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The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.
Haendel, Melissa A; Chute, Christopher G; Bennett, Tellen D; Eichmann, David A; Guinney, Justin; Kibbe, Warren A; Payne, Philip R O; Pfaff, Emily R; Robinson, Peter N; Saltz, Joel H; Spratt, Heidi; Suver, Christine; Wilbanks, John; Wilcox, Adam B; Williams, Andrew E; Wu, Chunlei; Blacketer, Clair; Bradford, Robert L; Cimino, James J; Clark, Marshall; Colmenares, Evan W; Francis, Patricia A; Gabriel, Davera; Graves, Alexis; Hemadri, Raju; Hong, Stephanie S; Hripscak, George; Jiao, Dazhi; Klann, Jeffrey G; Kostka, Kristin; Lee, Adam M; Lehmann, Harold P; Lingrey, Lora; Miller, Robert T; Morris, Michele; Murphy, Shawn N; Natarajan, Karthik; Palchuk, Matvey B; Sheikh, Usman; Solbrig, Harold; Visweswaran, Shyam; Walden, Anita; Walters, Kellie M; Weber, Griffin M; Zhang, Xiaohan Tanner; Zhu, Richard L; Amor, Benjamin; Girvin, Andrew T; Manna, Amin; Qureshi, Nabeel.
  • Haendel MA; Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, USA.
  • Chute CG; Translational and Integrative Sciences Center, Department of Molecular Toxicology, Oregon State University, Corvallis, Oregon, USA.
  • Bennett TD; Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA.
  • Eichmann DA; Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, Colorado, USA.
  • Guinney J; School of Library and Information Science, The University of Iowa, Iowa City, Iowa, USA.
  • Kibbe WA; Sage Bionetworks, Seattle, Washington, USA.
  • Payne PRO; Duke University, Durham,North Carolina, USA.
  • Pfaff ER; Institute for Informatics, Washington University in St. Louis, Saint Louis,Missouri, USA.
  • Robinson PN; North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA.
  • Saltz JH; Jackson Laboratory, Bar Harbor, Maine, USA.
  • Spratt H; Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA.
  • Suver C; University of Texas Medical Branch, Galveston, Texas, USA.
  • Wilbanks J; Sage Bionetworks, Seattle, Washington, USA.
  • Wilcox AB; Sage Bionetworks, Seattle, Washington, USA.
  • Williams AE; University of Washington, Seattle, Washington, USA.
  • Wu C; Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston,Massachusetts, USA.
  • Blacketer C; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.
  • Bradford RL; Janssen Research and Development, LLC, Raritan, New Jersey, USA.
  • Cimino JJ; North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA.
  • Clark M; University of Alabama-Birmingham, Birmingham, Alabama, USA.
  • Colmenares EW; North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA.
  • Francis PA; Department of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA.
  • Gabriel D; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Graves A; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Hemadri R; University of Iowa Institute for Clinical and Translational Science, The University of Iowa, Iowa City, Iowa, USA.
  • Hong SS; National Center for Advancing Translational Science, Bethesda, Maryland, USA.
  • Hripscak G; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Jiao D; Department of Biomedical Informatics, Columbia University, New York, New York, USA.
  • Klann JG; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Kostka K; Harvard Medical School, Boston,Massachusetts, USA.
  • Lee AM; IQVIA, Durham, North Carolina, USA.
  • Lehmann HP; University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA.
  • Lingrey L; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Miller RT; TriNetX, Cambridge,Massachusetts, USA.
  • Morris M; Tufts Clinical and Translational Science Institute, Tufts University, Boston,Massachusetts, USA.
  • Murphy SN; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh,Pennsylvania, USA.
  • Natarajan K; Mass General Brigham, Boston,Massachusetts, USA.
  • Palchuk MB; Irving Medical Center, Columbia University, New York, New York, USA.
  • Sheikh U; TriNetX, Cambridge,Massachusetts, USA.
  • Solbrig H; National Center for Advancing Translational Science, Bethesda, Maryland, USA.
  • Visweswaran S; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Walden A; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh,Pennsylvania, USA.
  • Walters KM; Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, USA.
  • Weber GM; Sage Bionetworks, Seattle, Washington, USA.
  • Zhang XT; North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill,North Carolina, USA.
  • Zhu RL; Department of Biomedical Informatics, Harvard Medical School, Boston,Massachusetts, USA.
  • Amor B; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Girvin AT; Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Manna A; Palantir Technologies, Palo Alto, California, USA.
  • Qureshi N; Palantir Technologies, Palo Alto, California, USA.
J Am Med Inform Assoc ; 28(3): 427-443, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-719257
Semantic information from SemMedBD (by NLM)
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3. Household composition PROCESS_OF Clinician
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8. Household composition PROCESS_OF Clinician
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9. Therapeutic procedure AFFECTS COVID-19
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10. COVID-19 PROCESS_OF Patients
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ABSTRACT

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.
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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Intersectoral Collaboration / Information Dissemination / Data Science / COVID-19 Subject: Intersectoral Collaboration / Information Dissemination / Data Science / COVID-19 Type of study: Observational study Language: English Journal: J Am Med Inform Assoc Clinical aspect: Etiology Year: 2021

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Intersectoral Collaboration / Information Dissemination / Data Science / COVID-19 Subject: Intersectoral Collaboration / Information Dissemination / Data Science / COVID-19 Type of study: Observational study Language: English Journal: J Am Med Inform Assoc Clinical aspect: Etiology Year: 2021
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