{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T03:44:59Z","timestamp":1785901499754,"version":"3.56.0"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K23GM146092"],"award-info":[{"award-number":["K23GM146092"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100019957","name":"National Foundation of Emergency Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100019957","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Health Syst."],"DOI":"10.1038\/s44401-026-00127-7","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T02:52:42Z","timestamp":1785898362000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving sepsis best practice utility and clinical acceptance using an LLM-enhanced prediction system"],"prefix":"10.1038","volume":"3","author":[{"given":"Claire","family":"Allison","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolei","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aaron","family":"Boussina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shamim","family":"Nemati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gabriel","family":"Wardi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"127_CR1","doi-asserted-by":"publisher","first-page":"e2013","DOI":"10.1016\/S2214-109X(25)00356-0","volume":"13","author":"GBD 2021","year":"2025","unstructured":"GBD 2021 Global Sepsis Collaborators. Global, regional, and national sepsis incidence and mortality, 1990-2021: a systematic analysis. Lancet Glob. Health 13, e2013\u2013e2026 (2025).","journal-title":"Lancet Glob. Health"},{"key":"127_CR2","doi-asserted-by":"publisher","first-page":"6","DOI":"10.4046\/trd.2018.0041","volume":"82","author":"HI Kim","year":"2019","unstructured":"Kim, H. I. & Park, S. Sepsis: early recognition and optimized treatment. Tuberc. Respir. Dis. (Seoul.) 82, 6\u201314 (2019).","journal-title":"Tuberc. Respir. Dis. (Seoul.)"},{"key":"127_CR3","doi-asserted-by":"publisher","DOI":"10.1186\/s13017-018-0165-6","volume":"13","author":"M Sartelli","year":"2018","unstructured":"Sartelli, M. et al. Raising concerns about the Sepsis-3 definitions. World J. Emerg. Surg. 13, 6 (2018).","journal-title":"World J. Emerg. Surg."},{"key":"127_CR4","doi-asserted-by":"publisher","first-page":"2096","DOI":"10.1097\/CCM.0b013e318250a887","volume":"40","author":"MH Hooper","year":"2012","unstructured":"Hooper, M. H. et al. Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit*. Crit. Care Med 40, 2096 (2012).","journal-title":"Crit. Care Med"},{"key":"127_CR5","doi-asserted-by":"publisher","first-page":"1510792","DOI":"10.3389\/fmed.2024.1510792","volume":"11","author":"F Li","year":"2025","unstructured":"Li, F. et al. Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring. Front Med (Lausanne) 11, 1510792 (2025).","journal-title":"Front Med (Lausanne)"},{"key":"127_CR6","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41746-023-00986-6","volume":"7","author":"A Boussina","year":"2024","unstructured":"Boussina, A. et al. Impact of a deep learning sepsis prediction model on quality of care and survival. npj Digit. Med. 7, 14 (2024).","journal-title":"npj Digit. Med."},{"key":"127_CR7","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1038\/s41746-025-01689-w","volume":"8","author":"SP Shashikumar","year":"2025","unstructured":"Shashikumar, S. P. et al. Development and prospective implementation of a large language model based system for early sepsis prediction. npj Digit. Med. 8, 290 (2025).","journal-title":"npj Digit. Med."},{"key":"127_CR8","first-page":"195","volume":"14","author":"AB McCoy","year":"2014","unstructured":"McCoy, A. B., Thomas, E. J., Krousel-Wood, M. & Sittig, D. F. Clinical decision support alert appropriateness: a review and proposal for improvement. Ochsner J. 14, 195\u2013202 (2014).","journal-title":"Ochsner J."},{"key":"127_CR9","first-page":"e1276","volume":"7","author":"K Ramesh","year":"2025","unstructured":"Ramesh, K. et al. Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey. Crit. Care Explor. 7, e1276 (2025).","journal-title":"Crit. Care Explor."},{"key":"127_CR10","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins, G. S. et al. TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).","journal-title":"BMJ"},{"key":"127_CR11","doi-asserted-by":"publisher","first-page":"e34","DOI":"10.2196\/jmir.6.3.e34","volume":"6","author":"G Eysenbach","year":"2004","unstructured":"Eysenbach, G. Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES. J. Med Internet Res. 6, e34 (2004).","journal-title":"J. Med Internet Res."},{"key":"127_CR12","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1055\/s-0039-1700869","volume":"10","author":"P Elias","year":"2019","unstructured":"Elias, P. et al. Evaluating the Impact of Interruptive Alerts within a Health System: Use, Response Time, and Cumulative Time Burden. Appl Clin. Inf. 10, 909\u2013917 (2019).","journal-title":"Appl Clin. Inf."},{"key":"127_CR13","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1097\/CCM.0000000000003803","volume":"47","author":"JC Ginestra","year":"2019","unstructured":"Ginestra, J. C. et al. Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock. Crit. Care Med 47, 1477\u20131484 (2019).","journal-title":"Crit. Care Med"},{"key":"127_CR14","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s44401-025-00062-z","volume":"3","author":"A Wong","year":"2026","unstructured":"Wong, A., Singh, K. & Nemati, S. Rethinking sepsis prediction in the era of large language models. npj Health Syst. 3, 5 (2026).","journal-title":"npj Health Syst."},{"key":"127_CR15","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1197\/jamia.M2334","volume":"14","author":"JA Osheroff","year":"2007","unstructured":"Osheroff, J. A. et al. A roadmap for national action on clinical decision support. J. Am. Med Inf. Assoc. 14, 141\u2013145 (2007).","journal-title":"J. Am. Med Inf. Assoc."},{"key":"127_CR16","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1001\/2013.jamainternmed.61","volume":"173","author":"H Singh","year":"2013","unstructured":"Singh, H., Spitzmueller, C., Petersen, N. J., Sawhney, M. K. & Sittig, D. F. Information overload and missed test results in electronic health record-based settings. JAMA Intern Med 173, 702\u2013704 (2013).","journal-title":"JAMA Intern Med"},{"key":"127_CR17","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-017-0430-8","volume":"17","author":"JS Ancker","year":"2017","unstructured":"Ancker, J. S. et al. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inf. Decis. Mak. 17, 36 (2017).","journal-title":"BMC Med Inf. Decis. Mak."},{"key":"127_CR18","doi-asserted-by":"publisher","DOI":"10.1186\/s12912-025-03369-2","volume":"24","author":"EAM Michels","year":"2025","unstructured":"Michels, E. A. M., Gilbert, S., Koval, I. & Wekenborg, M. K. Alarm fatigue in healthcare: a scoping review of definitions, influencing factors, and mitigation strategies. BMC Nurs. 24, 664 (2025).","journal-title":"BMC Nurs."},{"key":"127_CR19","unstructured":"Kordek, M. Alert fatigue by other names: review of contributing fields regarding the \u2018cry wolf\u2019 effect. Oregon Health and Science University; (2013)."},{"key":"127_CR20","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1038\/s41746-025-01971-x","volume":"8","author":"ML Burns","year":"2025","unstructured":"Burns, M. L. et al. Generative AI costs in large healthcare systems, an example in revenue cycle. NPJ Digit Med. 8, 579 (2025).","journal-title":"NPJ Digit Med."},{"key":"127_CR21","doi-asserted-by":"publisher","first-page":"e64226","DOI":"10.2196\/64226","volume":"26","author":"R Nagarajan","year":"2024","unstructured":"Nagarajan, R. et al. Economics and Equity of Large Language Models: Health Care Perspective. J. Med Internet Res. 26, e64226 (2024).","journal-title":"J. Med Internet Res."},{"key":"127_CR22","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1097\/CCM.0000000000003342","volume":"46","author":"CJ Paoli","year":"2018","unstructured":"Paoli, C. J., Reynolds, M. A., Sinha, M., Gitlin, M. & Crouser, E. Epidemiology and Costs of Sepsis in the United States-An Analysis Based on Timing of Diagnosis and Severity Level. Crit. Care Med. 46, 1889\u20131897 (2018).","journal-title":"Crit. Care Med."},{"key":"127_CR23","first-page":"483","volume":"41","author":"SL Jones","year":"2015","unstructured":"Jones, S. L. et al. Reductions in Sepsis Mortality and Costs After Design and Implementation of a Nurse-Based Early Recognition and Response Program. Jt Comm. J. Qual. Patient Saf. 41, 483\u2013491 (2015).","journal-title":"Jt Comm. J. Qual. Patient Saf."},{"key":"127_CR24","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1038\/s41746-024-01066-z","volume":"7","author":"JCC Kwong","year":"2024","unstructured":"Kwong, J. C. C., Nickel, G. C., Wang, S. C. Y. & Kvedar, J. C. Integrating artificial intelligence into healthcare systems: more than just the algorithm. NPJ Digit Med. 7, 52 (2024).","journal-title":"NPJ Digit Med."}],"container-title":["npj Health Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s44401-026-00127-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s44401-026-00127-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s44401-026-00127-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T02:52:45Z","timestamp":1785898365000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s44401-026-00127-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,5]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["127"],"URL":"https:\/\/doi.org\/10.1038\/s44401-026-00127-7","relation":{},"ISSN":["3005-1959"],"issn-type":[{"value":"3005-1959","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,5]]},"assertion":[{"value":"30 January 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A. Boussina and S. Nemati are co-founders of and hold equity in Clairyon Inc., a start-up that develops products related to digital health. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflicts of interest policies. A. Boussina acknowledges grant funding from the U.S. National Heart, Lung, and Blood Institute (award number R01HL157985). S. Nemati acknowledges grant funding from the U.S. National Institute of General Medical Sciences (award numbers #R35GM143121, #R42AI177108). Dr. Gabriel Wardi is an Editorial Board Member of\n                      NPJ Health Systems\n                      . Dr. Gabriel Wardi was not involved in the journal\u2019s review of, or decisions related to, this manuscript. The other authors do not declare a competing interest.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"72"}}