{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T05:23:14Z","timestamp":1793164994457,"version":"4.5.0"},"reference-count":43,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2018,7,4]],"date-time":"2018-07-04T00:00:00Z","timestamp":1530662400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"Atrius Health"},{"name":"Center for Population Health IT, Johns Hopkins University"}],"content-domain":{"domain":["agsjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J American Geriatrics Society"],"published-print":{"date-parts":[[2018,8]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To examine the value of unstructured electronic health record (EHR) data (free\u2010text notes) in identifying a set of geriatric syndromes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design<\/jats:title>\n                    <jats:p>Retrospective analysis of unstructured EHR notes using a natural language processing (NLP) algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Setting<\/jats:title>\n                    <jats:p>Large multispecialty group.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Participants<\/jats:title>\n                    <jats:p>Older adults (N=18,341; average age 75.9, 58.9% female).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Measurements<\/jats:title>\n                    <jats:p>We compared the number of geriatric syndrome cases identified using structured claims and structured and unstructured EHR data. We also calculated these rates using a population\u2010level claims database as a reference and identified comparable epidemiological rates in peer\u2010reviewed literature as a benchmark.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using insurance claims data resulted in a geriatric syndrome prevalence ranging from 0.03% for lack of social support to 8.3% for walking difficulty. Using structured EHR data resulted in similar prevalence rates, ranging from 0.03% for malnutrition to 7.85% for walking difficulty. Incorporating unstructured EHR notes, enabled by applying the NLP algorithm, identified considerably higher rates of geriatric syndromes: absence of fecal control (2.1%, 2.3 times as much as structured claims and EHR data combined), decubitus ulcer (1.4%, 1.7 times as much), dementia (6.7%, 1.5 times as much), falls (23.6%, 3.2 times as much), malnutrition (2.5%, 18.0 times as much), lack of social support (29.8%, 455.9 times as much), urinary retention (4.2%, 3.9 times as much), vision impairment (6.2%, 7.4 times as much), weight loss (19.2%, 2.9 as much), and walking difficulty (36.34%, 3.4 as much). The geriatric syndrome rates extracted from structured data were substantially lower than published epidemiological rates, although adding the NLP results considerably closed this gap.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Claims and structured EHR data give an incomplete picture of burden related to geriatric syndromes. Geriatric syndromes are likely to be missed if unstructured data are not analyzed. Pragmatic NLP algorithms can assist with identifying individuals at high risk of experiencing geriatric syndromes and improving coordination of care for older adults.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jgs.15411","type":"journal-article","created":{"date-parts":[[2018,7,4]],"date-time":"2018-07-04T08:45:41Z","timestamp":1530693941000},"page":"1499-1507","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification"],"prefix":"10.1111","volume":"66","author":[{"given":"Hadi","family":"Kharrazi","sequence":"first","affiliation":[{"name":"Center for Population Health Information Technology, Department of Health Policy and Management Bloomberg School of Public Health"},{"name":"Division of Health Sciences and Informatics, Department of General Internal Medicine University School of Medicine, Johns Hopkins 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