Journal
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Volume 28, Issue 7, Pages 1440-1450Publisher
OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocab023
Keywords
learning health system; clinical data warehouse; health information interoperability; application programming interfaces
Categories
Funding
- National Center for Advancing Translational Sciences [UL1 TR001450]
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The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina successfully reduced data fragmentation and promoted research systems integration by implementing RMIDs and RINS. Within four years, a total of 5513 RMIDs were created, with 13% linked to systems needed to evaluate research study performance and 18% linked to electronic health records.
Objective: Integrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier-the Research Master Identifier (RMID)-for tracking research studies across disparate systems and a data warehouse-inspired model-the Research Integrated Network of Systems (RINS)-for integrating data from those systems. Materials and Methods: In 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes. Results: Within 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patientlevel reporting. Discussion: Barriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart. Conclusion: By applying data warehousing principles to federate data at the study level, the RINS project reduced data fragmentation and promoted research systems integration.
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