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

2020; Oxford University Press; Volume: 28; Issue: 3 Linguagem: Inglês

10.1093/jamia/ocaa196

1527-974X

Melissa Haendel, Christopher G. Chute, Tellen D. Bennett, David Eichmann, Justin Guinney, Warren A. Kibbe, Philip Payne, Emily Pfaff, Peter N. Robinson, Joel Saltz, Heidi Spratt, Christine Suver, John Wilbanks, Adam B. Wilcox, Andrew E. Williams, Chunlei Wu, Clair Blacketer, Robert L. Bradford, James J. Cimino, Marshall Clark, Evan W Colmenares, Patricia A. Francis, Davera Gabriel, Alexis Graves, Raju Hemadri, Stephanie Hong, George Hripscak, Dazhi Jiao, Jeffrey G. Klann, Kristin Kostka, Adam M Lee, Harold P. Lehmann, Lora Lingrey, Robert Miller, Michele Morris, Shawn N. Murphy, Karthik Natarajan, Matvey B. Palchuk, Usman Ullah Sheikh, Harold R. 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 Michael, Lili Portilla, Joni L. Rutter, Christopher P. Austin, Kenneth Gersing,

Privacy-Preserving Technologies in Data

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.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.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.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.