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Manual chart review can capture functional status information but is labor-intensive and time-consuming. In this study, we developed and validated a scalable natural language processing (NLP) model to extract functional status information from unstructured electronic health record (EHR) notes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cohort study included 110 older adults seen at a geriatric osteoporosis clinic. A large language model\u2013augmented symbolic NLP pipeline (MedADL) was developed to extract activities of daily living (ADL) and instrumental activities of daily living (IADL) impairments from 13\u2003182 clinical notes and derive the Cumulative Functional Burden Index (CFBI), validated against grip strength, walking speed, balance score, and frailty status using ordinal logistic regression with 10-fold cross-validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The NLP model demonstrated moderate to strong performance across 16 ADL and IADL categories, with an average precision of 0.868, sensitivity of 0.828, specificity of 0.937, and F1 score of 0.816. CFBI was significantly associated with frailty status (odds ratio [OR]\u2009=\u20090.799, reciprocal OR\u2009=\u20091.25, p\u2009=\u2009.049, AUC (Area Under the Curve)\u2009=\u20090.66), indicating that each additional functional deficit was associated with approximately 25% higher odds of being classified in a less robust frailty category. CFBI was correlated with walking speed (r\u2009=\u20090.43, p\u2009&amp;lt;\u2009.001), balance (r\u2009=\u2009\u22120.42, p\u2009&amp;lt;\u2009.001), grip strength (r\u2009=\u2009\u22120.24, p\u2009=\u2009.019), and fall history (r\u2009=\u20090.21, p\u2009=\u2009.040).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>MedADL demonstrated moderate-to-strong performance across 16 bADL and iADL categories, and the derived CFBI was significantly associated with frailty status and correlated with 4 objective physical performance measures. Future studies will prospectively evaluate long-term outcomes and integrate the model into the EHR to enable real-time clinical decision support.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/gerona\/glag154","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T11:22:51Z","timestamp":1780917771000},"source":"Crossref","is-referenced-by-count":0,"title":["MedADL: high-throughput information extraction of functional status from electronic health records to advance frailty assessment in older adults"],"prefix":"10.1093","volume":"81","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":"Zhiyi","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Public Health, University of Texas Health Science Center at Houston , Houston, Texas,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":", Jennyly T","family":"Nguyen","sequence":"additional","affiliation":[{"name":"McGovern Medical School, University of Texas Health Science Center at Houston , Houston, Texas,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huipeng","family":"Liu","sequence":"additional","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8948-8871","authenticated-orcid":false,"given":"Jaerong","family":"Ahn","sequence":"additional","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":"Vanessa","family":"Ramirez","sequence":"additional","affiliation":[{"name":"McGovern Medical School, University of Texas Health Science Center at Houston , Houston, Texas,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jude","family":"Des Bordes","sequence":"additional","affiliation":[{"name":"McGovern Medical School, University of Texas Health Science Center at Houston , Houston, Texas,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2570-3741","authenticated-orcid":false,"given":"Hongfang","family":"Liu","sequence":"additional","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 Ji","family":"Kwak","sequence":"additional","affiliation":[{"name":"McGovern Medical School, University of Texas Health Science Center at Houston , Houston, Texas,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0075-5836","authenticated-orcid":false,"given":"Nahid J","family":"Rianon","sequence":"additional","affiliation":[{"name":"Kaiser Permanente Bernard J. 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