Method for managing patient quality of life

• US 10,102,341 B2
• Filed: 11/06/2015
• Issued: 10/16/2018
• Est. Priority Date: 08/16/2012
• Status: Active Grant

• Current Assignee: OrangeDot, Inc.
• Original Assignee: Ginger.io, Inc. (OrangeDot, Inc.)
• Inventors: Moturu, Sai, Madan, Anmol
• Primary Examiner(s): Tomaszewski, Michael
• Application Number:
  US14/934,893
• Publication Number:
  US 20160063205A1
• Time in Patent Office: 1,075 Days
• Field of Search: None
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 20/70 relating to mental therapies, e.g. psychological therapy or autogenous training
• CPC Class Codes: G16H 40/67 for remote operation
• 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

A method and system for modeling behavior and pain-related state of an individual, the method comprising: receiving a log of use dataset associated with communication behavior of the individual during a time period; receiving a supplementary dataset characterizing activity of the individual during the time period; receiving a survey dataset including responses, to at least one of a set of symptom-assessment surveys, associated with a set of time points of the time period; generating a predictive analysis of a pain-related state of the individual associated with at least a portion of the time period, from at least one of the log of use dataset, the supplementary dataset, and the survey dataset; and generating an alert upon detection that a set of parameters from the predictive analysis of the pain-related state satisfy a threshold condition.

Claims

1. A method for improving pain-related state determination for managing pain of an individual, the method comprising:

transmitting, from a communication module executing on a mobile device to a computing system, a log of use dataset associated with communication behavior of the individual during a time period, wherein the log of use dataset includes at least one of a phone calling component and a text messaging component;

at the computing system, receiving a motion supplementary dataset corresponding to a motion sensor of the mobile device, the motion supplementary dataset characterizing mobility of the individual in association with the time period;

collecting GPS data corresponding to a GPS sensor of the mobile device, the GPS data associated with location behavior of the individual in association with the time period;

within an application executing at the mobile device, generating a survey dataset upon retrieving responses provided by the individual to at least one of a set of surveys, associated with a set of time points of the time period;

selecting a patient subgroup for the individual from a first subgroup and a second subgroup based on the GPS data and the motion supplementary dataset, wherein the first subgroup is selected in response to the GPS data and the motion supplementary dataset indicating a first mobility behavior associated with the first subgroup, wherein the second subgroup is selected in response to the GPS data and the motion supplementary dataset indicating a second mobility behavior associated with the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the pain-related state determination;

at the computing system, generating a predictive model based on the selected patient subgroup, a passive dataset derived from the log of use dataset and the motion supplementary dataset, and the survey dataset;

determining a pain-related state of the individual, associated with at least a portion of the time period, from at least one of the passive dataset, the survey dataset, and an output of the predictive model;

at the computing system, upon detection that the pain-related state of the individual satisfies a threshold condition, enabling a communicable link with at least one of the mobile device of the individual and a care provider computing device; and

by way of the communicable link, automatically initiating provision of a therapeutic intervention for improving a health outcome of the individual, by way of at least one of the computing system and the mobile device.

2. The method of claim 1, wherein transmitting the log of use dataset further includes:

at the computing system, extracting, from the log of use dataset, a feature set including;

a number of text messages sent by the individual, a length of text messages sent by the individual, a phone call duration, an outgoing call count, and a number of ignored calls.

3. The method of claim 2, wherein determining the pain-related state of the individual comprises transforming at least two components of the feature set into a comparison between text messaging behavior and phone calling behavior by the individual and detecting an increase in text messaging behavior over phone calling behavior, from the comparison.

4. The method of claim 1, wherein receiving the motion supplementary dataset includes extracting a mobility radius associated with at least one time point of the time period and derived from global positioning sensors of the mobile device.

5. The method of claim 4, wherein determining the pain-related state includes determining a mobility parameter, and wherein detecting satisfaction of the threshold condition includes 1) comparing the mobility parameter to a mobility radius threshold with an associated duration threshold, and 2) comparing a phone calling parameter from the log of use dataset to a phone calling parameter threshold.

6. The method of claim 1, wherein determining the pain-related state includes determining a parameter indicative of overall use of the mobile device by the individual from the log of use dataset, and wherein detecting satisfaction of the threshold condition includes comparing the parameter to a total use threshold within a duration of time.

