ArticleLearning health systems2024
Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience.
Article in Learning health systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Available guidance for ethical challenges in learning health systems: an integrative literature review.Health research policy and systems · 2026Review
- Novel use of a hash-based tokenization for sharing sickle cell disease data without sharing protected health information.Blood advances · 2026Article
- Accuracy of an XGBoost-based privacy preserving record linkage system compared with an electronic health record patient matching module in identifying patients shared between nearby academic health centers.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Re-identification risk for common privacy preserving patient matching strategies when shared with de-identified demographics.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Higher mortality following SARS-CoV-2 infection in rural versus urban dwellers persists for two years post-infection.Nature communications · 2025Article
- Combining Real-World and Clinical Trial Data Through Privacy-Preserving Record Linkage: Opportunities and Challenges-A Narrative Review.Health science reports · 2025Article
- Accuracy of privacy preserving record linkage for real world data in the United States: a systemic review.JAMIA open · 2025Review
- Building a collaborative ecosystem across the IDeA-CTR networks in response to a public health emergency.Journal of clinical and translational science · 2025Article
- LinkingLearning health systems · 2025Article
- Long-Term Mortality Following SARS-CoV-2 Infection in Rural Versus Urban Dwellers With Autoimmune or Inflammatory Rheumatic Disease: A Retrospective Cohort Analysis From the National COVID Cohort Collaborative.Arthritis care & research · 2025Article
- Generating Real-World Evidence From the Excellence Network in Rheumatology.Pharmacoepidemiology and drug safety · 2024Article
- Linking clinical trial participants to their U.S. real-world data through tokenization: A practical guide.Contemporary clinical trials communications · 2024Article
- Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience.Learning health systems · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Introduction: Research 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. Methods: Separate 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. Results: These 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. Conclusion: This 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.