METHODS AND SYSTEMS FOR PROVIDING THERAPEUTIC GUIDELINES TO A PERSON HAVING DIABETES

• US 20110237918A1
• Filed: 02/23/2011
• Published: 09/29/2011
• Est. Priority Date: 02/23/2010
• Status: Abandoned Application

• Current Assignee: Roche Diabetes Care Inc. (Roche Holding AG)
• Original Assignee: Roche Diabetes Care Inc. (Roche Holding AG)
• Inventors: Atkinson, Craig, Duke, David, Rees, Christen, Wagner, Robin, Soni, Abhishek
• Application Number:
  US13/032,970
• Publication Number:
  US 20110237918A1
• Time in Patent Office: Days
• US Class Current: 600/365
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 70/20 relating to practices or guidelines

Abstract

A method is disclosed for providing therapeutic guidelines to a person having diabetes. The method comprises measuring a blood glucose (bG) level of the person for two or more days, wherein at least one bG measurement is taken per day, and the at least one daily bG measurement corresponds to one or more daily events for the person; recording the measured bG levels in a computing device; determining, by the computing device, whether the recorded bG levels are below, within, or above one or more predetermined bG ranges; an automatically providing, by the computing device, therapeutic guidelines to the person, based on whether the recorded bG levels are below, within, or above the one or more predetermined bG ranges.

Claims

1. A method, comprising:

detecting a pattern of an abnormality in blood glucose data collected from an individual with a computing device;

generating a change in therapy recommendation for the individual with the computing device based on said detecting the pattern; and

outputting a customized testing protocol customized to detect whether the change in therapy successfully addressed the abnormality with the computing device.

2. The method according to claim 1, further comprising receiving the blood glucose data with the computing device from a standardized structured testing data collection form before said detecting the pattern.

3. The method according to claim 2, wherein said receiving the blood glucose data includes downloading the blood glucose data from a blood glucose meter.

4. The method according to claim 2, wherein said receiving the blood glucose data includes scanning a paper version of the structured testing data collection form.

5. The method according to claim 2, further comprising:

confirming the change in therapy successfully addressed the abnormality by analyzing data from the customized testing protocol with the computing device;

instructing the individual to collect a second set of blood glucose data with the standardized structured testing data collection form with the computing device; and

analyzing the second set of blood glucose data for a second abnormality pattern with the computing device.

6. The method according to claim 1, further comprising:

receiving background information about the individual with the computing device; and

wherein said generating the change in therapy recommendation includes selecting the change in therapy recommendation based at least in part on the background information.

7. The method according to claim 6, wherein the background information includes demographic information.

8. The method according to claim 6, wherein the background information includes comorbidity information.

9. The method according to claim 6, wherein the background information includes medication information.

10. The method according to claim 6, wherein the background information includes diabetes duration.

11. The method according to claim 6, further comprising:

wherein the background information includes social media preferences for the individual; and

wherein said generating the change in therapy recommendation includes providing a social media advice component based at least on the social media preferences of the individual.

12. The method according to claim 1, further comprising detecting the abnormality in the blood glucose data with the computing device before said detecting the pattern.

13. The method according to claim 1, further comprising:

asking one or more assessment questions with the computing device at least based on the pattern of the abnormality; and

wherein said generating the change in therapy recommendation is at least based on answers to the assessment questions.

14. The method according to claim 1, wherein the change in therapy includes a change in medication recommendation.

15. The method according to claim 14, further comprising providing the change in medication recommendation to a physician.

16. The method according to claim 1, wherein the change in therapy includes a change in lifestyle.

17. The method according to claim 16, wherein the change in lifestyle includes a change in exercise.

18. The method according to claim 16, wherein the change in lifestyle includes a change in diet.

19. The method according to claim 16, further comprising:

determining the abnormality is severe with the computing device; and

notifying a health care provider that the abnormality is severe.

20. The method according to claim 1, wherein the abnormality includes hypoglycemia.

21. The method according to claim 20, wherein the pattern includes repeated waking hypoglycemia.

22. The method according to claim 1, wherein the abnormality includes hyperglycemia.

23. The method according to claim 22, wherein the pattern includes repeated postprandial hyperglycemia.

24. The method according to claim 22, wherein the pattern includes repeated preprandial hyperglycemia.

25. The method according to claim 1, wherein said outputting the customized testing protocol includes printing a customized structured testing form with a printer.

26. The method according to claim 1, wherein said outputting the customized testing protocol includes displaying a customized structured testing form on a computer display.

27. The method according to claim 1, wherein said outputting the customized testing protocol includes providing advice related to the therapy recommendation.

28. The method according to claim 1, wherein the computing device includes a personal computer.

29. The method according to claim 1, wherein the computing device includes blood glucose meter.

30. The method according to claim 1, wherein the computing device includes a web hosted computer system.

31. A method, comprising:

detecting a pattern for a blood glucose abnormality with a computing device base on blood glucose data and contextual data collected from an individual; and

generating a change in therapy recommendation for the individual automatically with the computing device based on said detecting the pattern.

32. The method according to claim 31, further comprising outputting a customized testing protocol customized to detect whether the change in therapy successfully addressed the blood glucose abnormality with the computing device.

33. The method according to claim 31, wherein the contextual data includes data about the individual surrounding collection of the blood glucose data.

34. The method according to claim 31, wherein the contextual data includes dietary information for the individual.

35. The method according to claim 31, wherein the contextual data includes activity information for the individual.

36. The method according to claim 31, wherein said detecting the pattern includes considering more than one type of the contextual data with the computing device.

37. The method according to any preceding claim 31, wherein during said generating the change in therapy recommendation the computing device takes into account more than one parameter.

38. The method according to claim 31, wherein during said generating the change in therapy recommendation the computing device takes into account relationships between parameters.

39. A method, comprising:

detecting a pattern for a blood glucose abnormality with a computing device base on blood glucose data and contextual data collected from an individual; and

generating a threat alert for the individual automatically with the computing device based on said detecting the pattern.

40. The method according to claim 39, further comprising:

wherein the blood glucose abnormality includes hypoglycemia; and

wherein said generating the threat alert includes displaying the threat alert on a glucose meter.

41. The method according to claim 39, further comprising:

collecting the blood glucose data and the contextual data within a window that spans before and after when the blood glucose abnormality occurs; and

wherein said collecting includes collecting the blood glucose data and the contextual data at predefined intervals within the window.

42. The method according to claim 41, wherein the window is three hours and the predefined interval is 15 to 20 minutes.

43. The method according to claim 39, further comprising:

wherein said detecting the pattern for the blood glucose abnormality is performed with a continuous blood glucose monitoring device; and

retesting the individual with a discrete testing glucose meter to reconfirm the abnormality.

44. The method according to claim 39, wherein all or part of the acts are performed by a personal computer.

45. The method according to claim 39, wherein all or part of the acts are performed by a blood glucose meter.

46. The method according to claim 39, wherein all or part of the acts are performed by a continuous blood glucose monitoring device.

47. (canceled)

48. A system, comprising:

means for detecting a pattern of an abnormality in blood glucose data collected from an individual;

means for generating a change in therapy recommendation for the individual based on the pattern; and

means for outputting a customized testing protocol customized to detect whether the change in therapy successfully addressed the abnormality.

49. The system of claim 48, wherein the means for detecting the pattern includes a computing device.