7. The method of claim 1, wherein generating the survey dataset comprises providing, within an application executing at the mobile device, a set of surveys derived from at least one of:

a Physical Activity Scale (PAS) survey, a Health Assessment Questionnaire (HAQ), and a pain and physical functioning visual assessment scale.

8. The method of claim 7, wherein providing the set of surveys includes automatically providing a first portion of the set of surveys within the application at a first subset of time points of the time period, and providing a second portion of the set of surveys within the application at a second subset of time points of the time period.

9. The method of claim 1, wherein automatically initiating provision of the therapeutic intervention includes at least one of:

enabling a healthcare entity to contact the individual and providing health advice related to pain management to the individual within an application executing at the mobile device.

10. The method of claim 1, wherein automatically initiating provision of the therapeutic intervention comprises providing the healthcare entity with an electronically-implemented tool for contacting the individual.

11. A method for improving pain-related state determination for managing pain of an individual, the method comprising:

upon establishing communication between a computing system and a communication module of a mobile device of the individual, receiving a log of use dataset associated with communication behavior of the individual during a time period, at the computing system, wherein the log of use dataset includes at least one of a phone calling component and a text messaging component;

at the computing system, receiving a mobility sensor supplementary dataset corresponding to a mobility sensor of the mobile communication device, the mobility sensor supplementary dataset characterizing physical activity of the individual during the time period;

selecting a patient subgroup for the individual from a first subgroup and a second subgroup based on the mobility sensor supplementary dataset, wherein the first subgroup is selected in response to the mobility sensor supplementary dataset indicating a first mobility behavior associated with the first subgroup, wherein the second subgroup is selected in response to the mobility sensor supplementary dataset indicating a second mobility behavior associated with the second subgroup, and wherein selection of the patient subgroup is operable to improve data storage, data retrieval, and the pain-related state determination;

at the computing system, generating a predictive model of a pain-related state of the individual in association with at least a portion of the time period, based on the selected patient subgroup and the log of use dataset;

in response to an output of the predictive model that indicates that the pain-related state of the individual satisfies a threshold condition, enabling a communicable link with at least one of the mobile device of the individual and a care provider computing device; and

by way of the communicable link, automatically initiating provision of a therapeutic intervention for improving a health outcome of the individual.

12. The method of claim 11, further comprising:

upon receiving the log of use dataset, extracting, from the log of use dataset, a feature set including;

a number of text messages sent by the individual, a number of text messages received by the individual, a number of phone calls received by the individual, a length of text messages received by the individual, and a length of phone calls received by the individual.

13. The method of claim 12, further including:

receiving a supplementary dataset, wherein receiving the supplementary dataset includes extracting a mobility radius associated with at least one time point of the time period and derived from global positioning sensors of the mobile device.

14. The method of claim 13, wherein generating the predictive model comprises processing information from the log of use dataset and the supplementary dataset with a principal component analysis.

15. The method of claim 13, wherein generating the predictive model further comprises processing information from the log of use dataset and the supplementary dataset, along with information derived from surveying the individual, with generalized estimating equations.

16. The method of claim 13, wherein generating the predictive model includes transforming at least two components of the feature set into a comparison between text messaging behavior and phone calling behavior by the individual and detecting an increase in text messaging behavior over phone calling behavior, from the comparison.

17. The method of claim 11, wherein automatically initiation provision of the therapeutic intervention comprises rendering an alert at a user interface of a dashboard of the care provider computing device, and wherein rendering the alert further includes providing, within the dashboard, an electronically-implemented tool for contacting the individual.

18. The method of claim 11, wherein generating the predictive model of the pain-related state of the individual includes generating an anticipated pain-related state of the individual at a future time point outside of the time period, and wherein the method further includes automatically initiating provision of the therapeutic intervention for the individual to prevent the anticipated pain-related state of the individual.

19. The method of claim 18, wherein the anticipated pain-related state is a state associated with a rheumatoid disorder, and wherein automatically initiating provision of the therapeutic intervention includes enabling a healthcare entity to contact the individual proximal in time to a future time point associated with the anticipated state.

20. The method of claim 11, wherein automatically initiating provision of the therapeutic intervention comprises determining parameters of a transcutaneous electrical nerve stimulation (TENS) treatment appropriate for the individual.