Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.
Pfaff ER., Girvin AT., Gabriel DL., Kostka K., Morris M., Palchuk MB., Lehmann HP., Amor B., Bissell M., Bradwell KR., Gold S., Hong SS., Loomba J., Manna A., McMurry JA., Niehaus E., Qureshi N., Walden A., Zhang XT., Zhu RL., Moffitt RA., Haendel MA., Chute CG., N3C Consortium None., Adams WG., Al-Shukri S., Anzalone A., Baghal A., Bennett TD., Bernstam EV., Bernstam EV., Bissell MM., Bush B., Campion TR., Castro V., Chang J., Chaudhari DD., Chen W., Chu S., Cimino JJ., Crandall KA., Crooks M., Davies SJD., DiPalazzo J., Dorr D., Eckrich D., Eltinge SE., Fort DG., Golovko G., Gupta S., Haendel MA., Hajagos JG., Hanauer DA., Harnett BM., Horswell R., Huang N., Johnson SG., Kahn M., Khanipov K., Kieler C., Luzuriaga KRD., Maidlow S., Martinez A., Mathew J., McClay JC., McMahan G., Melancon B., Meystre S., Miele L., Morizono H., Pablo R., Patel L., Phuong J., Popham DJ., Pulgarin C., Santos C., Sarkar IN., Sazo N., Setoguchi S., Soby S., Surampalli S., Suver C., Vangala UMR., Visweswaran S., Oehsen JV., Walters KM., Wiley L., Williams DA., Zai A.
ObjectiveIn 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 methodsWe 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.ResultsBeyond 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.DiscussionWe 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.ConclusionBy 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.