{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T00:02:06Z","timestamp":1793664126755,"version":"4.5.1"},"reference-count":25,"publisher":"American Medical Association (AMA)","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n Hospital quality reporting remains a manual, costly process with critical limitations as a mechanism to improve care outcomes.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To assess whether near-real-time quality measurement, enabled by large language models (LLMs), can improve quality performance as measured by the Centers for Medicare &amp;amp; Medicaid Services (CMS) Severe Sepsis and Septic Shock Management Bundle (SEP-1) quality metric.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This single-blind, unstratified, cluster randomized trial was conducted between December 13, 2024, and July 8, 2025, at 2 academic emergency departments (EDs) within the University of California, San Diego (UCSD) health system. Participants included all 66 attending physicians who practiced in the UCSD EDs and worked more than 3 shifts per month prior to study initiation.<\/jats:p>\n <\/jats:sec>\n \n Intervention<\/jats:title>\n Participants were randomized to receive targeted feedback from LLM-determined compliance with SEP-1 at the time of patient discharge or standard process.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n The primary outcome was overall compliance with SEP-1. Secondary outcomes included expert agreement with the LLM SEP-1 determination, 30-day mortality, and intensive care unit admissions of patients with severe sepsis and\/or septic shock in the ED. Effect sizes were estimated from a mixed-effects logistic regression model with the intervention group as a fixed effect and a random intercept for physician.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The study population included 66 physicians who treated 301 patients (121 in the control group and 180 in the intervention group; median age, 64.3 [IQR, 51.1-75.7] years; 171 [56.8%] male; 52 [17.3%] with chronic kidney disease; 52 [17.3%] with chronic heart failure) who met CMS inclusion criteria for SEP-1. Physicians in the control group had a SEP-1 compliance rate of 70.1%, while those in the intervention group had a rate of 82.9%. Assignment to the intervention group resulted in a 13.0% absolute improvement in SEP-1 compliance (95% CI, 2.5%-23.4%; odds ratio, 2.10 [95% CI, 1.15-3.81];\n P<\/jats:italic>\n \u2009=\u2009.02) in the mixed-effects model. The largest difference between the intervention group and control group was in noncompletion of the 30-mL\/kg fluid bolus component (3 of 180 [1.7%] vs 16 of 121 [13.2%]), a documentation-sensitive component of the quality measure. Agreement between LLM determination and expert review was 92%. No significant differences existed in intensive care unit admissions or 30-day mortality.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this cluster randomized trial of artificial intelligence (AI)-enabled medical record abstraction for sepsis care, rapid assessment of SEP-1 performance and targeted feedback improved overall compliance with the measure. AI-driven quality clinical integration may address limitations in existing hospital quality reporting and better support a learning health system.<\/jats:p>\n <\/jats:sec>\n \n Trial Registration<\/jats:title>\n \n ClinicalTrials.gov Identifier:\n NCT07581340<\/jats:ext-link>\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2026.11885","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:30:36Z","timestamp":1782401436000},"page":"e2611885","source":"Crossref","is-referenced-by-count":3,"title":["Medical Record Abstraction for Quality Improvement in Sepsis Care Using Artificial Intelligence"],"prefix":"10.1001","volume":"9","author":[{"given":"Aaron","family":"Boussina","sequence":"first","affiliation":[{"name":"Division of Biomedical Informatics, University of California, San Diego, San Diego, California"},{"name":"Joan & Irwin Jacobs Center for Health Innovation, University of California, San Diego, San 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and Sleep Medicine, University of California, San Diego, San Diego"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael T.","family":"McCurdy","sequence":"additional","affiliation":[{"name":"Division of Pulmonary and Critical Care Medicine, University of Maryland School of Medicine, Baltimore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher A.","family":"Longhurst","sequence":"additional","affiliation":[{"name":"Division of Biomedical Informatics, University of California, San Diego, San Diego, California"},{"name":"Joan & Irwin Jacobs Center for Health Innovation, University of California, San Diego, San Diego"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James S.","family":"Ford","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, University of California, San Diego, San 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M, Dy? SM, Stewart? CM, . Making healthcare safer IV: final report on prioritization of patient safety practices for a new rapid review or rapid response. Agency for Healthcare Research and Quality. July 2023. Accessed February 26, 2026. https:\/\/effectivehealthcare.ahrq.gov\/sites\/default\/files\/related_files\/prioritization-report.pdf","DOI":"10.23970\/AHRQEPC_MHS4PRIORITIZATION"},{"key":"zoi260362r11","unstructured":"Inpatient hospitals specifications manual. Centers for Medicare & Medicaid Services. February 21, 2026. Accessed October 2, 2025. https:\/\/qualitynet.cms.gov\/inpatient\/specifications-manuals"},{"key":"zoi260362r19","unstructured":"Grattafiori? A, Dubey? A, Jauhri? A, . The llama 3 herd of models.? arXiv. Preprint posted online November 23, 2024. doi:10.48550\/arXiv.2407.21783"},{"key":"zoi260362r20","unstructured":"Aboussina? A. quallm. Accessed February 26, 2026. https:\/\/github.com\/aboussina\/quallm"},{"key":"zoi260362r21","unstructured":"Snell? C, Lee? 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