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National COVID Cohort Collaborative data enhancements: a path for expanding common data models.
Kellie M Walters1, Marshall Clark1, Sofia Dard1
1NC TraCS Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Journal of the American Medical Informatics Association : JAMIA
|November 23, 2024
Summary
Data enhancements for long COVID research in the National COVID Cohort Collaborative (N3C) improve data utility. Standardized project-driven data modeling supports long COVID studies by aligning with common data models.
Area of Science:
- Health Informatics
- Clinical Research Data Management
Background:
- The National COVID Cohort Collaborative (N3C) requires robust data for long COVID research.
- Existing common data models may not fully capture specific research needs for long COVID.
Purpose of the Study:
- To develop and implement data enhancements for the N3C to support long COVID research.
- To provide standardized guidance for data enrichment.
Main Methods:
- Created data designs for specific long COVID research needs.
- Defined scope and provided guidance for data preparation and population.
- Ensured alignment with common data model specifications and terminology standards.
Main Results:
- 29 contributing sites integrated at least one data enhancement into their N3C data pipeline as of June 2024.
- Developed project-specific data modeling guidance and documentation.
Conclusions:
- Project-driven data enhancement is crucial for meeting specific research needs beyond common data models.
- Standardized data enhancement improves data fit for purpose in large research collaborations like N3C.
- This approach facilitates rapid development of tailored data resources for critical research areas such as long COVID.
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