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

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.

Keywords: COVID-19; EHR data; SARS-CoV-2; clinical data model harmonization; collaborative analytics; open science.

Figure 1.

Establishing National COVID Cohort Collaborative (N3C) sociotechnical processes and infrastructure via community workstreams. Each workstream includes representatives from National Center for Advancing Translational Sciences (NCATS), the Clinical and Translational Science Awards hubs, the Center for Data to Health, sites contributing data, and other members of the research community. (1) Data Partnership and Governance: This workstream designs governance and makes regulatory recommendations to National Institutes of Health (NIH) for their execution. Organizations sign a Data Transfer Agreement (DTA) with NCATS and may use the central institutional review board. (2) Phenotype and Data Acquisition: The community defines inclusion criteria for the N3C COVID-19 (coronavirus disease 2019) cohort and supports organizations in customized data export. (3) Data Ingest and Harmonization: Data reside within different organizations in different common data models. This workstream quality-assures and harmonizes data from different sources and common data models into a unified dataset. (4) Collaborative Analytics workstream: Data are made accessible for collaborative use by the N3C community. A secure data enclave (N3C Enclave), from which data cannot be removed, houses analytical tools and supports reproducible and transparent workflows. Formulation of clinical research questions and development of prototype machine learning and statistical workflows is collaboratively coordinated; portals and dashboards support resource, data, expertise, and results navigation and reuse. (5) Synthetic Clinical Data: A pilot to determine the degree to which synthetic derivatives of the Limited Data Set are able to approximate analyses derived from original data, while enhancing shareable data outside the N3C Enclave. ACT: Accrual to Clinical Trials; OMOP: Observational Medical Outcomes Partnership; PCORnet: National Patient-Centered Clinical Research Network.

Figure 2.

Panel A. Regulatory steps and user access. Organizations can operate as data contributors or data users or both; contribution is not required for use. For contributing organizations, the first step is a Data Transfer Agreement (DTA) which is executed between National Center for Advancing Translational Sciences (NCATS) and the contributing organization (and its affiliates where applicable). For organizations using data, a separate, umbrella/institute-wide Data Use Agreement (DUA) is executed between organizations and NCATS. Interested investigators submit a Data Use Request (DUR) for each project proposal, which is reviewed by a Data Access Committee (DAC). The DUR includes a brief description of how the data will be used, a signed User Code of Conduct (UCoC) that articulates fundamental actions and prohibitions on data user activities, and if requesting access to patient-level data a proof of additional institutional review board (IRB) approval. The DAC reviews the DUR and upon approval, grants access to the appropriate data level within the National COVID Cohort Collaborative (N3C) Enclave. Synthetic data currently follow the same procedure, but if the pilot is successful, we aim to make access available by simple registration if provisioned by the organizations. The lock symbol references steps where multiple conditions must be met. HIPAA: Health Insurance Portability and Accountability Act; LDS: Limited Data Set; NIH: National Institutes of Health. Panel B. Features and requirements for each level of data in the N3C Enclave: Synthetic,^, De-identified data ^,,, and Limited Data Set, .

Figure 3.

National COVID Cohort Collaborative (N3C) Data Quality Checks. At the sites, the extraction script performs a check for duplicate primary keys; if duplicate keys are found, the extraction fails until the site resolves the error. When extraction is successfully completed, a data “manifest” is created that contains metadata about the site and the payload. Site personnel then sFTP the data to N3C to be queued for ingestion. The first step in the ingestion process checks that the payload is consistent with the formatting requirements and the manifest file. Next, the payload is loaded into a database modeled after the payload’s native common data model (CDM), which ensures source data model conformance. Next, a series of data quality checks including a subset of coronavirus disease 2019 (COVID-19)–specific code validations are performed, and if needed, minimal corrections are made. Any corrections are recorded and added to the payload documentation. Next, the payload is transformed to Observational Medical Outcomes Partnership (OMOP) 5.3.1 using the validated maps from the payload’s native CDM. Once in OMOP 5.3.1, a subset of the Observational Health Data Sciences and Informatics (OHDSI) Data Quality Dashboard tests are run, and the results of these are added to the payload documentation. The payload is then exported to a merged database containing all the previously harmonized site data payloads, where it is then checked for conformance again before export to the analytics pipeline. DC: Data Characterization; DQD: Data Quality Dashboard.

Figure 4.

National COVID Cohort Collaborative (N3C) Enclave. The analytical environment for N3C is a secure, virtualized, cloud-based platform. Within the N3C Enclave, researchers have access to raw data, as well as transformed and harmonized datasets derived by other researchers. Analytical tools hosted within the environment support complex ETL (extract-transform-load), generation of coronavirus disease 2019 (COVID-19)–specific data elements, statistical analysis, machine learning, and rich visualizations. Third-party tools contributed by the community can be integrated into the environment; current tools include Observational Health Data Sciences and Informatics (OHDSI) tools and the Leaf patient cohort builder. N3C is developing methods for integration of genomic, imaging, and other data modalities.

Figure 5.

The Contributor Attribution Model. In the National COVID Cohort Collaborative Enclave, the Contributor Attribution Model is used to aggregate all contributions to any given workflow or report generated with a specific declaration of what exactly each person contributed, supporting the notion of transitive credit. ORCID identifiers are used to identify users. An example contributor to an artifact used in the National COVID Cohort Collaborative is shown on the lower panel.