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
{"title":"National COVID Cohort Collaborative Data Enhancements: A Path for Expanding Common Data Models.","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","doi":"10.1093/jamia/ocae299","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way.</p><p><strong>Results: </strong>As of June 2024, 29 sites have added at least one data enhancement to their N3C pipeline.</p><p><strong>Discussion: </strong>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.</p>","PeriodicalId":50016,"journal":{"name":"Journal of the American Medical Informatics Association","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Medical Informatics Association","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1093/jamia/ocae299","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.
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.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
