SYSTEM AND METHOD FOR USING PREDICTIVE MODELS TO DETERMINE LEVELS OF HEALTHCARE INTERVENTIONS

• US 20110071363A1
• Filed: 11/11/2009
• Published: 03/24/2011
• Est. Priority Date: 09/22/2009
• Status: Abandoned Application

• Current Assignee: Healthways Incorporated
• Original Assignee: Healthways Incorporated
• Inventors: Montijo, Michael, Hobgood, Adam, Grant, Jason, Turner, John
• Application Number:
  US12/616,754
• Publication Number:
  US 20110071363A1
• Time in Patent Office: Days
• US Class Current: 600/300
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A computer implemented method may be used for targeting patients within a population for increased medical interventions. In one embodiment, predictive models are used to determine whether a patient is likely to be near the end of life, whether the patient'"'"'s healthcare costs are stabilizing, whether the patient is likely to have high future healthcare costs, and whether the patient is likely to have high future clinical risk. In one embodiment, scores from these predictive models are used to determine the level of medical intervention for a particular patient.

Claims

1. A computerized method for targeting a group of patients for medical care, the method comprising:

receiving health information about a plurality of patients;

selecting a subset of the plurality of patients;

comparing the relative associated future healthcare costs and clinical risk of the subset of the plurality of patients; and

assigning a level of medical intervention for each of the patients in the selected subset based on the comparison of the patients.

2. The method of claim 1, wherein the received health information comprises healthcare insurance claims information.

3. The method of claim 1, further comprising creating a medical care plan based on the assigned level of medical intervention.

4. The method of claim 1, wherein selecting a plurality of patients comprises selecting a plurality of patients based on the likelihood of each patient dying within a particular time range.

5. The method of claim 1, wherein selecting a plurality of patients comprises selecting a plurality of patients based on the likelihood of the future healthcare costs for each patient stabilizing.

6. The method of claim 1, wherein assigning a level of medical intervention comprises assigning patients with high future healthcare costs and high clinical risk to an increased level of medical intervention.

7. The method of claim 1, further comprising periodically reassigning patients to a different level of medical intervention.

8. The method of claim 1, further comprising assigning the non-selected patients to a level of medical intervention.

9. The method of claim 1, wherein a computing device assigns the level of medical intervention.

10. The method of claim 1, further comprising delivering the assigned level of medical intervention to a patient in the selected subset.

11. The method of claim 1, wherein selecting the subset of the plurality of patients comprises both selecting a plurality of patients based on the likelihood of each patient dying within a particular time range and selecting a plurality of patients based on the likelihood of the future healthcare costs for each patient stabilizing.

12. A computerized method for targeting a group of patients for medical care, the method comprising:

receiving health information associated with each of a plurality of patients;

receiving output from multiple predictive models about the plurality of patients;

comparing a subset of the plurality of patients relative to one another based on the output of the multiple predictive models; and

assigning a level of medical intervention for each of the patients based on the comparison of the patients.

13. The method of claim 12, wherein the received health information comprises healthcare insurance claims information.

14. The method of claim 12, further comprising creating a medical care plan based on the assigned level of medical intervention.

15. The method of claim 12, wherein a computing device assigns the level of medical intervention.

16. The method of claim 12, wherein the output from the multiple predictive models comprises output indicative of each patient'"'"'s likelihood of dying in the near term.

17. The method of claim 12, wherein the output from the multiple predictive models comprises output indicative of the likelihood of each patient'"'"'s healthcare costs stabilizing.

18. The method of claim 12, wherein the output from the multiple predictive models comprises output indicative of the relative level of each patient'"'"'s future healthcare costs.

19. The method of claim 12, wherein the output from the multiple predictive models comprises output indicative of each patient'"'"'s relative future clinical risk.

20. The method of claim 12, wherein patients with an associated predictive model output indicative of a higher relative level of future healthcare costs and a higher relative future clinical risk are assigned to an increased level of medical intervention.

21. The method of claim 12, wherein the levels of medical intervention comprise a surveillance level, an end of life level, and an intermediate level of medical intervention.

