Method for modeling behavior and health changes

• US 10,068,672 B2
• Filed: 10/30/2017
• Issued: 09/04/2018
• 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 Thejasvee
• Primary Examiner(s): Tomaszewski, Michael
• Application Number:
  US15/798,005
• Publication Number:
  US 20180052974A1
• Time in Patent Office: 309 Days
• Field of Search: None
• 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 improving health risk determination for a patient, the method comprising:

accessing a log of use corresponding to a communication application for a mobile computing device associated with the patient;

collecting location data corresponding to a location sensor of the mobile computing device;

collecting motion data corresponding to a motion sensor of the mobile computing device;

selecting a patient subgroup for the patient from a first subgroup and a second subgroup based on the location data and the motion data, wherein the first subgroup is selected in response to the location data and the motion data indicating a first mobility behavior shared by the first subgroup, wherein the second subgroup is selected in response to the location data and the motion data indicating a second mobility behavior shared by the second subgroup, wherein the selection of the patient subgroup is operable to improve data storage, data retrieval, and the health risk determination;

retrieving a health risk model associated with the selected patient subgroup, the health risk model defining a correlation between a risk of change in a medical symptom and communication behavior for patients within the subgroup;

determining a patient risk of change in the medical symptom for the patient based on the log of use and the health risk model; and

facilitating provision of a health-related intervention for improving a health outcome of the patient, based on the patient risk of change.

2. The method of claim 1, wherein the method further comprises automatically storing the location data, the motion data, and communication data from the log of use in association with the patient subgroup selected from the first and the second subgroups.

3. The method of claim 2, further comprising anonymizing the location data, the motion data, and the communication data prior to automatically storing the location data, the motion data, and the communication data.

4. The method of claim 1, wherein the first mobility behavior comprises a low level of patient physical activity, wherein the first subgroup is selected in response to the location data and the motion data indicating the low level of patient physical activity, wherein the second mobility behavior comprises a high level of patient physical activity, and wherein the second subgroup is selected in response to the location data and the motion data indicating the high level of patient physical activity.

5. The method of claim 1, wherein the first mobility behavior comprises a total patient movement below a threshold, wherein the first subgroup is selected in response to the location data and the motion data 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 location data and the motion data indicating the movement of the patient being above the threshold.

6. The method of claim 1, wherein determining the patient risk of change in the medical symptom comprises determining the patient risk of change in the medical symptom based on the location data, the motion data, the log of use, and the health risk model.

7. The method of claim 6,

wherein the health risk model defines a correlation between a sleep-related condition and the communication behavior for the patients within the subgroup, and

wherein determining the patient risk of change in the medical symptom comprises predicting a medical diagnosis of the sleep-related condition based on the location data, the motion data, the log of use, and the health risk model.

8. The method of claim 1, further comprising:

in response to the patient risk of change in the medical symptom exceeding a threshold risk, facilitating a digital communication between a care provider and the patient; and

updating the patient risk of change in the medical symptom for the patient based on the digital communication.

9. A method for improving health status determination for a first patient, the method comprising:

accessing a log of use corresponding to a communication application for a first mobile computing device associated with the first patient;

collecting first mobility data corresponding to a first mobility sensor of the first mobile computing device;

selecting a patient subgroup for the first patient from a first subgroup and a second subgroup based on the first mobility data, wherein the first subgroup is selected in response to the first mobility data indicating a first mobility behavior associated with the first subgroup, wherein the second subgroup is selected in response to the first mobility data indicating a second mobility behavior associated with the second subgroup, wherein the selection of the patient subgroup is operable to improve data storage, data retrieval, and the health status determination;

retrieving a health risk model associated with the selected patient subgroup, the health risk model defining a correlation between health status and communication behavior for patients associated with the subgroup;

determining a patient health status for the first patient based on the log of use and the health risk model; and

facilitating provision of a health-related intervention for improving the patient health status for the first patient, based on the patient health status.

