National COVID Cohort Collaborative data enhancements: a path for expanding common data models.

IF 4.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of the American Medical Informatics Association Pub Date : 2025-02-01 DOI:10.1093/jamia/ocae299
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
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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.

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国家 COVID 群体协作数据增强:扩展通用数据模型的途径。
导言:为支持国家 COVID 队列协作组织 (N3C) 的长期 COVID 研究,N3C 表型和数据采集团队创建了数据设计,以帮助贡献站点增强其数据。增强功能包括:长COVID专科门诊指标;入院、出院和转院(ADT)交易;患者层面的健康社会决定因素;以及院内使用氧气补充剂:对于每项改进,我们都确定了范围,并编写了如何以标准化方式准备和填充数据的指南:结果:截至 2024 年 6 月,29 个站点在其 N3C 管道中增加了至少一项数据增强功能:讨论:使用通用数据模型对 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改进。这应该以符合清洁发展机制规范的标准化方式进行。我们的方法为增强数据提供了一个有用的途径,以提高数据的适用性。
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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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