Remote clinical study site monitoring and data quality scoring

• US 8,706,537 B1
• Filed: 11/16/2012
• Issued: 04/22/2014
• Est. Priority Date: 11/16/2012
• Status: Active Grant

• Current Assignee: Medidata Solutions Incorporated (Dassault Systemes SA)
• Original Assignee: Medidata Solutions Incorporated (Dassault Systemes SA)
• Inventors: Young, Stephen, Marinelli, Vincent, Laudanovic, Mladen, Kring, Anson, Kopelman, Andrew
• Primary Examiner(s): Jarrett, Scott L
• Application Number:
  US13/679,533
• Time in Patent Office: 522 Days
• Field of Search: 705/7.41, 705/7.39
• US Class Current: 705/7.11
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 70/40 relating to drugs, e.g. their side effects or intended usage
• CPC Class Codes: G16H 80/00 ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Abstract

A method for remote site monitoring includes receiving data from a data site, converting those data into a site-level quality score using a metric risk profile, and calculating a risk indicator based on the site-level quality score. The metric risk profile may be based on historic data and study data, where the study data is received from a plurality of data sites. In some embodiments, converting the data includes normalizing a value of a metric by applying to it the metric risk profile and aggregating the normalized metric values to calculate the site-level quality score. An apparatus for remote site monitoring is also described.

Claims

1. A computer-implemented method, comprising:

receiving site data from one or more clinical sites, the site data including clinical monitoring data;

converting, by a processor, the site data into a site-level data quality score using at least one single-sided or double-sided metric risk profile, wherein;

the site-level data quality score is based on at least two metrics; and

the at least one metric risk profile is based on at least clinical study data and historic clinical study data, the clinical study data being received from a plurality of clinical sites and the historic clinical study data having a statistical distribution and a benchmark related to a metric;

calculating a risk indicator based on the site-level data quality score; and

generating, by a processor, the at least one metric risk profile in at least substantially real-time by;

calculating a clinical study benchmark for at least one of the metrics based on the clinical study data;

automatically scaling the historic clinical study data statistical distribution by a ratio of the clinical study benchmark to the historic clinical study data benchmark; and

assigning risk regions, utilizing measures of statistical significance, to the scaled statistical distribution; and

outputting at least one of said risk indicator and said site-level data quality score.

2. The method of claim 1, further comprising:

receiving site data from a plurality of clinical sites; and

calculating a higher-level data quality score for the plurality of clinical sites.

3. The method of claim 2, further comprising calculating a second risk indicator based on the higher-level data quality score.

4. The method of claim 2, wherein the plurality of clinical sites comprise sites having a common element.

5. The method of claim 2, wherein the plurality of clinical sites comprise sites from a single country.

6. The method of claim 2, wherein the plurality of clinical sites comprise sites from an entire data study.

7. The method of claim 1, wherein the converting of the site data comprises:

calculating a plurality of metric values from the site data;

normalizing each metric value by applying to it a corresponding metric risk profile; and

aggregating the normalized metric values to calculate the site-level data quality score.

8. The method of claim 7, wherein each metric value is weighted based on significance of the metric to the data quality of the clinical site.

9. The method of claim 1, wherein the risk regions correspond to percentiles of the historic data distribution modified by the ratio.

10. The method of claim 8, wherein the aggregating comprises combining weighted, normalized metric values.

11. The method of claim 8, wherein the weighting applied to each metric value is dynamically determined from site-based feedback concerning actual site data quality conditions.

12. An apparatus, comprising:

a site data filter configured to receive site data from one or more clinical sites, the site data including clinical monitoring data;

a processor configured to;

convert the site data into a site-level data quality score using at least one single-sided or double-sided metric risk profile, wherein;

the site-level data quality score is based on at least two metrics; and

the at least one metric risk profile is based on at least clinical study data and historic clinical study data, the clinical study data being received from a plurality of clinical sites and the historic clinical study data having a statistical distribution and a benchmark related to a metric;

calculate a risk indicator based on the site-level data quality score; and

generate the at least one metric risk profile in at least substantially real-time by;

calculating a clinical study benchmark for at least one of the metrics based on the clinical study data;

automatically scaling the historic clinical study data statistical distribution by a ratio of the clinical study benchmark to the historic clinical study data benchmark; and

assigning risk regions, utilizing measures of statistical significance, to the scaled statistical distribution; and

a component configured to output at least one of said risk indicator and said site-level data quality score.

