Synergies between centralized and federated approaches to data quality: A report from the national COVID cohort collaborative

Emily R. Pfaff, Andrew T. Girvin, Davera L. Gabriel, Kristin Kostka, Michele Morris, Matvey B. Palchuk, Harold P. Lehmann, Benjamin Amor, Mark Bissell, Katie R. Bradwell, Sigfried Gold, Stephanie S. Hong, Johanna Loomba, Amin Manna, Julie A. Mcmurry, Emily Niehaus, Nabeel Qureshi, Anita Walden, Xiaohan Tanner Zhang, Richard L. ZhuRichard A. Moffitt, Melissa A. Haendel, Christopher G. Chute, William G. Adams, Shaymaa Al-Shukri, Alfred Anzalone, Ahmad Baghal, Tellen D. Bennett, Elmer V. Bernstam, Mark M. Bissell, Brian Bush, Thomas R. Campion, Victor Castro, Jack Chang, Deepa D. Chaudhari, Wenjin Chen, San Chu, James J. Cimino, Keith A. Crandall, Mark Crooks, Sara J.Deakyne Davies, John Dipalazzo, David Dorr, Dan Eckrich, Sarah E. Eltinge, Daniel G. Fort, George Golovko, Snehil Gupta, Melissa A. Haendel, Janos G. Hajagos, David A. Hanauer, Brett M. Harnett, Ronald Horswell, Nancy Huang, Steven G. Johnson, Michael Kahn, Kamil Khanipov, Curtis Kieler, Katherine Ruiz De Luzuriaga, Sarah Maidlow, Ashley Martinez, Jomol Mathew, James C. Mcclay, Gabriel Mcmahan, Brian Melancon, Stephane Meystre, Lucio Miele, Hiroki Morizono, Ray Pablo, Lav Patel, Jimmy Phuong, Daniel J. Popham, Claudia Pulgarin, Carlos Santos, Indra Neil Sarkar, Nancy Sazo, Soko Setoguchi, Selvin Soby, Sirisha Surampalli, Christine Suver, Uma Maheswara Reddy Vangala, Shyam Visweswaran, James von Oehsen, Kellie M. Walters, Laura Wiley, David A. Williams, Adrian Zai

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Objective: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Materials and Methods: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. Conclusion: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

Original languageEnglish (US)
Pages (from-to)609-618
Number of pages10
JournalJournal of the American Medical Informatics Association
Volume29
Issue number4
DOIs
StatePublished - Apr 1 2022

Keywords

  • COVID-19
  • data accuracy
  • electronic health records

ASJC Scopus subject areas

  • Health Informatics

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