HELPING PEOPLE WITH THEIR HEALTH

• US 20120221345A1
• Filed: 02/24/2011
• Published: 08/30/2012
• Est. Priority Date: 02/24/2011
• Status: Abandoned Application

• Current Assignee: Health Value Management, Inc. (Humana Incorporated)
• Original Assignee: Health Value Management, Inc. (Humana Incorporated)
• Inventors: McClure, Douglas J., Lee, Frederick C., Chalk, Mary Beth, Hartman, Catherine A., Greenfield, Arnold J., Barbeau, Elizabeth M., Adler, Martin D., Pilkington, Christopher, Sroka, Jason J., MacWilliams, Robert A.
• Application Number:
  US13/033,642
• Publication Number:
  US 20120221345A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/00 Administration; Management

Abstract

Among other things, a method includes, on successive occasions over a period of time, gathering measured data and self-reported data that represent a health state of a particular participant in a health goal system, using the gathered data in a machine learning engine to select, for a current occasion, a current intervention to be self-applied by the particular participant, the machine learning engine having learned from data gathered from a group of participants on successive occasions, the effectiveness of sequences of self-applied interventions in improving the health states of participants who belong to respective groups that share similar characteristics, the current intervention being expected to affect, for the particular participant (i) a behavior, (ii) the health state, or (iii) a health awareness, and providing the particular participant, electronically through a user interface, information that will encourage the particular participant to engage in the selected self-applied current intervention.

Claims

1. A method comprising

on successive occasions over a period of time, gathering measured data and self-reported data that represent a health state of a particular participant in a health goal system,

using the gathered data in a machine learning engine to select, for a current occasion, a current intervention to be self-applied by the particular participant,

the machine learning engine having learned from data gathered from a group of participants on successive occasions, effectiveness of sequences of self-applied interventions in improving the health states of participants who belong to respective groups that share similar characteristics,

the current intervention being expected to affect, for the particular participant (i) a behavior, (ii) the health state, or (iii) a health awareness, and

providing the particular participant, electronically through a user interface, information that will encourage the particular participant to engage in the selected self-applied current intervention.

2. The method of claim 1 further comprising identifying that a required input in the gathered data is absent and taking an action if a predictive model requires a range of input values that are not available for the particular participant.

3. The method of claim 2 in which taking an action comprises posing a question to the particular participant to solicit a response and complete the required input.

4. The method of claim 2 in which taking an action comprises taking a measurement of the particular participant to complete the required input.

5. The method of claim 1 in which the current intervention is selected independent of a specific instantiation of an interaction associated with capabilities and limitations of a specific device.

6. The method of claim 1 in which the information is retrieved from a content library storing at least one of text, audio, and video.

7. The method of claim 1 in which a content library contains a reference to a specific media item and a corresponding reference to an external resource capable of providing the media item to the participant.

8. The method of claim 1 further comprising determining a set of enrollment questions to present to the particular participant to determine the health and wellness goals for the participant.

9. A method comprising

providing a particular participant, electronically through a user interface, intervention messages that will encourage the particular participant to engage in a selected self-applied current intervention, the intervention messages based on data gathered from a group of participants on successive occasions, the data indicating effectiveness of sequences of self-applied interventions in improving health states of participants who belong to respective groups that share similar characteristics.

10. The method of claim 9 further comprising identifying that a required input in the gathered data is absent and taking an action if a predictive model requires a range of input values that are not available for the particular participant.

11. The method of claim 10 in which taking an action comprises posing a question to the particular participant to solicit a response and complete the required input.

12. The method of claim 10 in which taking an action comprises taking a measurement of the particular participant to complete the required input.

13. The method of claim 9 in which the current intervention is selected independent of a specific instantiation of an interaction associated with the capabilities and limitations of a specific device.

14. The method of claim 9 in which the intervention messages are retrieved from a content library storing at least one of text, audio, and video.

15. The method of claim 9 in which a content library contains a reference to a specific media item and a corresponding reference to an external resource capable of providing the media item to the participant.

16. A method comprising

using a machine learning engine to select, for a current occasion, a current intervention to be self-applied by a particular participant,

the machine learning engine having learned from data gathered from a group of participants on successive occasions, effectiveness of sequences of self-applied interventions in improving health states of participants who belong to respective groups that share similar characteristics,

the current intervention being expected to affect, for the particular participant (i) a behavior, (ii) the health state, or (iii) an health awareness.

