Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.
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Pfaff Emily R, Girvin Andrew T, Gabriel Davera L, Kostka Kristin, Morris Michele, Palchuk Matvey B, Lehmann Harold P, Amor Benjamin, Bissell Mark, Bradwell Katie R, Gold Sigfried, Hong Stephanie S, Loomba Johanna, Manna Amin, McMurry Julie A, Niehaus Emily, Qureshi Nabeel, Walden Anita, Zhang Xiaohan Tanner, Zhu Richard L, Moffitt Richard A, Haendel Melissa A, Chute Christopher G, N3C Consortium, Adams William G, Al-Shukri Shaymaa, Anzalone Alfred, Baghal Ahmad, Bennett Tellen D, Bernstam Elmer V, Bernstam Elmer V, Bissell Mark M, Bush Brian, Campion Thomas R, Castro Victor, Chang Jack, Chaudhari Deepa D, Chen Wenjin, Chu San, Cimino James J, Crandall Keith A, Crooks Mark, Davies Sara J Deakyne, DiPalazzo John, Dorr David, Eckrich Dan, Eltinge Sarah E, Fort Daniel G, Golovko George, Gupta Snehil, Haendel Melissa A, Hajagos Janos G, Hanauer David A, Harnett Brett M, Horswell Ronald, Huang Nancy, Johnson Steven G, Kahn Michael, Khanipov Kamil, Kieler Curtis, Luzuriaga Katherine Ruiz De, Maidlow Sarah, Martinez Ashley, Mathew Jomol, McClay James C, McMahan Gabriel, Melancon Brian, Meystre Stephane, Miele Lucio, Morizono Hiroki, Pablo Ray, Patel Lav, Phuong Jimmy, Popham Daniel J, Pulgarin Claudia, Santos Carlos, Sarkar Indra Neil, Sazo Nancy, Setoguchi Soko, Soby Selvin, Surampalli Sirisha, Suver Christine, Vangala Uma Maheswara Reddy, Visweswaran Shyam, Oehsen James von, Walters Kellie M, Wiley Laura, Williams David A, Zai Adrian
Аннотация
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.