Year: 2019
Type: article
Abstract: 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 (DQe-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.
DOI: 10.1093/jamia/ocz014
Authors: Hossein Estiri, Jeffrey G Klann, Sarah R Weiler, Ernest Alema-Mensah, R Joseph Applegate, Galina Lozinski, Nandan Patibandla, Kun Wei, William G Adams, Marc D Natter, Elizabeth O Ofili, Brian Ostasiewski, Alexander Quarshie, Gary E Rosenthal, Elmer V Bernstam, Kenneth D Mandl, Shawn N Murphy
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,332
Funders: Cancer Prevention and Research Institute of Texas, Patient-Centered Outcomes Research Institute, National Institutes of Health, National Human Genome Research Institute, U.S. National Library of Medicine, National Center for Advancing Translational Sciences, National Institute on Minority Health and Health Disparities
Awards: G1540669808, G196810821, G200861231, G2223263931, G2436063892, G265186493, Data Science and Informatics Core for Cancer Research, G3395997892, G3575224597, G3641532177, G4808617802, G499514328, G535993249, G5373300361, G5562597660, G560201083, G580208524, G6583045785, G6716917589, G731149954, G7436411625, G8335174863, G8692209788, G9000020520, G9000036806, G9000037139, G9000046174, G9000059764, G9000065602, G9000076474, G9000192446, G9000290745, G9000315460, University of Pittsburgh Clinical and Translational Science Institute, Clinical Research Education and Career Development (CRECD) R25 Program at Morehouse School of Medicine, G947657586
OpenAlex ID: W2934637430