Walters Kellie M, Clark Marshall, Dard Sofia, Hong Stephanie S, Kelly Elizabeth, Kostka Kristin, Lee Adam M, Miller Robert T, Morris Michele, Palchuk Matvey B, Pfaff Emily R
NC TraCS Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Biomedical Informatics and Data Science Section, Department of General Internal Medicine, Johns Hopkins University, Baltimore, MD 21287, United States.
J Am Med Inform Assoc. 2025 Feb 1;32(2):391-397. doi: 10.1093/jamia/ocae299.
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.
For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way.
As of June 2024, 29 sites have added at least one data enhancement to their N3C pipeline.
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.
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.
为支持国家新冠队列协作项目(N3C)中的长新冠研究,N3C表型与数据采集团队创建了数据设计,以帮助参与站点完善其数据。完善内容包括长新冠专科诊所指标;入院、出院及转院记录;患者层面的健康社会决定因素;以及院内氧气补充使用情况。
对于每一项完善内容,我们定义了范围,并撰写了关于如何以标准化方式准备和填充数据的指南。
截至2024年6月,29个站点已在其N3C流程中至少添加了一项数据完善内容。
通用数据模型的使用对N3C的成功至关重要;然而,这些数据模型无法满足所有需求。需要进行项目驱动的数据完善。这应以符合通用数据模型规范的标准化方式进行。我们的方法为完善数据以更好地满足目的提供了一条有用的途径。
在本项目中,我们迅速制定了针对特定项目的数据建模指南和文档,以支持长新冠研究,同时坚持术语标准和数据协调。