Methods and systems for integrated health systems

• US 8,538,778 B2
• Filed: 05/15/2009
• Issued: 09/17/2013
• Est. Priority Date: 05/15/2008
• Status: Expired due to Fees

• Current Assignee: Soar BioDynamics, Ltd.
• Original Assignee: Soar BioDynamics, Ltd.
• Inventors: Neville, Thomas
• Primary Examiner(s): Morgan, Robert
• Assistant Examiner(s): Coleman, Charles P
• Application Number:
  US12/466,684
• Publication Number:
  US 20100049546A1
• Time in Patent Office: 1,586 Days
• Field of Search: 705/2, 705/3
• US Class Current: 705/3
• 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
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Methods, business methods, and systems are provided herein for integrated healthcare. As the amount of medical information increases rapidly, including information from multiple biomarkers, analysis and management of that information becomes more and more important to extract meaningful conclusions from the information. Statistical and computational methods are described herein that have been created for the methods and systems for integrated healthcare. For example, a computer system is described extracts significance over time of PSA and fPSA biomarker tests for prostate health.

Claims

1. A method of performing a course of medical action for a medical condition of a subject comprising:

a) obtaining a first series of PSA values from a subject;

b) performing a first dynamic analysis using a computer system, comprising;

i) fitting said first series of PSA values to form a first fitted trend;

ii) estimating a first characteristic of said first fitted trend, wherein said first characteristic is a first PSA velocity defined as a rate of change in trend PSA;

iii) calculating one or more probabilities of the occurrence of one or more medical conditions of said subject using said first characteristic, wherein said one or more medical conditions comprises a prostate medical condition selected from the group consisting of the following;

prostatitis due to inflammation prostatitis due to infection prostate cancer, benign prostate hyperplasia, and no prostate disease;

c) determining if the results of said first dynamic analysis have reached a first threshold for medical action, wherein said threshold corresponds to a probability that the subject has a medical condition, wherein the probability is based on analysis of PSA velocity population data; and

d) performing a first course of medical action for a first medical condition if said results of said first dynamic analysis reaches said threshold, wherein said first course of medical action is treatment with antibiotics and/or anti-inflammatory medication;

measurement of prostate volume;

continued PSA tests;

a free PSA test;

a proPSA test;

a biopsy, medical imaging;

or medical treatment for prostate cancer.

2. The method of claim 1, wherein said subject is a human.

3. The method of claim 1, wherein said computer system comprises a device for network communication, a storage unit, and a processor.

4. The method of claim 3, wherein said computer system comprises a Monte Carlo engine.

5. The method of claim 1, wherein said first dynamic analysis further comprises estimating a second characteristic of said first fitted trend, wherein said second characteristic is a first PSA variation.

6. The method of claim 5, further comprising:

e) obtaining a second series of PSA values from said subject, wherein at least one of said second series of PSA values is measured after performing said first course of medical action;

f) performing a second dynamic analysis on a computer system, comprising;

i) comparing said PSA value measured after performing said first course of medical action to said first series of PSA values and said first fitted trend;

ii) estimating a third characteristic of said second series of PSA values, wherein said characteristic is a second PSA velocity or a second PSA variation;

g) determining if the results of said second dynamic analysis have reached a second threshold for medical action, wherein said threshold is based on analysis of population data and the results of said first dynamic analysis; and

h) performing a second course of medical action for a second medical condition when the results of said second dynamic analysis reach said second threshold.

7. The method of claim 5, further comprising:

e) obtaining a second series of PSA values from said subject, wherein at least one of said second series of PSA values is measured after performing said first course of medical action;

f) performing a second dynamic analysis on a computer system, comprising;

i) fitting said second series of PSA values to form a second fitted trend;

ii) estimating a third characteristic of said second fitted trend, wherein said characteristic is a second PSA velocity or a second PSA variation;

g) obtaining the results of said first course of medical action;

h) determining if the results of said second dynamic analysis have reached a second threshold for medical action, wherein said second threshold is based on analysis of population data, the results of said first dynamic analysis, and the results of said first course of medical action; and

i) performing a second course of medical action for a second medical condition if the results of said second dynamic analysis reach said second threshold.

