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

Area of Science:

• Health Informatics
• Data Science
• Epidemiology

Background:

• COVID-19 necessitates rapid data sharing for effective research and public health response.
• Existing organizational clinical data are often inaccessible to external researchers, limiting large-scale analyses.
• Challenges in data sharing include technical, regulatory, policy, and governance barriers.

Purpose of the Study:

• To introduce the National COVID Cohort Collaborative (N3C) as an open science community for analyzing multi-center patient-level data.
• To overcome barriers to sharing and harmonizing individual-level clinical data for COVID-19 research.
• To enable efficient, transparent, and reproducible collaborative analytics on a large scale.

Main Methods:

• Established legal agreements and governance structures for participating organizations and researchers.
• Developed data extraction scripts for identifying and ingesting COVID-19 cases (positive, negative, and possible).
• Created a data quality assurance and harmonization pipeline to generate a unified dataset within a secure data enclave.

Main Results:

• Successfully built a scalable infrastructure for incorporating multi-organizational clinical data for COVID-19 analytics.
• Established inclusive workstreams, data extraction, harmonization pipelines, and a secure data enclave.
• Initiated a synthetic data pilot to enhance data accessibility and democratize research.

Conclusions:

• The N3C demonstrates the feasibility of a multisite collaborative network for analyzing COVID-19 clinical data.
• This initiative facilitates rapid collaboration among clinicians, researchers, and data scientists.
• The N3C is expected to accelerate the identification of treatments and specialized care, ultimately saving lives and reducing the impact of COVID-19.