{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T02:33:43Z","timestamp":1776911623353,"version":"3.51.2"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"name":"N3C Attribution & Publication Policy","award":["1.2-2020-08-25b"],"award-info":[{"award-number":["1.2-2020-08-25b"]}]},{"DOI":"10.13039\/100006108","name":"NCATS","doi-asserted-by":"publisher","award":["75N95023D00001"],"award-info":[{"award-number":["75N95023D00001"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,2,1]]},"abstract":"Abstract<\/jats:title>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Materials and Methods<\/jats:title>\n For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n As of June 2024, 29 sites have added at least one data enhancement to their N3C pipeline.<\/jats:p>\n <\/jats:sec>\n \n Discussion<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/jamia\/ocae299","type":"journal-article","created":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T13:16:08Z","timestamp":1732367768000},"page":"391-397","source":"Crossref","is-referenced-by-count":5,"title":["National COVID Cohort Collaborative data enhancements: a path for expanding common data models"],"prefix":"10.1093","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7473-1709","authenticated-orcid":false,"given":"Kellie M","family":"Walters","sequence":"first","affiliation":[{"name":"NC TraCS Institute, University of North Carolina at Chapel Hill , Chapel Hill, NC 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