8. The method of claim 1, further comprising:

e) obtaining a second series of PSA values from said subject, wherein at least one of said second series of PSA values is measured after performing said first course of medical action;

f) performing a second dynamic analysis on a computer system, comprising;

i) comparing said PSA value measured after performing said first course of medical action to said first series of PSA values and said first fitted trend;

ii) estimating a second characteristic of said second series of PSA values, wherein said characteristic is a second PSA velocity or a PSA variation;

g) determining if the results of said second dynamic analysis have reached a second threshold for medical action, wherein said threshold is based on analysis of population data and the results of said first dynamic analysis; and

h) performing a second course of medical action for a second medical condition when the results of said second dynamic analysis reach said second threshold.

9. The method of claim 8, wherein said second characteristic is said second PSA velocity, and said second PSA velocity is calculated using a PSA test value obtained before said first course of medical action and said PSA test value measured after said first course of medical action.

10. The method of claim 8, wherein said second characteristic is said PSA variation.

11. The method of claim 8, wherein said first or second course of medical action is treatment with antibiotics and/or anti-inflammatory medication;

measurement of prostate volume;

continued PSA tests;

a free PSA test;

a proPSA test;

a biopsy;

medical imaging;

or medical treatment for prostate cancer.

12. The method of claim 8, wherein said first or second threshold is further based on a personal characteristic of said subject, wherein said personal characteristic is selected from the group consisting of said subject'"'"'s:

age, demographics, life expectancy, Emotional Weights, family history, medical history, and expected side effects of treatment.

13. The method of claim 8, wherein said first or second threshold is calculated using Monte Carlo methods.

14. The method of claim 8, wherein Bayesian analysis is used in the process of determining if the results of said first or second dynamic analysis have reached said first or second threshold for medical action.

15. The method of claim 1, further comprising:

e) obtaining a second series of PSA values from said subject, wherein at least one of said second series of PSA values is measured after performing said first course of medical action;

f) performing a second dynamic analysis on a computer system, comprising;

i) fitting said second series of PSA values to form a second fitted trend;

ii) estimating a second characteristic of said second fitted trend, wherein said characteristic is a second PSA velocity or a PSA variation;

g) obtaining the results of said first course of medical action;

h) determining if the results of said second dynamic analysis have reached a second threshold for medical action, wherein said second threshold is based on analysis of population data, the results of said first dynamic analysis, and the results of said first course of medical action; and

i) performing a second course of medical action for a second medical condition if the results of said second dynamic analysis reach said second threshold.

16. The method of claim 15, wherein said second characteristic is said PSA variation.

17. The method of claim 15, wherein said first or second course of medical action is treatment with antibiotics and/or anti-inflammatory medication;

measurement of prostate volume;

continued PSA tests;

a free PSA test;

a proPSA test;

a biopsy;

medical imaging;

or medical treatment for prostate cancer.

18. The method of claim 15, wherein said second medical condition is selected from the group consisting of:

prostatitis due to inflammation, prostatitis due to infection, prostate cancer at various stages of progression, benign prostatic hyperplasia, and no prostate disease.

19. The method of claim 15, wherein said first and second medical conditions are the same.

20. The method of claim 15, wherein said first and second medical conditions are different.

21. The method of claim 15, wherein said first or second threshold is further based on a personal characteristic of said subject, wherein said personal characteristic is selected from the group consisting of said subject'"'"'s:

age, demographics, life expectancy, Emotional Weights, family history, medical history, and expected side effects of treatment.

22. The method of claim 15, wherein said first or second threshold is calculated using Monte Carlo methods.

23. The method of claim 15, wherein Bayesian analysis is used in the process of determining if the results of said first or second dynamic analysis have reached said first or second threshold for medical action.

