{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T18:34:55Z","timestamp":1784399695228,"version":"3.55.0"},"reference-count":16,"publisher":"Oxford University Press (OUP)","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>People who inject drugs (PWID) face disproportionate risks for infectious diseases yet remain difficult to identify within health care systems. Natural language processing (NLP) offers potential solutions for identifying PWID to improve access to harm reduction services.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We evaluated an NLP dashboard designed to identify Veterans with evidence of injection drug use across 6 Veterans Health Administration facilities between August and October 2024. Four independent reviewers assessed electronic health records to confirm recent injection drug use and evaluated preventive care delivery, including HIV\/hepatitis screening, sexually transmitted infection testing, preexposure prophylaxis usage, and harm reduction services.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Among 502 075 veterans, the dashboard identified 507 potential PWID, with 78 (15%) confirmed through chart review. Of confirmed PWID, 49% injected opiates, 41% cocaine, and 37% methamphetamines. HIV prevalence was 6%, hepatitis C antibody positivity 45% (28% viremic), and hepatitis B exposure 13%. Despite 94% engaging with mental health services and 82% with social work, only 29% saw infectious disease specialists. Most PWID (88%) had not received syringes, 74% lacked recent gonorrhea\/chlamydia screening, and only 1 received HIV preexposure prophylaxis. Independent reviewers completed most chart reviews within 1 to 2\u2005minutes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>The NLP dashboard efficiently identified PWID within an extensive health care system, revealing significant gaps in preventive care delivery despite high engagement with mental health services. Findings suggest opportunities to leverage existing therapeutic relationships while enhancing collaboration among mental health, social work, and infectious disease services to improve care for this vulnerable population.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ofid\/ofaf113","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T19:08:49Z","timestamp":1741201729000},"source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Natural Language Processing to Identify Veterans Who Inject Drugs to Assess Preexposure Prophylaxis and Sexually Transmitted Infection Testing Services at the Veterans Health Administration"],"prefix":"10.1093","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7509-6156","authenticated-orcid":false,"given":"Minh Q","family":"Ho","sequence":"first","affiliation":[{"name":"Department of Internal Medicine, College of Medicine, University of Central Florida , Orlando, Florida ,","place":["USA"]},{"name":"Department of Internal Medicine, Section Infectious Disease, Orlando VA Healthcare System , Orlando, Florida ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin","family":"O\u2019Connor","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Section Infectious Disease, Orlando VA Healthcare System , Orlando, Florida ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4056-0737","authenticated-orcid":false,"given":"Karine","family":"Rozenberg-Ben-Dror","sequence":"additional","affiliation":[{"name":"VISN 12 Pharmacy Benefit Management, Veterans Affairs Great Lakes Health Care System , Westchester, Illinois ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed S","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Section Infectious Disease, Orlando VA Healthcare System , Orlando, Florida ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Slazinski","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Section Infectious Disease, Orlando VA Healthcare System , Orlando, Florida ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"2025052712080634400_ofaf113-B1","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1093\/cid\/ciac543","article-title":"Estimated number of people who inject drugs in the United States","volume":"76","author":"Bradley","year":"2023","journal-title":"Clin Infect Dis"},{"key":"2025052712080634400_ofaf113-B2","doi-asserted-by":"crossref","first-page":"19132","DOI":"10.1038\/s41598-023-45115-1","article-title":"Deep-learning-based natural-language-processing models to identify cardiovascular disease hospitalisations of patients 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