Apparatus and method for prediction and management of subject compliance in clinical research

• US 20020143577A1
• Filed: 04/02/2001
• Published: 10/03/2002
• Est. Priority Date: 04/02/2001
• Status: Active Grant

• Current Assignee: eResearchTechnology, Inc. (Explorer Holdings, Inc.)
• Original Assignee: Invivodata Incorporated (Explorer Holdings, Inc.)
• Inventors: Shiffman, Saul, Paty, Jean A., Hufford, Michael R.
• Granted Patent: US 6,879,970 B2
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 90/00 Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 20/00 ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 70/20 relating to practices or guidelines
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system for developing and implementing empirically derived algorithms to generate decision rules to predict subject noncompliance and fraud with research protocols in clinical trials allows for the identification of complex patterns of variables that detect or predict subject noncompliance and fraud with research protocol in the clinical trial. The present invention can also be used to monitor subject compliance with the research protocol to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of noncompliance, from minor noncompliance needing only corrective feedback, to significant noncompliance requiring subject removal from the clinical trial. The algorithms and decision rules can also be domain-specific, such as detecting non-compliance or fraud among subjects in a cardiovascular drug trial, or demographically specific, such as taking into account gender or age which provides for algorithms and decision rules to be optimized for the specific sample of subjects being studied.

Claims

1. A method of predicting subject noncompliance, comprising the steps of:

providing historical subject compliance data;

generating at least one predictive algorithm for predicting subject noncompliance by quantitative analysis of the historical subject compliance; and

translating the at least one predictive algorithm into at least one prediction rule for use with a clinical trial.

2. The method of predicting subject noncompliance of claim 1, further comprising the steps of:

obtaining subject compliance information; and

comparing the subject compliance information to the at least one prediction rule to determine if action is needed.

3. The method of predicting subject noncompliance of claim 1, wherein said step of providing includes providing historical protocol data and wherein said step of generating includes quantitative analysis of the historical protocol data.

4. The method of predicting subject noncompliance of claim 2, further comprising the step of determining an appropriate action if the step of comparing indicates that action is needed.

5. The method of predicting subject noncompliance of claim 2, wherein the step of obtaining includes the step of employing a portable electronic device capable of displaying information and receiving and storing input from a user.

6. The method of predicting subject noncompliance of claim 2, further comprising the step of creating an evaluability database adapted to store data related to subject compliance.

7. The method of predicting subject noncompliance of claim 6, wherein the evaluability database is tailored to a condition affecting the subject.

8. The method of determining subject noncompliance of claim 1, wherein the step of providing employs at least one database containing at least one of the group of the historical subject compliance data and the historical protocol data.

9. A method of determining subject noncompliance, comprising the steps of:

providing at least one of the group of historical subject compliance data and historical protocol data;

generating at least one algorithm reflective of at least one of the group of the historical subject compliance data and the historical protocol data by quantitative analysis of the historical subject compliance data and the historical protocol data;

translating the at least one algorithm into at least one decision rule for analyzing subject compliance information;

obtaining the subject compliance information; and

comparing the subject compliance information to the at least one decision rule to determine if corrective action is needed.

10. The method of determining subject noncompliance of claim 9, further comprising the step of determining an appropriate corrective action if the step of comparing indicates that corrective action is needed.

11. The method of determining subject noncompliance of claim 9, wherein the step of obtaining includes using a portable electronic device capable of displaying information and receiving and storing input from a user.

12. The method of determining subject noncompliance of claim 9, wherein the step of generating employs at least one of the group of multiple linear regression, discriminant function analysis, logistic regression, neural networks, classification trees and regression trees.

13. The method of determining subject noncompliance of claim 9, wherein the step of providing employs at least one database containing at least one of the group of the historical subject compliance data and the historical protocol data.

15. The method of determining subject noncompliance of claim 14, further comprising the step of determining an appropriate corrective action if the step of comparing indicates that corrective action is needed.

16. The method of determining subject noncompliance of claim 15, wherein the step of obtaining includes using a portable electronic device capable of displaying information and receiving and storing input from a user.

19. The method of detecting subject fraud of claim 18, further comprising the steps of:

comparing the subject compliance information to the at least one fraud detection rule to determine if action is needed.

20. The method of detecting subject fraud of claim 19, further comprising the step of determining an appropriate action if the step of comparing indicates that action is needed.

