Method for modeling behavior and health changes

• US 9,836,581 B2
• Filed: 08/16/2013
• Issued: 12/05/2017
• Est. Priority Date: 08/16/2012
• Status: Active Grant

• Current Assignee: OrangeDot, Inc.
• Original Assignee: Ginger.io, Inc. (OrangeDot, Inc.)
• Inventors: Madan, Anmol, Moturu, Sai
• Primary Examiner(s): Tomaszewski, Michael
• Application Number:
  US13/969,349
• Publication Number:
  US 20140052475A1
• Time in Patent Office: 1,572 Days
• Field of Search: 705 2- 3
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 40/60 for the operation of medical equipment or devices
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

One method for supporting a patient through a treatment regimen includes: accessing a log of use of a native communication application executing on a mobile computing device by a patient; selecting a subgroup of a patient population based on the log of use of the native communication application and a communication behavior common to the subgroup; retrieving a regimen adherence model associated with the subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and communication behavior for patients within the subgroup; predicting patient adherence to the treatment regimen based on the log of use of the native communication application and the regimen adherence model; and presenting a treatment-related notification based on the patient adherence through the mobile computing device.

Claims

1. A method for using mobility-related data to improve adherence determination for a patient during a treatment regimen, the method comprising:

accessing a first log of user interaction with a user interface of a native communication application executing on a mobile computing device by the patient during the treatment regimen;

generating a first patient interface comprising a first visible input region, the first patient interface defining a first digital survey for presentation at a display of the mobile computing device;

collecting GPS data corresponding to a GPS sensor of the mobile computing device, the GPS data describing physical location of the mobile computing device and associated with location behavior of the patient during the treatment regimen;

collecting motion data corresponding to an accelerometer system and a gyroscope system of the mobile computing device, the motion data describing physical orientation of the mobile computing device and associated with physical activity behavior of the patient during the treatment regimen;

selecting a patient subgroup for the patient from a first subgroup and a second subgroup based on the GPS data and the motion data, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile computing device indicating a first mobility behavior shared by the first subgroup, wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile computing device indicating a second mobility behavior shared by the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the adherence determination;

automatically storing the first log of user interaction, the GPS data, and the motion data in association with the patient subgroup selected from the first and the second subgroups;

retrieve a regimen adherence model based on the patient subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and a communication behavior shared by the patient subgroup;

estimating an adherence to the treatment regimen by the patient within the first time period based on the GPS data, the motion data, the regimen adherence module, and patient inputs collected during the first time period at the visible input region of the first patient interface; and

promoting a treatment to the patient based on the patient adherence.

2. The method of claim 1, wherein receiving the first survey response corresponding to the first time period comprises receiving a first symptom level of the patient within the first time period, and wherein estimating the first adherence to the treatment regimen by the patient comprises estimating the first adherence by the patient based on the first symptom level.

3. The method of claim 1, wherein accessing the first log of use of the native communication application comprises quantifying a frequency and a duration of a first set of outgoing voice communications from a phone call application executing on the mobile computing device during the first time period.

4. The method of claim 3, wherein accessing the first log of use of the native communication application comprises generating a weighted composite of the frequency, the duration, a timing, and a contact diversity of outgoing voice communications and a frequency, a duration, and a timing of incoming voice communications within the phone call application.

5. The method of claim 3, wherein correlating the first log of use of the native communication application with the first adherence comprises correlating the frequency of the first set of outgoing voice communications with conformity to the treatment regimen by the patient, and wherein correlating the second log of use of the native communication application with the second adherence comprises correlating a frequency of a second set of outgoing voice communications within the phone call application with neglect of the treatment regimen by the patient, the frequency of the first set of outgoing voice communications greater than the frequency of the second set of outgoing voice communications.

6. The method of claim 3, wherein accessing the first log of use of the native communication application further comprises quantifying the frequency of outgoing textual communications from a textual messaging application executing on the mobile computing device.

7. The method of claim 1, wherein generating the patient regimen adherence model comprises identifying a change in use of the native communication application, a change in adherence to the treatment regimen between the first time period and the second time period, and a relationship between the change in use of the native communication application and the change in adherence to the treatment regimen and generating the patient regimen adherence model based on the relationship.

8. The method of claim 7, further comprising estimating a first level of patient symptoms during the first time period from the first survey, estimating a second level of patient symptoms during the second time period from the second survey, and predicting an efficacy of the treatment regimen as treatment for the health condition of the patient based on the patient regimen adherence model, the first level of patient symptoms, and the second level of patient symptoms.

