A centralized informatics infrastructure for the National Institute on Drug Abuse Clinical Trials Network

Author:

Pan Jeng-Jong1,Nahm Meredith2,Wakim Paul3,Cushing Carol3,Poole Lori4,Tai Betty3,Pieper Carl F5

Affiliation:

1. Veterans Health Administration, Washington, DC

2. Duke Translational Medicine Institute, Durham, NC,

3. National Institute on Drug Abuse, Center for the Clinical Trials Network, Bethesda, MD

4. Duke Clinical Research Institute, Durham, NC

5. Biostatistics and Bioinformatics Department, Duke University Medical Center, Durham, NC

Abstract

Background Clinical trial networks (CTNs) were created to provide a sustaining infrastructure for the conduct of multisite clinical trials. As such, they must withstand changes in membership. Centralization of infrastructure including knowledge management, portfolio management, information management, process automation, work policies, and procedures in clinical research networks facilitates consistency and ultimately research. Purpose In 2005, the National Institute on Drug Abuse (NIDA) CTN transitioned from a distributed data management model to a centralized informatics infrastructure to support the network's trial activities and administration. We describe the centralized informatics infrastructure and discuss our challenges to inform others considering such an endeavor. Methods During the migration of a clinical trial network from a decentralized to a centralized data center model, descriptive data were captured and are presented here to assess the impact of centralization. Results We present the framework for the informatics infrastructure and evaluative metrics. The network has decreased the time from last patient-last visit to database lock from an average of 7.6 months to 2.8 months. The average database error rate decreased from 0.8% to 0.2%, with a corresponding decrease in the interquartile range from 0.04%—1.0% before centralization to 0.01—0.27% after centralization. Centralization has provided the CTN with integrated trial status reporting and the first standards-based public data share. A preliminary cost-benefit analysis showed a 50% reduction in data management cost per study participant over the life of a trial. Limitations A single clinical trial network comprising addiction researchers and community treatment programs was assessed. The findings may not be applicable to other research settings. Conclusions The identified informatics components provide the information and infrastructure needed for our clinical trial network. Post centralization data management operations are more efficient and less costly, with higher data quality. Clinical Trials 2009; 6: 67—75. http://ctj.sagepub.com

Publisher

SAGE Publications

Subject

Pharmacology,General Medicine

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