ArticleJournal of the American Medical Informatics Association : JAMIA2021
The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 399 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
399 citing papers in PubMed, 1 synthesis or guideline pooled it, 576 citations in OpenAlex.
- Learning competing risks across multiple hospitals: one-shot distributed algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Pooled it
- Enhancing the Observational Medical Outcomes Partnership Common Data Model to Support Health Equity Research: Expanding Race and Ethnicity Data Representation.Medical care · 2026Article
- Electronic Phenotype for Detection, Staging, and Subtyping of Acute Kidney Injury.American journal of kidney diseases : the official journal of the National Kidney Foundation · 2026Article
- A comparison of Fast Healthcare Interoperability Resources and Observational Medical Mutcomes Partnership electronic health record data within the All of Us Research Program.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Increasing Multimorbidity Burden After COVID-19 Infection Among People With and Without HIV in the United States: Insights From the National Clinical Cohort Collaborative (N3C).Open forum infectious diseases · 2026Article
- PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Characterization and validation of EHR computable phenotypes for Long COVID using patient-reported symptoms: insights from the nationwide RECOVER program.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A Systematic Evaluation of Cohort Selection Criteria and Their Impact on Machine Learning Model Performance and Demographic Disparities in COVID-19 Outcomes: Cohort Study.JMIR formative research · 2026Article
- Article
- Harmonization of Real-World Data for Vaccine-Preventable Infectious Diseases: Integration of SARS-CoV-2 Diagnostic and Serologic Data From Multiple Sources.Open forum infectious diseases · 2026Article
- Epidemiological characteristics of paediatric COVID-19 and influenza co-infections in the United States, 2020-2024.Epidemiology and infection · 2026Article
- Comorbidity Exposure-Window Definitions and Multidimensional Disparities in Long COVID Risk: Evidence from a U.S. National Cohort (2020-2024).medRxiv : the preprint server for health sciences · 2026Article
- Eras of bioinformatics technologies from command-line interfaces to artificial intelligence (AI) chatbots.Briefings in bioinformatics · 2026Review
- Improving Assessment of Vaccine Effectiveness by Coupling Test-negative Design Studies with Survival Models.Epidemiology (Cambridge, Mass.) · 2026Article
- Differential COVID-19 Outcomes Across Lysosomal Disorders.medRxiv : the preprint server for health sciences · 2026Article
- Extending the Observational Medical Outcomes Partnership Common Data Model to Support Observational Peripheral Vascular Disease Research.The Journal of surgical research · 2026Observational
- ReFIT: Federated Transfer Learning for Sequential Prediction and Uncertainty Quantification Using Streaming EHR Data.Statistics in biosciences · 2026Article
- Multi-scale data improves performance of machine learning model for long COVID identification.Communications medicine · 2026Article
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- VO: The Vaccine Ontology.Scientific data · 2026Article
339 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
57 authors at 20 institutions in 1 country.
Funding
Abstract
objectiveCoronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. MATERIALS AND
methodsThe Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.
resultsOrganized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.
conclusionsThe N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
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What OpenQuestion holds
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.