Evidence map›Paper›PMID 38249841›Full record

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.

Umberto Tachinardi, Shaun J Grannis, Sam G Michael, Leonie Misquitta, Jayme Dahlin, Usman Sheikh, Abel Kho, Jasmin Phua, Sara S Rogovin, Benjamin Amor and 9 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. LinkingLearning health systems · 2025
    Article
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  11. Article
  12. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Umberto TachinardiDepartment of Biomedical Informatics University of Cincinnati College of Medicine Cincinnati Ohio USA.
Shaun J GrannisCenter 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.
Sam G MichaelNational Center for Advancing Translational Science NIH Bethesda Maryland USA.
Leonie MisquittaNational Center for Advancing Translational Science NIH Bethesda Maryland USA.
Jayme DahlinNational Center for Advancing Translational Science NIH Bethesda Maryland USA.
Usman SheikhNational Center for Advancing Translational Science NIH Bethesda Maryland USA.
Abel KhoDepartment of Medicine Northwestern University, Feinberg School of Medicine Chicago Illinois USA.
Jasmin PhuaPublic Sector Datavant, Inc San Francisco California USA.
Sara S RogovinPublic Sector Datavant, Inc San Francisco California USA.
Benjamin AmorFederal Health Palantir Technologies Denver Colorado USA.
Maya ChoudhuryFederal Health Palantir Technologies Denver Colorado USA.
Philip SparksFederal Health Palantir Technologies Denver Colorado USA.
Amin MannaaFederal Health Palantir Technologies Denver Colorado USA.
Saad LjazouliFederal Health Palantir Technologies Denver Colorado USA.
Joel SaltzSchool of Medicine Stony Brook University Stony Brook New York USA.
Fred PriorCOM Biomedical Informatics University of Arkansas for Medical Sciences Little Rock Arkansas USA.
Ahmen BaghalCOM Biomedical Informatics University of Arkansas for Medical Sciences Little Rock Arkansas USA.
Kenneth GersingNational Center for Advancing Translational Science NIH Bethesda Maryland USA.
Peter J EmbiDepartment of Biomedical Informatics Vanderbilt University Medical Center Nashville Tennessee USA.

Funding

Research Services Section (RSS)ZICTR000242 · NCATS · NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES · PI WILSON, KELLI · 2015 to 2025
$64.9M
CLC NIH HHS 75N90021D00028NIAID NIH HHS 75N93021D00028NIDA NIH HHS 75N95024D00028
6 · The paper itself

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.

Indexed as

clinical researchdata privacylearning health systemsrecord linkagetranslational research

Identifiers

PMID38249841
PMCPMC10797567

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.