9. The method of claim 1, wherein generating the patient regimen adherence model comprises selecting a subgroup of patients from a patient population based on the first log of use of the native communication application, the second log of use of the native communication application, and a communication behavior common to the subgroup and generating the regimen adherence model based on the communication behavior common to the subgroup and adherence to treatment regimens by patients within the subgroup.

10. A method for calculating position of a GPS receiver during a communication period to calculate adherence of a patient to a treatment regimen, the patient associated with a health condition, the method comprising:

collecting GPS satellite data received at the GPS receiver of a mobile computing device comprising the GPS receiver, a microprocessor, a display, and a wireless communication transceiver, wherein the GPS satellite data is received by the GPS receiver during the communication period;

accessing a log of use of a native communication application executing on the mobile computing device by the patient during the communication period;

wirelessly receiving the GPS satellite data and the log of use at a server from the mobile computing device, wherein the remote server comprises a central processing unit (CPU);

calculating, by the server CPU, a set of features comprising a location feature and a communication behavior feature and a location feature, using the GPS satellite data and the log of use, wherein the set of features are configured to improve calculation of patient adherence to the treatment regimen;

selecting a subgroup for the patient based on the log of use of the native communication application and a communication behavior common to the subgroup;

retrieving, at the remote server, the regimen adherence model, wherein the regimen adherence model is associated with the subgroup and defines a correlation between treatment regimen adherence and communication behavior for patients within the subgroup;

using the correlation to calculate, by the server CPU, the patient adherence associated with position of the GPS receiver during the communication period based on the communication behavior feature, the location feature, and the regimen adherence model;

transmitting the patient adherence from the remote server to the mobile computing device; and

displaying a visual representation of the patient adherence on the display of the mobile computing device.

11. The method of claim 10, further comprising receiving from the patient a survey response corresponding to a time period associated with the log of use of a native communication application and extracting from the survey response a qualitative measure of adherence to the treatment regimen by the patient within the time period, wherein selecting the subgroup of the patient population comprises selecting the subgroup of the patient population further based on the qualitative measure of adherence to the treatment regimen by the patient that is similar to adherence to treatment regimens by patients within the subgroup.

12. The method of claim 11, wherein retrieving the regimen adherence model comprises updating the regimen adherence model with the log of use of the native communication application and the survey response of the patient.

13. The method of claim 10, wherein presenting the treatment-related notification comprises transmitting a custom notification from a healthcare provider to the mobile computing device and displaying the custom notification on a display of the mobile computing device.

14. The method of claim 10, wherein accessing the log of use of the native communication application comprises generating a quantitative assessment of a frequency and a duration of outgoing phone calls and textual communications from the mobile computing device during a preset time period.

15. A method comprising:

providing a care provider interface to a care provider, wherein the care provider interface is accessible over the Internet by a care provider computing device;

accessing a log of use of a native communication application executing on a mobile device associated with a patient, the log of use describing patient digital communication behavior during the treatment regimen;

collect GPS data corresponding to a GPS sensor of the mobile device, the GPS data describing physical location of the mobile device and associated with location behavior of the patient during the treatment regimen;

collect motion data corresponding to an accelerometer system and a gyroscope system of the mobile device, the motion data describing physical orientation of the mobile device and associated with physical activity behavior of the patient during the treatment regimen;

selecting a patient subgroup from a first subgroup and a second subgroup based on the GPS data and the motion data, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a first mobility behavior shared by the first subgroup, wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a second mobility behavior shared by the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the adherence determination;

for the patient within the patient subgroup, estimating adherence of the patient to a prescribed treatment regimen during a period of time based on the log of use, the GPS data, the motion data, and survey responses entered by the patient through a corresponding mobile computing device;

estimating an efficacy of the treatment regimen in treating the health condition for the patient within the patient subgroup based on adherence to prescribed treatment regimens and health statuses of patients within the patient subgroup;

generating a treatment regimen report specific to the patient subgroup and enabling a wireless communicable link with a care provider computing device, based on the efficacy of the treatment regimen; and

presenting, by way of the wireless communicable link with the care provider computing device, information derived from the treatment regimen report.

16. The method of claim 15, further comprising, for a patient within the subgroup, updating a prescribed treatment regimen in accordance with the efficacy of the treatment regimen.

17. The method of claim 15, further comprising characterizing communication behavior of a subsequent patient based on use of a native communication application executing on a corresponding mobile computing device by the subsequent patient, associating the subsequent patient with the subgroup based on the communication behavior of the subsequent patient, and prescribing the treatment regimen to the subsequent patient.

18. The method of claim 15, wherein selecting the subgroup of patients comprises selecting a set of patients within the population based on similar patient communication behaviors within the set of patients prior to initiation of treatment regimens and during administration of the treatment regimens to the patients in the set of patients.

