determination step 455 may then identify whether an alternative treatment is likely to reach the desired results, when yes, program flow moves to step 465 where the alternative treatment may be submitted to an authorization software module for further evaluation.
  • step 455 identifies that the alternative treatment regimen is not likely to result in the desired results
  • program flow may move to step 460.
  • Step 460 may then identify whether any other patient specific information helps predict whether yet another treatment may be effective in reaching the desired result.
  • program flow may move from step 460 to step 465 where that yet another treatment may be provided to the authorization software module for further evaluation.
  • step 460 When step 460 identifies that there are no other alternative treatment regimens, program flow may move to step 440 where a more conventional therapy (such as the original "standard" treatment) may be sent to the recommendation GUI of a caregiver.
  • a more conventional therapy such as the original "standard” treatment
  • FIG. 5 illustrates an exemplary series of steps that may be performed by a treatment authorization software module consistent with the present disclosure.
  • a first step 510 of the flow chart of FIG. 5 may receive an alternate treatment protocol
  • next step 520 may compare the cost of a standard treatment protocol with the alternate treatment protocol when identifying a cost delta between the two different treatment protocols.
  • step 530 may send information relating to the patient and information relating to probabilities of not achieving a desired result from the standard therapy, such that, the identified cost delta may be reviewed for approval.
  • Program flow then moves to determination step 540 that identifies whether the patient's insurance company approves the alternative treatment.
  • the alternate treatment protocol may be sent to the therapy recommendation GUI of a caregiver in step 550 of FIG. 5.
  • the standard treatment protocol may be sent to the therapy recommendation GUI of the caregiver in step 560 of FIG. 5.
  • FIG. 6 illustrates and exemplary charts of information that may be reviewed by an insurance company.
  • FIG. 6 also illustrates treatment regimens that may be changed based on actions performed by a treatment authorization software module of a patient's insurance company.
  • the first chart of information in FIG. 6 illustrates therapeutic steps that may be provided to patient Peter Parker that is suffering from prostate cancer.
  • each chart identifies that the patient should be watched (active surveillance), that interstitial prostate brachytherapy should be provided to the patient, and that a radical prostatectomy should be performed on the patient.
  • the first chart of FIG. 6 indicates that changes in the patient's treatment protocol have not been approved by the patient's insurance company, where external beam radiotherapy should still be performed despite an 89% chance of failure.
  • the second chart of FIG. 6 indicates that the patient's insurance company has indicated that the external beam radiotherapy should be skipped after the insurance company has approved a change in the patient's therapy.
  • FIG. 7 illustrates exemplary treatment charts that may include information used to identify treatment recommendations from an insurance company. Notice that in a first instance an alternate treatment of proton beam therapy was not approved by an insurance company in a first chart of FIG. 7. Note also that in a second chart of FIG. 7 that the alternate treatment of proton beam therapy was approved by the patient's insurance company.
  • FIG. 8 illustrates an exemplary table that may be stored in a database that may be referenced when systems of the present disclosure correlate how likely a particular treatment therapy will meet or not meet a desired treatment outcome.
  • the table of FIG. 8 may be stored in a databased like the patient attribute therapy outcome correlation database of FIG. 1.
  • the table of FIG. 8 includes columns that identify different therapies, diagnoses, patient attributes, functions for forecasting how likely a treatment will yield a desired percentage reduction in patient symptoms over time, and correlating factors, such as an R squared value or other offsetting factors.
  • Therapies included in FIG. 8 include methylphenidate for treating attention deficit hyperactivity disorder (ADHD), short-acting 2 agonists (SABA) for treating asthma, risedronate for treating osteoporosis, and rebif for treating multiple sclerosis that are associated with a treatment therapy and a diagnosis.
  • patient attributes include an ADHD patient age of 8, a measure of forces expiratory volume per a measure of forced vital capacity (i.e. FEV1/FVC) of 73%, a body mass index (BMI) of 41, and a resting heart rate of 73 beats per minute.
  • Each of the treatment therapies listed in the table of FIG. 8 are also associated with a function (i.e. a best fit curve) and an R squared value or coefficient that may be used to forecast patient response factors that correspond to how likely a particular treatment therapy will reach a treatment result goal.
  • a function i.e. a best fit curve
  • an R squared value or coefficient that may be used to forecast patient response factors that correspond to how likely a particular treatment therapy will reach a treatment result goal.
  • FIG. 9 illustrates an exemplary computing system that may be used to implement all or a portion of a device for use with the present technology.
  • the computing system 900 of FIG. 9 includes one or more processors 910 and memory 920.
  • Main memory 920 stores, in part, instructions and data for execution by processor 910.
  • Main memory 920 can store the executable code when in operation.
  • the system 900 of FIG. 9 further includes a mass storage device 930, portable storage medium drive(s) 940, output devices 950, user input devices 960, a graphics display 970, and peripheral devices 980.
  • the components shown in FIG. 9 are depicted as being connected via a single bus 990. However, the components may be connected through one or more data transport means.
  • processor unit 910 and main memory 920 may be connected via a local microprocessor bus, and the mass storage device 930, peripheral device(s) 980, portable storage device 940, and display system 970 may be connected via one or more input/output (I/O) buses.
  • I/O input/output
  • Mass storage device 930 which may be implemented with a magnetic disk drive, solid state drives, an optical disk drive or other devices, may be a non-volatile storage device for storing data and instructions for use by processor unit 910. Mass storage device 930 can store the system software for implementing embodiments of the present invention for purposes of loading that software into main memory 920.
  • Portable storage device 940 operates in conjunction with a portable nonvolatile storage medium, such as a FLASH thumb drive, compact disk or Digital video disc, to input and output data and code to and from the computer system 900.
  • a portable nonvolatile storage medium such as a FLASH thumb drive, compact disk or Digital video disc
  • the system software for implementing embodiments of the present invention may be stored on such a portable medium and input to the computer system 900 via the portable storage device 940.
  • Input devices 960 provide a portion of a user interface.
  • Input devices 960 may include an alpha-numeric keypad, such as a keyboard, for inputting alphanumeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys.
  • the system 900 as shown in FIG. 9 includes output devices 950. Examples of suitable output devices include speakers, printers, network interfaces, and monitors.
  • Display system 970 may include a liquid crystal display (LCD) or other suitable display device.
  • Display system 970 receives textual and graphical information, and processes the information for output to the display device.
  • LCD liquid crystal display
  • Peripherals 980 may include any type of computer support device to add additional functionality to the computer system.
  • peripheral device(s) 980 may include a modem or a router.
  • the components contained in the computer system 900 of FIG. 9 are those typically found in computer systems that may be suitable for use with embodiments of the present invention and are intended to represent a broad category of such computer components that are well known in the art.
  • the computer system 900 of FIG. 9 can be a personal computer, hand held computing device, telephone, mobile computing device, workstation, server, minicomputer, mainframe computer, or any other computing device.
  • the computer can also include different bus configurations, networked platforms, multi-processor platforms, etc.
  • Various operating systems can be used including Unix, Linux, Windows, Macintosh OS, Android, and other suitable operating systems.

