METHOD FOR MODELING BEHAVIOR AND HEALTH CHANGES

• US 20140052474A1
• Filed: 08/16/2013
• Published: 02/20/2014
• Est. Priority Date: 08/16/2012
• Status: Abandoned Application

• Current Assignee: OrangeDot, Inc.
• Original Assignee: Ginger Os, Inc.
• Inventors: Madan, Anmol, Moturu, Sai, Brandis, Mickey
• Application Number:
  US13/969,339
• Publication Number:
  US 20140052474A1
• Time in Patent Office: Days
• US Class Current: 705/3
• 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 detecting a change in health status of a patient includes: accessing a log of use of a native communication application executing on a mobile computing device by the 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 health risk model associated with the subgroup, the health risk model defining a correlation between risk of change in a medical symptom and communication behavior for patients within the subgroup; predicting a risk of change in a medical symptom for the patient based on the log of use of the native communication application and the health risk model; and transmitting a notification to a care provider associated with the patient in response to the risk of change in the medical symptom for the patient that exceeds a threshold risk.

Claims

1. A method for detecting a change in health status of a patient, the method comprising:

accessing a log of use of a native communication application executing on a mobile computing device by the 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 health risk model associated with the subgroup, the health risk model defining a correlation between risk of change in a medical symptom and communication behavior for patients within the subgroup;

predicting a risk of change in a medical symptom for the patient based on the log of use of the native communication application and the health risk model; and

transmitting a notification to a care provider associated with the patient in response to the risk of change in the medical symptom for the patient that exceeds a threshold risk.

2. The method of claim 1, further comprising receiving a survey response from the patient and extracting a demographic of the patient from the survey response, wherein selecting the subgroup of the patient population comprises further selecting the subgroup based on a common demographic within the subgroup that is substantially similar to the demographic of the patient.

3. The method of claim 2, wherein retrieving the health risk model comprises updating the health risk model with the log of use of the native communication application and the first survey response from the patient.

4. The method of claim 1, wherein predicting the risk of change in the medical symptom for the patient comprises predicting a risk of increased symptom severity for symptoms associated with a particular disease, and wherein transmitting the notification to the care provider comprises transmitting the notification to the care provider in response to the risk of change in the medical symptom for the patient that exceeds a threshold risk specific to the particular disease.

5. The method of claim 1, further comprising transmitting a behavior change suggestion to the mobile computing device based on the risk of change in the medical symptom for the patient.

6. The method of claim 5, wherein transmitting the behavior change suggestion to the mobile computing device comprises transmitting a custom notification from the care provider to the mobile computing device of the patient responsive to the notification.

7. The method of claim 1, 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.

8. The method of claim 7, wherein accessing the log of use of the native communication application further comprises generating a quantitative assessment of a frequency of, a length of, and a response time to incoming textual communications to the mobile computing device during the preset time period.

9. The method of claim 7, wherein accessing the log of use of the native communication application further 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.

10. The method of claim 1, further comprising accessing a second log of use of a native communication application executing on a second mobile computing device by a second patient and predicting a second risk of change in a medical symptom for the second patient based on the second log of use of the native communication application and the health risk model, wherein transmitting the notification to the care provider comprises transmitting a scored patient triage list to the care provider according to the risk of change in the medical symptom for the patient and the second risk of change in the medical symptom for the second patient.

11. The method of claim 1, wherein transmitting the notification to the care provider comprises prompting a medical service provider to schedule a visit with the patient.

12. The method of claim 1, wherein retrieving the health risk model comprises retrieving the health risk model defining a correlation between progression of a disease and communication behavior for patients within the subgroup, and wherein predicting the risk of change in the medical symptom for the patient comprises predicting a medical diagnosis of the disease for the patient further based on the progression of the disease within the subgroup.

13. The method of claim 12, wherein transmitting the notification to the care provider comprises selecting a treatment for the disease based on efficacy of the treatment within the subgroup and inserting the treatment into a health record of the patient accessible to the care provider.

14. The method of claim 1, wherein predicting the risk of change in the medical symptom for the patient comprises identifying a change in communication behavior of the patient over a period of time that mimics changes in communication behavior by patients within the subgroup over similar periods of time and predicting a declining health status of the patient based on the change in communication behavior of the patient over the period of time.

15. The method of claim 14, wherein identifying the change in communication behavior of the patient over the period of time comprises identifying a change in a composite function representing work-life balance of the patient, and wherein predicting the declining health status of the patient comprises correlating the change in the composite function with a declining health status based on the health risk model.

16. The method of claim 14, wherein identifying the change in communication behavior of the patient over the period of time comprises identifying a change in mobile usage by the patient over the period of time, and wherein predicting the declining health status of the patient comprises correlating the change in mobile usage by the patient with a declining health status based on the health risk model.

17. The method of claim 16, wherein identifying the change in mobile usage by the patient over the period of time comprises identifying changes activity, hyper-activity, and inactivity of the patient over the period time.

18. The method of claim 1, wherein predicting the risk of change in the medical symptom for the patient comprises generating a quantitative risk score corresponding to a predicted level of risk of change in the medical symptom for the patient.

19. A method comprising:

selecting a subgroup of patients associated with a health condition from a population of patients, patients in the subgroup exhibiting similar behavioral characteristics;

for patients within the subgroup, characterizing communication behavior of a patient based on use of a native communication application executing on a corresponding mobile computing device by the patient during the period of time;

extracting characteristics of medical symptoms of patients within the subgroup from surveys submitted by patients within the subgroup;

identifying a relationship between communication behaviors of patients within the subgroup and characteristics of medical symptoms of patients within the subgroup;

generating a health risk model for the subgroup based on the relationship, the health risk model defining a correlation between a change in communication behavior and risk of change in a medical symptom.

20. The method of claim 19, further comprising

accessing a log of use of a native communication application executing on a corresponding mobile computing device by a subsequent patient,

selecting the health risk model based on the log of use of the native communication application and a communication behavior common to the subgroup,

predicting a risk of change in the medical symptom for the subsequent patient based on the log of use of the native communication application and the health risk model, and

transmitting a notification to a care provider associated with the patient in response to the risk of change in the medical symptom for the patient that exceeds a threshold risk.

21. The method of claim 20, wherein generating the health risk model for the subgroup comprises generating the health risk model that defines a correlation between a change in communication behavior and risk of hospital admission within a specified period of time, and wherein predicting the risk of change in the medical symptom for the subsequent patient comprises predicting a risk of hospital admission for the subsequent patient within the specified period of time, and wherein transmitting the notification to the care provider comprises transmitting the risk of hospital admission by the subsequent patient within the specified period of time to a hospital associated with the subsequent patient.

22. The method of claim 19, wherein extracting characteristics of medical symptoms of patients within the subgroup from surveys submitted by patients within the subgroup comprises extracting characteristics of medical symptoms of a subset of patients within the subgroup from surveys submitted by the subset of patients.

23. The method of claim 19, wherein characterizing communication behavior of a patient, for the patient within the subgroup, comprises detecting a change in communication behavior by the patient, wherein extracting characteristics of medical symptoms of patients within the subgroup comprises detecting changes in symptom severity within patients within the subgroup, and wherein identifying the relationship between communication behavior of patients within the subgroup and characteristics of medical symptoms of patients within the subgroup comprises identifying a relationship between changes in communication behavior and changes in symptom severity for patients within the subgroup.

24. The method of claim 19, wherein characterizing communication behavior of a patient within the subgroup comprises generating a quantitative assessment of a frequency and a duration of outgoing phone calls and textual communications from the corresponding mobile computing device during a preset time period.