National COVID Cohort Collaborative data enhancements: A path for expanding common data models
Walters, KM; Clark, M; Dard, S; et al.; N3C and RECOVER Consortia, Journal of the American Medical Informatics Association
Published
February 2025
Journal
Journal of the American Medical Informatics Association
Abstract
Objective: 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 transactions; patient-level social determinants of health; and in-hospital use of oxygen supplementation. Materials and 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 common data model specifications. Our approach offers a useful pathway for enhancing data to improve fit for purpose. Conclusion: In this initiative, we rapidly produced project-specific data modeling guidance and documentation in support of long COVID research while maintaining a commitment to terminology standards and harmonized data.
Authors
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; N3C and RECOVER Consortia
Keywords
COVID-19; clinical informatics; common data models; data modeling; electronic health record data
Short Summary
In this RECOVER study, researchers aimed to improve a large national database on COVID-19 patients from many hospitals to better answer research questions about Long COVID. They noticed that the current database was missing the details needed to study Long COVID properly. To fix this, researchers created clear, step-by-step instructions to help hospitals add specific details from patients’ electronic health records (EHRs) to data they submitted to RECOVER. These extra details included notes on patient visits to Long COVID clinics, hospital stays, living situations, jobs, and if they needed extra oxygen while in the hospital. By June 2024, 29 hospitals had added this more complete information to the data they submitted to RECOVER. Having the hospitals submit data this way made it easier for researchers to compare patient information from different places. This study shows that using clear and standard ways to collect data are important for big research studies. It also shows that researchers sometimes need more details to study difficult research questions, like those about the long-term effects of COVID.