13. The apparatus of claim 12, wherein:

the site data filter is configured to receive site data from a plurality of clinical sites; and

the processor is configured to calculate a higher-level data quality score for the plurality of clinical sites.

14. The apparatus of claim 13, wherein the processor is configured to calculate a second risk indicator based on the higher-level data quality score.

15. The apparatus of claim 13, wherein the plurality of clinical sites comprise sites having a common element.

16. The apparatus of claim 13, wherein the plurality of clinical sites comprise sites from a single country.

17. The apparatus of claim 13, wherein the plurality of clinical sites comprise sites from an entire data study.

18. The apparatus of claim 12, wherein the processor further comprises a site risk profiler configured to calculate the risk indicator based on the site-level data quality score.

19. The apparatus of claim 12, wherein the processor further comprises:

a site metric processor configured to calculate a plurality of metric values from the site data;

a metric risk profiler configured to normalize each metric value by applying to it a corresponding metric risk profile; and

an aggregator configured to aggregate the normalized metric values to calculate the site-level data quality score.

20. The apparatus of claim 19, wherein each metric value is weighted based on significance of the metric to the data quality of the clinical site.

21. The apparatus of claim 20, wherein the aggregating comprises combining weighted, normalized metric values.

22. The apparatus of claim 20, wherein the weighting applied to each metric value is dynamically determined from site-based feedback concerning actual site data quality conditions.

23. The apparatus of claim 12, wherein the risk regions correspond to percentiles of the historic data distribution modified by the ratio.

24. A computer readable storage medium, comprising computer executable instructions embodied therein, to be executed by a computer, for:

receiving site data from one or more clinical sites, the site data including clinical monitoring data;

converting, by a processor, the site data into a site-level data quality score using at least one single-sided or double-sided metric risk profile, wherein;

the site-level data quality score is based on at least two metrics; and

the at least one metric risk profile is based on at least clinical study data and historic clinical study data, the clinical study data being received from a plurality of clinical sites and the historic clinical study data having a statistical distribution and a benchmark related to a metric;

calculating a risk indicator based on the site-level data quality score; and

generating, by a processor, the at least one metric risk profile in at least substantially real-time by;

calculating a clinical study benchmark for at least one of the metrics based on the clinical study data;

automatically scaling the historic clinical study data statistical distribution by a ratio of the clinical study benchmark to the historic clinical study data benchmark; and

assigning risk regions, utilizing measures of statistical significance, to the scaled statistical distribution; and

outputting at least one of said risk indicator and said site-level data quality score.

25. The computer readable storage medium of claim 24, further comprising computer executable instructions embodied therein, to be executed by a computer, for:

receiving site data from a plurality of clinical sites; and

calculating a higher-level data quality score for the plurality of clinical sites.

26. The computer readable storage medium of claim 25, further comprising computer executable instructions embodied therein, to be executed by a computer, for calculating a second risk indicator based on the higher-level data quality score.

27. The computer readable storage medium of claim 24, further comprising computer executable instructions embodied therein, to be executed by a computer, for:

calculating a plurality of metric values from the site data;

normalizing each metric value by applying to it a corresponding metric risk profile; and

aggregating the normalized metric values to calculate the site-level data quality score.

28. The computer readable storage medium of claim 27, wherein each metric value is weighted based on significance of the metric to the data quality of the clinical site.

29. The computer readable storage medium of claim 28, wherein the weighting applied to each metric value is dynamically determined from site-based feedback concerning actual site data quality conditions.