Purpose We describe the design, implementation, and use of a large, multiorganizational distributed database developed to support the Mini-Sentinel Pilot Program of the US Food and Drug Administration (FDA). As envisioned by the US FDA, this implementation will inform and facilitate the development of an active surveillance system for monitoring the safety of medical products (drugs, biologics, and devices) in the USA. Methods A common data model was designed to address the priorities of the Mini-Sentinel Pilot and to leverage the experience and data of participating organizations and data partners. A review of existing common data models informed the process. Each participating organization designed a process to extract, transform, and load its source data, applying the common data model to create the Mini-Sentinel Distributed Database. Transformed data were characterized and evaluated using a series of programs developed centrally and executed locally by participating organizations. A secure communications portal was designed to facilitate queries of the Mini-Sentinel Distributed Database and transfer of confidential data, analytic tools were developed to facilitate rapid response to common questions, and distributed querying software was implemented to facilitate rapid querying of summary data. Results As of July 2011, information on 99 260 976 health plan members was included in the Mini-Sentinel Distributed Database. The database includes 316 009 067 person-years of observation time, with members contributing, on average, 27.0 months of observation time. All data partners have successfully executed distributed code and returned findings to the Mini-Sentinel Operations Center. Conclusion This work demonstrates the feasibility of building a large, multiorganizational distributed data system in which organizations retain possession of their data that are used in an active surveillance system.

The US Food and Drug Administration's Sentinel system has developed the capability to conduct active safety surveillance of marketed medical products in a large network of electronic healthcare databases. We assessed the extent to which the newly developed, semiautomated Sentinel Propensity Score Matching (PSM) tool could produce the same results as a customized protocol-driven assessment, which found an adjusted hazard ratio (HR) of 3.04 (95% confidence interval [CI], 2.81-3.27) comparing angioedema in patients initiating angiotensin-converting enzyme (ACE) inhibitors vs. beta-blockers. Using data from 13 Data Partners between 1 January 2008, and 30 September 2013, the PSM tool identified 2,211,215 eligible ACE inhibitor and 1,673,682 eligible beta-blocker initiators. The tool produced an HR of 3.14 (95% CI, 2.86-3.44). This comparison provides initial evidence that Sentinel analytic tools can produce findings similar to those produced by a highly customized protocol-driven assessment.

When using data from both the ICD-9-CM and ICD-10-CM eras, or when using results from ICD-10-CM data to compare to results from ICD-9-CM data, researchers should test multiple ICD-10-CM outcome definitions as part of sensitivity analysis. Ongoing assessment of the impact of ICD-10-CM transition on identification of health outcomes in US electronic health care databases should occur as more data accrue.