4.6 Article

A federated EHR network data completeness tracking system

Journal

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocz014

Keywords

data completeness; data quality; electronic health records; systems thinking

Funding

  1. Patient-Centered Outcomes Research Institute Award [CDRN-1306-04608]
  2. National Human Genome Research Institute [R01-HG009174, U01-HG008685]
  3. National Institute on Minority Health and Health Disparities [U54MD008149, 8U54MD007588, U54MD008173]
  4. National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health [UL1 TR000371, UL1TR002378, UL1 TR001105, UL1 TR001857, U01 TR002393]
  5. National Library of Medicine grant [R01 LM011829]
  6. Cancer Prevention Research Institute of Texas (CPRIT) Data Science and Informatics Core for Cancer Research [RP170668]
  7. Reynolds and Reynolds Professorship in Clinical Informatics

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Objective: The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network. Materials and Methods: The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQ(e)-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action. A pilot implementation of the system involved 6 ARCH partner sites between January 2017 and May 2018. Results: The ARCH CTX has enabled the network to monitor and, if needed, adjust its data management processes to maintain complete datasets for secondary use. The system allows the network and its partner sites to profile data completeness both at the network and partner site levels. Interactive visualizations presenting the current state of completeness in the context of the entire network as well as changes in completeness across time were valued among the CTX user base. Discussion: Distributed clinical data networks are complex systems. Top-down approaches that solely rely on technology to report data completeness may be necessary but not sufficient for improving completeness (and quality) of data in large-scale clinical data networks. Improving and maintaining complete (high-quality) data in such complex environments entails sociotechnical systems that exploit technology and empower human actors to engage in the process of high-quality data curating. Conclusions: The CTX has increased the network's capacity to rapidly identify data completeness issues and empowered ARCH partner sites to get involved in improving the completeness of respective data in their repositories.

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