21. The method of detecting subject fraud of claim 19, wherein the step of providing includes the use of a portable electronic device capable of displaying information and receiving and storing input from a user.

22. The method of detecting subject fraud of claim 19, further comprising the step of creating an evaluability database adapted to store data related to subject fraud.

23. The method of detecting subject fraud of claim 22, wherein the evaluability database is tailored to a condition affecting the subject.

24. The method of detecting subject fraud of claim 18, wherein the step of providing employs at least one database containing at least one of the group of the historical subject compliance data and the historical protocol data.

26. The medium of claim 25 having instructions further comprising the steps of:

obtaining subject compliance information; and

comparing the subject compliance information to the at least one prediction rule to determine if action is needed.

27. The medium of claim 25, wherein the step of obtaining includes the use of a portable electronic device capable of displaying information and receiving and storing input from a user.

28. The medium of claim 25 having instructions further comprising the step of creating an evaluability database adapted to store data related to subject compliance.

30. The medium of claim 29 having instructions further comprising the step of determining an appropriate corrective action if the step of comparing indicates that corrective action is needed.

31. The medium of claim 29, wherein the step of obtaining includes using a portable electronic device capable of displaying information and receiving and storing input from a user.

32. The medium of claim 29, wherein the step of generating employs at least one of the group of multiple linear regression, discriminant function analysis, logistic regression, neural networks, classification trees and regression trees.

14. A method of determining subject noncompliance, comprising the steps of:

providing historical subject compliance data and historical protocol data;

generating a spectrum of noncompliance representative of the historical subject compliance data not compliant with the historical protocol data by quantitative analysis of the historical subject compliance data and the historical protocol data;

obtaining subject compliance information; and

comparing the spectrum of noncompliance to the subject compliance information to determine if corrective action is needed.

17. A method of detecting subject fraud, comprising the steps of:

providing historical subject compliance data and historical protocol data;

generating at least one fraud detection algorithm for detecting subject fraud by quantitative analysis of the historical subject compliance data and the historical protocol data; and

translating the at least one fraud detection algorithm into at least one fraud detection rule for use with a clinical trial.

18. A method of detecting subject fraud, comprising the steps of:

providing subject compliance data;

generating at least one fraud detection algorithm for detecting subject fraud by quantitative analysis of the compliance data; and

translating the at least one fraud detection algorithm into at least one fraud detection rule for use with a clinical trial.

25. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:

providing at least one of the group of historical subject compliance data and historical protocol data;

generating at least one predictive algorithm for predicting subject noncompliance by quantitative analysis of at least one of the group of the historical subject compliance data and the historical protocol data; and

translating the at least one predictive algorithm into at least one prediction rule for use with a clinical trial.

29. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:

providing at least one of the group of historical subject compliance data and historical protocol data;

generating at least one algorithm reflective of at least one of the group of the historical subject compliance data and the historical protocol data by quantitative analysis of the historical subject compliance data and the historical protocol data;

translating the at least one algorithm into at least one decision rule for analyzing subject compliance information;

obtaining the subject compliance information; and

comparing the subject compliance information to the at least one decision rule to determine if corrective action is needed.

33. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:

providing historical subject compliance data and historical protocol data;

generating a spectrum of noncompliance representative of the historical subject compliance data not compliant with the historical protocol data by quantitative analysis of the historical subject compliance data and the historical protocol data;

obtaining subject compliance information; and

comparing the spectrum of noncompliance to the subject compliance information to determine if corrective action is needed.

34. The medium of claim 33 having instructions further comprising the step of determining an appropriate corrective action if the step of comparing indicates that corrective action is needed.

35. The medium of claim 34, wherein the step of obtaining includes using a portable electronic device capable of displaying information and receiving and storing input from a user.

36. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:

providing historical subject compliance data and historical protocol data;

generating at least one fraud detection algorithm for detecting subject fraud by quantitative analysis of the historical subject compliance data and the historical protocol data; and

translating the at least one fraud detection algorithm into at least one fraud detection rule for use with a clinical trial.

37. A medium suitable for use in an electronic device and having instructions for execution on the electronic device, the instructions comprising the steps of:

providing subject compliance data;

generating at least one fraud detection algorithm for detecting subject fraud by quantitative analysis of the compliance data; and

translating the at least one fraud detection algorithm into at least one fraud detection rule for use with a clinical trial.