Abstract

La présente invention concerne des systèmes et des procédés d'identification des meilleurs procédés de traitement d'un patient spécifique en fonction de troubles, d'états ou de symptômes correspondant à des informations d'attribut relatives au patient spécifique. Des procédés et des systèmes correspondant à la présente invention permettent d'associer des probabilités qu'un traitement particulier soit susceptible ou ne soit pas susceptible de bénéficier de façon spécifique au patient spécifique. Des procédés et des systèmes de la présente invention permettent également de fournir des régimes de traitement alternatifs à des soignants lorsque lesdits régimes de traitement alternatifs sont associés à un niveau de succès comparable aux souhaits d'un soignant ou prennent en compte les exigences d'une compagnie d'assurance.
PCT/US2016/064244 2016-11-30 2016-11-30 Moteur de modification de protocole d'assurance WO2018101935A1 (fr)

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Application Number Priority Date Filing Date Title
PCT/US2016/064244 WO2018101935A1 (fr) 2016-11-30 2016-11-30 Moteur de modification de protocole d'assurance

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/US2016/064244 WO2018101935A1 (fr) 2016-11-30 2016-11-30 Moteur de modification de protocole d'assurance

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WO2018101935A1 true WO2018101935A1 (fr) 2018-06-07

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060111933A1 (en) * 2003-10-09 2006-05-25 Steven Wheeler Adaptive medical decision support system
US8392215B2 (en) * 2006-04-20 2013-03-05 Jack Joseph Tawil Method for measuring health care quality
US20130325502A1 (en) * 2012-06-05 2013-12-05 Ari Robicsek System and method for providing syndrome-specific, weighted-incidence treatment regimen recommendations
US20150238270A1 (en) * 2014-02-24 2015-08-27 Vida Diagnostics, Inc. Treatment outcome prediction for lung volume reduction procedures
US20160053327A1 (en) * 2014-08-23 2016-02-25 Tiehua Chen Compositions and methods for prediction of clinical outcome for all stages and all cell types of non-small cell lung cancer in multiple countries

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060111933A1 (en) * 2003-10-09 2006-05-25 Steven Wheeler Adaptive medical decision support system
US8392215B2 (en) * 2006-04-20 2013-03-05 Jack Joseph Tawil Method for measuring health care quality
US20130325502A1 (en) * 2012-06-05 2013-12-05 Ari Robicsek System and method for providing syndrome-specific, weighted-incidence treatment regimen recommendations
US20150238270A1 (en) * 2014-02-24 2015-08-27 Vida Diagnostics, Inc. Treatment outcome prediction for lung volume reduction procedures
US20160053327A1 (en) * 2014-08-23 2016-02-25 Tiehua Chen Compositions and methods for prediction of clinical outcome for all stages and all cell types of non-small cell lung cancer in multiple countries

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