{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:32Z","timestamp":1755219812988,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686080"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Overprescription of opioids and antibiotics remains a significant public health challenge in the US, contributing to systemic and dental health issues. This study developed and tested a natural language processing (NLP) model to identify patients visiting the emergency department (ED) at Temple University Health System for dental-related reasons. We extracted data from EHR and EDR systems, yielding a cohort of 89,349 patients, including 2,918 (3%) with dental-related ED visits. Using gold-standard datasets created through manual annotation, the NLP model combined fuzzy matching and embedding-based algorithms, achieving 95% accuracy, 98% specificity, and 92% sensitivity. The cohort was evenly split by gender, predominantly African American\/Black (57%), with most patients aged 20\u201340 years (54%), and the majority relying on Medicaid (36%) or Medicare (28%). Notably, 70% of patients received antibiotics, and 11% were prescribed opioids. This study demonstrated the high prevalence of antibiotics and opioid prescriptions for dental pain in ED settings.<\/jats:p>","DOI":"10.3233\/shti250862","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:33:21Z","timestamp":1754566401000},"source":"Crossref","is-referenced-by-count":0,"title":["Painful Prescriptions: Opioid and Antibiotic Use for Dental Pain in the Emergency Department"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0559-5958","authenticated-orcid":false,"given":"Jay","family":"Patel","sequence":"first","affiliation":[{"name":"Center for Dental Informatics and Artificial Intelligence, Department of Oral Health Sciences, Temple University Kornberg School of Dentistry"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Usha","family":"Sambamoorthi","sequence":"additional","affiliation":[{"name":"College of Pharmacy, University of North Texas Health Science Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ritwik","family":"Katiyar","sequence":"additional","affiliation":[{"name":"Center for Dental Informatics and Artificial Intelligence, Department of Oral Health Sciences, Temple University Kornberg School of Dentistry"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wayne A.","family":"Satz","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Lewis Katz School of Medicine at Temple University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250862","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:33:21Z","timestamp":1754566401000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250862","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}