17. The method of claim 16 further comprising identifying that a required input in the gathered data is absent and taking an action if a predictive model requires a range of input values that are not available for the particular participant.

18. The method of claim 17 in which taking an action comprises posing a question to the particular participant to solicit a response and complete the required input.

19. The method of claim 17 in which taking an action comprises taking a measurement of the particular participant to complete the required input.

20. The method of claim 16 in which the current intervention is selected independent of a specific instantiation of an interaction associated with capabilities and limitations of a specific device.

21. A system comprising

a processor to, on successive occasions over a period of time, gather measured data and self-reported data that represent a health state of a particular participant in a health goal system,

a machine learning engine configured to use the gathered data to select, for a current occasion, a current intervention to be self-applied by the particular participant,

the machine learning engine having learned from data gathered from a group of participants on successive occasions, effectiveness of sequences of self-applied interventions in improving the health states of participants who belong to respective groups that share similar characteristics,

the current intervention being expected to affect, for the particular participant (i) a behavior, (ii) the health state, or (iii) a health awareness, and

a communications interface configured to electronically provide to a user interface, viewable by the particular participant, information that will encourage the particular participant to engage in the selected self-applied current intervention.

22. The system of claim 21 further comprising identifying that a required input in the gathered data is absent and taking an action if a predictive model requires a range of input values that are not available for the particular participant.

23. The system of claim 22 in which taking an action comprises posing a question to the particular participant to solicit a response and complete the required input.

24. The system of claim 22 in which taking an action comprises taking a measurement of the particular participant to complete the required input.

25. The system of claim 21 in which the current intervention is selected independent of a specific instantiation of an interaction associated with capabilities and limitations of a specific device.

26. The system of claim 21 in which the information is retrieved from a content library storing at least one of text, audio, and video.

27. The system of claim 21 in which a content library contains a reference to a specific media item and a corresponding reference to an external resource capable of providing the media item to the participant.

28. The system of claim 21 further comprising determining a set of enrollment questions to present to the particular participant to determine the health and wellness goals for the participant.

29. A system comprising

a communications interface configured to electronically provide to a user interface, viewable by a particular participant, intervention messages that will encourage the particular participant to engage in a selected self-applied current intervention, the intervention messages based on data gathered from a group of participants on successive occasions, the data indicating effectiveness of sequences of self-applied interventions in improving health states of participants who belong to respective groups that share similar characteristics.

30. The system of claim 29 further comprising identifying that a required input in the gathered data is absent and taking an action if a predictive model requires a range of input values that are not available for the particular participant.

31. The system of claim 30 in which taking an action comprises posing a question to the particular participant to solicit a response and complete the required input.

32. The system of claim 30 in which taking an action comprises taking a measurement of the particular participant to complete the required input.

33. The system of claim 29 in which the current intervention is selected independent of a specific instantiation of an interaction associated with capabilities and limitations of a specific device.

34. The system of claim 29 in which the messages are retrieved from a content library storing at least one of text, audio, and video.

35. The system of claim 29 in which a content library contains a reference to a specific media item and a corresponding reference to an external resource capable of providing the media item to the participant.

36. A system comprising

a machine learning engine configured to select, for a current occasion, a current intervention to be self-applied by a particular participant,

the machine learning engine having learned from data gathered from a group of participants on successive occasions, effectiveness of sequences of self-applied interventions in improving health states of participants who belong to respective groups that share similar characteristics,

the current intervention being expected to affect, for the particular participant (i) a behavior, (ii) the health state, or (iii) a health awareness.

37. The system of claim 36 further comprising identifying that a required input in the gathered data is absent and taking an action if a predictive model requires a range of input values that are not available for the particular participant.

38. The system of claim 37 in which taking an action comprises posing a question to the particular participant to solicit a response and complete the required input.

39. The system of claim 37 in which taking an action comprises taking a measurement of the particular participant to complete the required input.

40. The system of claim 36 in which the current intervention is selected independent of a specific instantiation of an interaction associated with capabilities and limitations of a specific device.