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While manual chart review can identify delirium, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) can analyze unstructured text in electronic health records (EHRs) to extract meaningful clinical information.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To support national integration of NLP for EHR-based delirium identification across different institutions, we launched the Delirium Interest Group within the national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) NLP Working Group. This paper outlines our initial efforts to standardize, evaluate, and translate an NLP-based delirium detection model into the i2b2\/ENACT platform.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multisite contextual inquiry identified several key challenges, including variations in local screening practices (eg, tools used, documentation frequency, and quality control), the need for harmonized definitions in the context of EHRs, and the complexity of modeling temporal logic. Multisite NLP evaluation revealed variable performance degradation driven by differences in delirium screening practices, clinical documentation patterns and semantics, and note syntactic structures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our work represents an important first step toward enabling scalable and standardized NLP-based delirium detection across institutions. By engaging diverse institutions through the ENACT NLP Working Group, we identified shared challenges and site-specific variations that impact model implementation and performance. Our collaborative approach enabled the development of a more robust framework for delirium identification across heterogeneous EHR systems. Future efforts will build on this foundation to enhance the validity, usability, and translational impact of delirium detection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/gerona\/glaf207","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T12:08:17Z","timestamp":1758888497000},"source":"Crossref","is-referenced-by-count":3,"title":["Advancing delirium detection through the Open Health Natural Language Processing Consortium and the Evolve to Next-Gen Accrual to Clinical Trials Network"],"prefix":"10.1093","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1691-5179","authenticated-orcid":false,"given":"Sunyang","family":"Fu","sequence":"first","affiliation":[{"name":"McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston , Houston, Texas,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min 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