24. The method of claim 1 or 15, wherein said fitting is to an exponential function.

25. The method of claim 1, 8, or 15, wherein said first or second threshold is further based on a personal characteristic of said subject, wherein said personal characteristic is selected from the group consisting of said subject'"'"'s:

age, demographics, life expectancy, Emotional Weights, family history, medical history, and expected side effects of treatment.

26. The method of claim 25, wherein said method comprises generating probability distributions based on Monte Carlo simulations that use a database of PSA values measured in a plurality of subjects.

27. The method of claim 26, wherein said method comprises generating probability distributions based on Monte Carlo simulations that use a database of PSA values measured in a plurality of subjects.

28. The method of claim 1, 8, or 15, wherein said first or second threshold is calculated using Monte Carlo methods.

29. The method of claim 1, 8, or 15, wherein Bayesian analysis is used in the process of determining if the results of said first or second dynamic analysis have reached said first or second threshold for medical action.

30. The method of claim 8 or 15, wherein said second medical condition is selected from the group consisting of:

prostatitis due to inflammation, prostatitis due to infection, prostate cancer at various stages of progression, benign prostatic hyperplasia, and no prostate disease.

31. The method of claim 8 or 15, wherein said first and second medical conditions are the same.

32. The method of claim 8 or 15, wherein said first and second medical conditions are different.

33. A method comprising:

a. receiving a series of PSA values of a subject;

b. performing a dynamic analysis using a computer system, comprising;

i) fitting said series of PSA values to form a fitted trend;

ii) estimating a characteristic of said fitted trend, wherein said characteristic is a PSA velocity defined as a rate of change in trend PSA;

iii) calculating one or more probabilities of the occurrence of one or more medical conditions of said subject using said first characteristic, wherein said one or more medical conditions comprises a prostate medical condition selected from the group consisting of the following;

prostatitis due to inflammation, prostatitis due to infection, prostate cancer, benign prostate hyperplasia, and no prostate disease;

c. determining if the results of said dynamic analysis have reached a first threshold for medical action for a medical condition, wherein said threshold corresponds to a probability that the subject has a medical condition, wherein the probability is based on analysis of PSA velocity population data; and

d. delivering the results of said determining step to a user for use in selecting a course of medical action, wherein said course of medical action is treatment with antibiotics and/or anti-inflammatory medication;

measurement of prostate volume;

continued PSA tests;

a free PSA test;

a proPSA test;

a biopsy, medical imaging;

or medical treatment for prostate cancer.

34. The method of claim 33, wherein the series of PSA values are received from a user.

35. The method of claim 34, wherein the user is selected from the group consisting of the following:

a physician, a health care provider, a pharmacy, an insurance company, and the subject.

36. The method of claim 34, wherein said series of PSA values is received from said user through a webpage.

37. The method of claim 34, wherein said series of PSA values is received from said user through an electronic device.

38. The method of claim 34, wherein said series of PSA values is received from said user through an assay device.

39. The method of claim 33, wherein the series of PSA values is received from a device.

40. The method of claim 39, wherein the device is selected from the group consisting of the following:

a lab test device, a point-of-care assay device, a personal electronic device, an electronic medical record, and a computer system.

41. The method of claim 33, wherein said computer system comprises a Monte Carlo engine.

42. The method of claim 33, wherein the results of said determining step are delivered to a user through an electronic medical record.

43. The method of claim 33, wherein the results of said determining step are delivered to a user through a webpage.

44. The method of claim 33, wherein said the results of said determining step are delivered to a user through an electronic device with a display.

45. The method of claim 33, wherein the results of said determining step are delivered to a user through a printout.

46. The method of claim 33, wherein said computer system comprises a processor, a storage unit, and a device for network communication.

47. The method of claim 33, wherein the method is carried out for a fee.

48. The method of claim 47, wherein each delivery of the results of said determining step is carried out for a fee.

49. The method of claim 33 further comprising suggesting a course of medical action to said user based on said results of said determining step or the results of said dynamic analysis.

50. The method of claim 49, wherein said suggesting is provided for a fee.