19. The method of claim 18, wherein estimating the efficacy of the treatment regimen comprises, for a patient within the subgroup, identifying an increase in use of a native communication application by the patient from prior to initiation of the treatment regimen by the patient to during administration of the treatment regimen to the patient, correlating the increase in use of a corresponding native communication application by the patient with improved health status of the patient, and associating improved health status of the patient with efficacy of the treatment in treating the health condition of the patient.

20. The method of claim 15, further comprising generating a treatment regimen model for the subgroup based on the adherence to the treatment regimen, communication behavior, and survey responses of patients within the subgroup throughout the period of time.

21. The method of claim 15, wherein estimating the efficacy of the treatment regimen comprises estimating an efficacy of a physical therapy regimen in treating physical handicaps for patients within the subgroup.

22. The method of claim 15, wherein estimating the efficacy of the treatment regimen comprises estimating an efficacy of a pharmacotherapy regimen in treating mental disorders for patients within the subgroup.

23. The method of claim 15, wherein generating the treatment regimen report comprises predicting risk of decline in a health condition of a patient within the subgroup during administration of the treatment regimen to the patient.

24. A system for using mobility-related data to improve adherence determination for a patient during a treatment regimen, the system comprising:

a communication monitoring module operable to access a log of use of a native communication application executing on a mobile device associated with the patient, the log of use describing patient digital communication behavior during the treatment regimen;

a mobility monitoring module operable to;

collect GPS data corresponding to a GPS sensor of the mobile device, the GPS data describing physical location of the mobile device and associated with location behavior of the patient during the treatment regimen; and

collect motion data corresponding to an accelerometer system and a gyroscope system of the mobile device, the motion data describing physical orientation of the mobile device and associated with physical activity behavior of the patient during the treatment regimen; and

a processing system operable to;

select a patient subgroup for the patient from a first subgroup and a second subgroup based on the GPS data and the motion data, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a first mobility behavior shared by the first subgroup, wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a second mobility behavior shared by the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the adherence determination;

automatically store the log of use dataset, the GPS data, and the motion data in association with the patient subgroup selected from the first and the second subgroups;

retrieve a regimen adherence model based on the patient subgroup, the regimen adherence model defining a correlation between treatment regimen adherence and a communication behavior shared by the patient subgroup;

predict patient adherence to the treatment regimen based on the log of use, the GPS data, the motion data, and the regimen adherence model; and

promote a treatment to the patient based on the patient adherence.

25. The system of claim 24, wherein the first mobility behavior comprises a low level of patient physical activity, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a low level of patient physical activity shared by the first subgroup, wherein the second mobility behavior comprises a high level of patient physical activity, and wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating a high level of patient physical activity shared by the second subgroup.

26. The system of claim 24, wherein the first mobility behavior comprises a total patient movement below a threshold, wherein the first subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating movement of the patient being below the threshold, wherein the second mobility behavior comprises a total patient movement above the threshold, wherein the second subgroup is selected in response to the physical location and the physical orientation of the mobile device indicating the movement of the patient being above the threshold.

27. A method for improving processing of communication behavior data for predicting adherence of a patient to a treatment regimen, the patient associated with a health condition, the method comprising:

accessing a log of use of a native communication application executing on a mobile computing device by the patient during a communication period;

defining a subset of communication feature types for a subgroup of a patient population, wherein the subset of communication feature types are from a set of feature types, and wherein defining the subset of communication feature types is configured to improve processing of features by a remote server;

determining an orientation of the mobile computing device in relation to the communication period based on signals from a set of inertial sensors mounted respectively at the mobile computing device during the communication period;

extracting a patient mobility feature associated with the communication period from the orientation;

extracting communication behavior features from the log of use, the communication behavior features typifying the subset of communication feature types;

selecting the subgroup for the patient based on the log of use of the native communication application and a communication behavior common to the subgroup, wherein selecting the subgroup is configured to improve manipulation of the communication behavior features and retrieval of a regimen adherence model of a set of regimen adherence models by the remote server;

retrieving, at the remote server, the regimen adherence model, wherein the regimen adherence model is associated with the subgroup and defines a correlation between treatment regimen adherence and communication behavior for patients within the subgroup;

predicting, at the remote server, patient adherence to the treatment regimen based on the communication behavior features, the mobility feature, and the regimen adherence model;

enabling a wireless communicable link with the mobile computing device; and

presenting, by way of the wireless communicable link with the mobile computing device, a treatment-related notification at a care provider interface, based on the patient adherence through the mobile computing device.