Open AccessSynergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative
Author(s) -
Emily Pfaff,
Andrew T. Girvin,
Davera Gabriel,
Kristin Kostka,
Michele Morris,
Matvey B. Palchuk,
Harold P. Lehmann,
Benjamin Amor,
Mark M. Bissell,
Katie R. Bradwell,
Sigfried Gold,
Stephanie Hong,
Johanna Loomba,
Amin Manna,
Julie A. McMurry,
Emily Niehaus,
Nabeel Qureshi,
Anita Walden,
Xiaohan Tanner Zhang,
Richard L. Zhu,
Richard A. Moffitt,
Melissa Haendel,
Christopher G Chute,
William Adams,
Shaymaa Al-Shukri,
Alfred Anzalone,
Ahmad Baghal,
Tellen D. Bennett,
Elmer V. Bernstam,
Elmer V. Bernstam,
Mark M. Bissell,
Brian Bush,
Thomas R. Campion,
Víctor M. 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 A. Dorr,
Daniel Eckrich,
Sarah E Eltinge,
Daniel Fort,
George Golovko,
Sagar Gupta,
Melissa Haendel,
Janos Hajagos,
David A. Hanauer,
Brett Harnett,
Ronald Horswell,
Nancý Huang,
Steven G. Johnson,
Michael G. Kahn,
Kamil Khanipov,
Curtis Kieler,
Katherine Ruiz De Luzuriaga,
Sarah E. Maidlow,
A. Pérez Martínez,
Jomol Mathew,
James C. McClay,
Gabriel McMahan,
Brian Melancon,
Stéphane M. Meystre,
Lucio Miele,
Hiroki Morizono,
Ray Pablo,
Lav P. Patel,
Jimmy Phuong,
Daniel J Popham,
Claudia Pulgarin,
Carlos Antônio do Nascimento 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 K. Wiley,
David A. Williams,
Adrian H. Zai
Publication year - 2021
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocab217
Subject(s) - benchmarking , computer science , analytics , data science , context (archaeology) , heuristics , pipeline (software) , informatics , data quality , data mining , business , engineering , operations management , metric (unit) , marketing , paleontology , electrical engineering , biology , programming language , operating system
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.