Privacy‐preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience

Author:

Tachinardi Umberto1,Grannis Shaun J.2,Michael Sam G.3,Misquitta Leonie3,Dahlin Jayme3,Sheikh Usman3,Kho Abel45,Phua Jasmin5,Rogovin Sara S.5,Amor Benjamin6,Choudhury Maya6,Sparks Philip6,Mannaa Amin6,Ljazouli Saad6,Saltz Joel7,Prior Fred8,Baghal Ahmen8,Gersing Kenneth3,Embi Peter J.9

Affiliation:

1. Department of Biomedical Informatics University of Cincinnati College of Medicine Cincinnati Ohio USA

2. Center for Biomedical Informatics, Regenstrief Institute Department of Family Medicine, IU School of Medicine Regenstrief Institute, Inc. and Indiana University School of Medicine Indianapolis Indiana USA

3. National Center for Advancing Translational Science NIH Bethesda Maryland USA

4. Department of Medicine Northwestern University, Feinberg School of Medicine Chicago Illinois USA

5. Public Sector Datavant, Inc San Francisco California USA

6. Federal Health Palantir Technologies Denver Colorado USA

7. School of Medicine Stony Brook University Stony Brook New York USA

8. COM Biomedical Informatics University of Arkansas for Medical Sciences Little Rock Arkansas USA

9. Department of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USA

Abstract

AbstractIntroductionResearch driven by real‐world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID‐19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy‐preserving record linkage (PPRL) methods to ensure that patient‐level EHR data remains secure and private when governance‐approved linkages with other datasets occur.MethodsSeparate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using “deidentified tokens,” which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear‐text identifiers.ResultsThese methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross‐linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates.ConclusionThis large‐scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions.

Funder

National Center for Advancing Translational Sciences

Publisher

Wiley

Subject

Health Information Management,Public Health, Environmental and Occupational Health,Health Informatics

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