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

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.

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.

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.

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.

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.

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.

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.

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.

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.