Background: To assess the current state of clinical data interoperability, we evaluated the use of data standards across 38 large professional society registries.
Methods: The analysis included 4 primary components: 1) environmental scan, 2) abstraction and cross-tabulation of clinical concepts and corresponding data elements from registry case report forms, dictionaries, and / or data models, 3) cross-tabulation of same across national common data models, and 4) specifying data element metadata to achieve native data interoperability.
Results: The registry analysis identified approximately 50 core clinical concepts. None were captured using the same data representation across all registries, and there was little implementation of data standards. To improve technical implementation, we specified 13 key metadata for each concept to be used to achieve data consistency.
Conclusion: The registry community has not benefitted from and does not contribute to interoperability efforts. A common, authoritative process to specify and implement common data elements is greatly needed.
Learning Objective: After participating in this session, the learner will be able to articulate recommendations to achieve clinical data liquidity through native data interoperability, per findings of the Duke-Pew Data Interoperability Project, an analysis of the representations of more than 20 common core clinical concepts found in 38 large professional society registries.
James Tcheng (Presenter)
Duke Clinical Research Institute
Joseph Drozda, Mercy Health System
Davera Gabriel, Duke Clinical Research Institute
Anne Heath, Duke Clinical Research Institute
Rebecca Wilgus, Duke Clinical Research Institute
Mary Williams, Duke Clinical Research Institute
Thomas Windle, University of Nebraska Medical Center
John Windle, University of Nebraska Medical Center