The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment

Melissa A. Haendel, Christopher G. Chute, Tellen D. Bennett, David A. Eichmann, Justin Guinney, Warren A. Kibbe, Philip R.O. Payne, Emily R. Pfaff, Peter N. Robinson, Joel H. Saltz, Heidi Spratt, Christine Suver, John Wilbanks, Adam B. Wilcox, Andrew E. Williams, Chunlei Wu, Clair Blacketer, Robert L. Bradford, James J. Cimino, Marshall ClarkEvan W. Colmenares, Patricia A. Francis, Davera Gabriel, Alexis Graves, Raju Hemadri, Stephanie S. Hong, George Hripscak, Dazhi Jiao, Jeffrey G. Klann, Kristin Kostka, Adam M. Lee, Harold P. Lehmann, Lora Lingrey, Robert T. Miller, Michele Morris, Shawn N. Murphy, Karthik Natarajan, Matvey B. Palchuk, Usman Sheikh, Harold Solbrig, Shyam Visweswaran, Anita Walden, Kellie M. Walters, Griffin M. Weber, Xiaohan Tanner Zhang, Richard L. Zhu, Benjamin Amor, Andrew T. Girvin, Amin Manna, Nabeel Qureshi, Michael G. Kurilla, Sam G. Michael, Lili M. Portilla, Joni L. Rutter, Christopher P. Austin, Ken R. Gersing

Research output: Contribution to journalArticlepeer-review

186 Scopus citations


Objective: Coronavirus 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 Methods: The 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. Results: Organized 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. Conclusions: The 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.

Original languageEnglish
Pages (from-to)427-443
Number of pages17
JournalJournal of the American Medical Informatics Association
Issue number3
StatePublished - Mar 1 2021


  • COVID-19
  • EHR data
  • SARS-CoV-2
  • clinical data model harmonization
  • collaborative analytics
  • open science


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