22. The method of claim 12, further comprising periodically reassigning patients to a different level of medical intervention.

23. The method of claim 12, further comprising delivering the assigned level of medical intervention to a patient based on the comparison of the patients.

24. The method of claim 12, wherein the output from the multiple predictive models comprises two or more outputs selected from the group consisting of output indicative of each patient'"'"'s likelihood of dying in the near term, output indicative of the likelihood of each patient'"'"'s healthcare costs stabilizing, output indicative of the relative level of each patient'"'"'s future healthcare costs and output indicative of each patient'"'"'s relative future clinical risk.

25. The method of claim 12, wherein the output from the multiple predictive models comprises:

output indicative of each patient'"'"'s likelihood of dying in the near term;

output indicative of the likelihood of each patient'"'"'s healthcare costs stabilizing;

output indicative of the relative level of each patient'"'"'s future healthcare costs; and

output indicative of each patient'"'"'s relative future clinical risk.

26. A computerized method for targeting a group of patients for medical care, the method comprising:

selecting a plurality of patients;

determining an estimated medical factor associated with each patient;

comparing the patients based on the associated medical factor of each patient; and

assigning a level of medical intervention for each patient based on the relative estimated medical factor of each patient.

27. The method of claim 26, wherein the estimated medical factor comprises the estimated future medical costs associated with each patient.

28. The method of claim 27, wherein the estimated medical factor further comprises the estimated future clinical risk associated with each patient.

29. The method of claim 28, wherein patients with a high estimated future medical costs and high estimated clinical risk relative to the other patients are assigned to an increased level of medical intervention.

30. The method of claim 26, wherein selecting a plurality of patients comprises selecting a plurality of patients based on the likelihood of each patient dying within a particular time range.

31. The method of claim 26, wherein selecting a plurality of patients comprises selecting a plurality of patients based on the likelihood of the future medical costs for each patient stabilizing.

32. The method of claim 26, wherein determining an estimated medical factor comprises analyzing output from one or more predictive models.

33. The method of claim 32, wherein the one or more predictive models are neural networks.

34. The method of claim 26, wherein the levels of medical intervention comprise a surveillance level and active care level of medical intervention.

35. The method of claim 26, further comprising creating a medical plan based on the assigned level of medical intervention.

36. The method of claim 26, further comprising assigning the non-selected patients to a level of medical intervention.

37. The method of claim 26, wherein a computing device assigns the level of medical intervention.

38. The method of claim 26, wherein selecting a plurality of patients comprises both selecting a plurality of patients based on the likelihood of each patient dying within a particular time range and selecting a plurality of patients based on the likelihood of the future healthcare costs for each patient stabilizing.

39. The method of claim 26, further comprising delivering the assigned level of medical intervention to a patient based on the relative estimated medical factor of said patient.

40. A computerized system for targeting a group of patients for medical care, the system comprising:

a memory to store health information about a plurality of patients;

a processor to perform;

selecting a subset of the plurality of patients;

comparing the relative associated future healthcare costs and clinical risk of the subset of the plurality of patients; and

assigning a level of medical intervention for each of the patients in the selected subset based on the comparison of the patients.

41. The system of claim 40, wherein the received health information comprises healthcare insurance claims information.

42. The system of claim 40, wherein the processor further performs creating a medical care plan based on the assigned level of medical intervention.

43. The system of claim 40, wherein selecting a plurality of patients comprises selecting a plurality of patients based on the likelihood of each patient dying within a particular time range.

44. The system of claim 40, wherein selecting a plurality of patients comprises selecting a plurality of patients based on the likelihood of the future healthcare costs for each patient stabilizing.

45. The system of claim 40, wherein assigning a level of medical intervention comprises assigning patients with high future healthcare costs and high clinical risk to an increased level of medical intervention.

46. The system of claim 40, wherein the processor further performs periodically reassigning patients to a different level of medical intervention.

47. The system of claim 40, wherein the processor further performs assigning the non-selected patients to a level of medical intervention.