10. The method of claim 9, wherein selection of the patient subgroup is operable to improve data storage and data retrieval associated with health status determination, and wherein the method further comprises, at a remote computing system communicable with the first mobile computing device and a second mobile computing device associated with a second patient:

automatically storing the first mobility data in association with the selected patient subgroup;

selecting the patient subgroup for the second patient from the first subgroup and the second subgroup based on second mobility data corresponding to a second mobility sensor of the second mobile computing device; and

automatically storing the second mobility data in association with the selected patient subgroup.

11. The method of claim 9, wherein collecting the first mobility data corresponding to the first mobility sensor comprises collecting GPS satellite data corresponding to a GPS receiver of the first mobile computing device comprising the GPS receiver, a microprocessor, a display, and a wireless communication module, wherein selecting the patient subgroup comprises:

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

calculating, by the CPU of the remote server, a location feature based on the GPS satellite data, wherein the location feature is operable to improve health status determination; and

selecting the patient subgroup for the first patient from the first subgroup and the second subgroup based on the location feature.

12. The method of claim 9, wherein collecting the first mobility data corresponding to the first mobility sensor comprises collecting motion data corresponding to a set of inertial sensors mounted at the first mobile computing device, and wherein selecting the patient subgroup comprises:

determining an orientation of the first mobile computing device based on the motion data; and

selecting the patient subgroup for the first patient from the first subgroup and the second subgroup based on the orientation of the first mobile computing device.

13. The method of claim 9, wherein accessing the log of use comprises extracting a voice communication feature for the first patient from the log of use, and wherein determining the patient health status for the first patient comprises determining the patient health status based on the voice communication feature and the health risk model.

14. The method of claim 13, further comprising:

collecting a phone usage feature indicating phone usage behavior of the first patient associated with the first mobile computing device, wherein determining the patient health status for the first patient comprises determining the patient health status based on the phone usage feature, the voice communication feature, and the health risk model.

15. The method of claim 9, wherein the health risk model defines a correlation between a mental health status and the communication behavior for patients associated with the subgroup, and wherein determining the patient health status for the first patient comprises determining a patient mental health status for the first patient based on the first mobility data, the log of use, and the health risk model.

16. The method of claim 15, further comprising determining a treatment regimen for the patient mental health status of the first patient based on the first mobility data and the log of use, wherein the treatment regimen is operable to improve the patient mental health status of the first patient.

17. The method of claim 16, wherein the treatment regimen comprises a medication regimen associated with at least one of medication type and medication dosage, and wherein determining the treatment regimen comprises determining the at least one of the medication type and the medication dosage for the first patient based on the first mobility data and the log of use.

18. The method of claim 15, wherein the mental health status comprises at least one of:

a depression disorder, an anxiety disorder, a bipolar disorder, and a psychotic disorder.

19. The method of claim 9, wherein the log of use indicates a communication behavior associated with a communication between the first patient and a care provider, and wherein selecting the patient subgroup comprises selecting the patient subgroup based on the communication behavior and the first mobility data.

20. The method of claim 9, wherein determining the patient health status comprises predicting a risk of increased symptom severity for symptoms associated with a disease based on the log of use and the health risk model.

21. The method of claim 20, further comprising transmitting a notification to a care provider in response to the risk of increased symptom severity exceeding a threshold risk associated with the disease.

22. The method of claim 21, wherein the health risk model defines a correlation between a change in the communication behavior and risk of hospital admission within a specified period of time, and wherein determining the patient health status comprises predicting a risk of hospital admission for the first patient within the specified period of time, and wherein transmitting the notification to the care provider comprises transmitting the risk of hospital admission within the specified period of time to a hospital associated with the first patient.

23. The method of claim 9, further comprising:

identifying a relationship between changes in communication behavior and changes in symptom severity for patients within the patient subgroup; and

generating the health risk model based on the relationship.

24. The method of claim 9, wherein determining the patient health status comprises:

identifying a change in a composite function associated with stress and productivity of the patient based on the log of use; and

determining the patient health status based on the health risk model and the change in the composite function associated with stress and productivity.