{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T10:34:48Z","timestamp":1774953288262,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"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>Large Language Models (LLMs) show potential in medical document generation, but ensuring reliability requires extensive expert involvement, limiting clinical applications. To address this challenge, we developed an LLM-based evaluation framework with three progressive Chain of Thought (CoT) strategies: Qualitative (expert persona), Quantitative-qualitative (error analysis), and Insight-integrated (expert reasoning). This framework captures nuanced evaluation patterns while maintaining efficiency. When tested on 33 LLM-generated Emergency Department records across five criteria, our Insight-integrated approach demonstrated strong correlation with expert evaluations (r = 0.680, p &lt; .001), outperforming both Qualitative (r = 0.524) and Quantitative-qualitative (r = 0.630) approaches. Our findings suggest that LLM-based evaluation frameworks can align with expert assessments as useful tools for validating medical documentation in clinical settings.<\/jats:p>","DOI":"10.3233\/shti250995","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:32Z","timestamp":1754566652000},"source":"Crossref","is-referenced-by-count":1,"title":["LLM-Based Medical Document Evaluation: Integrating Human Expert Insights"],"prefix":"10.3233","author":[{"given":"Junhyuk","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Nursing, Samsung Medical Center"},{"name":"SAIHST, Sungkyunkwan University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dasol","family":"Choi","sequence":"additional","affiliation":[{"name":"Yonsei University"},{"name":"MODULABS"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wonchul","family":"Cha","sequence":"additional","affiliation":[{"name":"SAIHST, Sungkyunkwan University"},{"name":"Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taerim","family":"Kim","sequence":"additional","affiliation":[{"name":"SAIHST, Sungkyunkwan University"},{"name":"Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine"}],"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\/SHTI250995","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:37:32Z","timestamp":1754566652000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250995","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}