48. The system of claim 40, wherein selecting the subset of the plurality of patients comprises both selecting a plurality of patients based on the likelihood of each patient dying within a particular time range and selecting a plurality of patients based on the likelihood of the future healthcare costs for each patient stabilizing.

49. A computerized system for targeting a group of patients for medical care, the system comprising:

a memory to store health information associated with each of a plurality of patients;

a processor to perform;

receiving output from multiple predictive models about the plurality of patients;

comparing a subset of the plurality of patients relative to one another based on the output of the multiple predictive models; and

assigning a level of medical intervention for each of the patients based on the comparison of the patients.

50. The system of claim 49, wherein the received health information comprises healthcare insurance claims information.

51. The system of claim 49, wherein the processor further performs creating a medical care plan based on the assigned level of medical intervention.

52. The system of claim 49, wherein the output from the multiple predictive models comprises output indicative of each patient'"'"'s likelihood of dying in the near term.

53. The system of claim 49, wherein the output from the multiple predictive models comprises output indicative of the likelihood of each patient'"'"'s healthcare costs stabilizing.

54. The system of claim 49, wherein the output from the multiple predictive models comprises output indicative of the relative level of each patient'"'"'s future healthcare costs.

55. The method of claim 49, wherein the output from the multiple predictive models comprises output indicative of each patient'"'"'s relative future clinical risk.

56. The system of claim 49, wherein patients with an associated predictive model output indicative of a higher relative level of future healthcare costs and a higher relative future clinical risk are assigned to an increased level of medical intervention.

57. The system of claim 49, wherein the levels of medical intervention comprise a surveillance level, an end of life level, and an intermediate level of medical intervention.

58. The system of claim 49, wherein the processor further performs periodically reassigning patients to a different level of medical intervention.

59. The system of claim 49, wherein the output from the multiple predictive models comprises two or more outputs selected from the group consisting of output indicative of each patient'"'"'s likelihood of dying in the near term, output indicative of the likelihood of each patient'"'"'s healthcare costs stabilizing, output indicative of the relative level of each patient'"'"'s future healthcare costs and output indicative of each patient'"'"'s relative future clinical risk.

60. The system of claim 49, wherein the output from the multiple predictive models comprises:

output indicative of each patient'"'"'s likelihood of dying in the near term;

output indicative of the likelihood of each patient'"'"'s healthcare costs stabilizing;

output indicative of the relative level of each patient'"'"'s future healthcare costs; and

output indicative of each patient'"'"'s relative future clinical risk.

61. A computerized system for targeting a group of patients for medical care, the system comprising:

a processor to perform;

selecting a plurality of patients;

determining an estimated medical factor associated with each patient;

comparing the patients based on the associated medical factor of each patient; and

assigning a level of medical intervention for each patient based on the relative estimated medical factor of each patient.

62. The system of claim 61, wherein the estimated medical factor comprises the estimated future medical costs associated with each patient.

63. The system of claim 62, wherein the estimated medical factor further comprises the estimated future clinical risk associated with each patient.

64. The system of claim 63, wherein patients with a high estimated future medical costs and high estimated clinical risk relative to the other patients are assigned to an increased level of medical intervention.

65. The system of claim 63, wherein selecting a plurality of patients comprises selecting a plurality of patients based on the likelihood of each patient dying within a particular time range.

66. The system of claim 61, wherein selecting a plurality of patients comprises selecting a plurality of patients based on the likelihood of the future medical costs for each patient stabilizing.

67. The system of claim 61, wherein determining an estimated medical factor comprises analyzing output from one or more predictive models.

68. The system of claim 67, wherein the one or more predictive models are neural networks.

69. The system of claim 61, wherein the levels of medical intervention comprise a surveillance level and active care level of medical intervention.

70. The system of claim 61, wherein the processor further performs creating a medical plan based on the assigned level of medical intervention.

71. The system of claim 61, wherein the processor further performs assigning the non-selected patients to a level of medical intervention.

72. The system of claim 61, wherein selecting a plurality of patients comprises both selecting a plurality of patients based on the likelihood of each patient dying within a particular time range and selecting a plurality of patients based on the likelihood of the future healthcare costs for each patient stabilizing.