National COVID Cohort Collaborative Data Enhancements: A Path for Expanding Common Data Models

Kellie M Walters; Marshall Clark; Sofia Dard; Stephanie S Hong; Elizabeth Kelly; Kristin Kostka; Adam M Lee; Robert T Miller; Michele Morris; Matvey B Palchuk; Emily R Pfaff

doi:10.1093/jamia/ocae299

National COVID Cohort Collaborative Data Enhancements: A Path for Expanding Common Data Models

J Am Med Inform Assoc. 2024 Nov 23:ocae299. doi: 10.1093/jamia/ocae299. Online ahead of print.

Authors

Affiliations

¹ NC TraCS Institute 160 N Medical Drive Chapel Hill, NC 27599.
² NC TraCS Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
³ Biomedical Informatics and Data Science Section, Department of General Internal Medicine, Johns Hopkins University, Baltimore, MD, United States.
⁴ Nuffield Department of Rheumatology, Orthopaedics and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, United Kingdom.
⁵ Tufts Clinical and Translational Science Institute, Boston, MA, United States.
⁶ Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
⁷ TriNetX LLC, Cambridge, MA, United States.
⁸ Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

PMID: 39579089
DOI: 10.1093/jamia/ocae299

Abstract

Introduction: To support long COVID research in National COVID Cohort Collaborative (N3C), the N3C Phenotype and Data Acquisition team created data designs to aid contributing sites in enhancing their data. Enhancements include: long COVID specialty clinic indicator; Admission, Discharge, and Transfer (ADT) transactions; patient-level social determinants of health; and in-hospital use of oxygen supplementation.

Methods: For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way.

Results: As of June 2024, 29 sites have added at least one data enhancement to their N3C pipeline.

Discussion: The use of common data models is critical to the success of N3C; however, these data models cannot account for all needs. Project-driven data enhancement is required. This should be done in a standardized way in alignment with CDM specifications. Our approach offers a useful pathway for enhancing data to improve fit for purpose.

Keywords: COVID-19; Clinical Informatics; Common data models; Data modeling; EHR Data.