{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T01:19:06Z","timestamp":1785115146363,"version":"3.55.0"},"reference-count":20,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01HL141916"],"award-info":[{"award-number":["1R01HL141916"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006751","name":"U.S. Army","doi-asserted-by":"publisher","award":["W81XWH2210610"],"award-info":[{"award-number":["W81XWH2210610"]}],"id":[{"id":"10.13039\/100006751","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Academic Emergency Medicine"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Prehospital emergencies require providers to rapidly identify patients\u2019 medical condition and determine treatment needs. We tested whether medics\u2019 initial, written impressions of patient condition contain information that can help identify patients who require prehospital lifesaving interventions (LSI) prior to or during transport.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We analyzed free\u2010text medic impressions of prehospital patients encountered at the scene of an accident or injury, using data from STAT MedEvac air medical transport service from 2012 to 2021. EMR records were used to identify LSIs performed for these patients in prehospital settings. Text was cleaned via natural language processing and transformed using term frequency\u2013inverse document frequency. A gradient boosting machine learning (ML) model was used to predict individual patient need for prehospital LSI as well as seven LSI subcategories (e.g., airway interventions, blood transfusion, vasopressor medication).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 12,913 prehospital patients were included in our sample (mean age\u2009= 52.3\u2009years, 63% men). We observed good ML performance in predicting overall LSI (area under the receiver operating curve = 0.793, 95% confidence interval = [0.776\u20130.810]; average precision = 0.670, 95% confidence interval = [0.643\u20130.695] vs. LSI rate of 0.282) and equivalent\u2010or\u2010better performance in predicting each LSI subcategory except for crystalloid fluid administration. We identified individual words within medic impressions that portended high (e.g., <jats:italic>unresponsive, hemorrhage<\/jats:italic>) or low (e.g., <jats:italic>droop<\/jats:italic>, <jats:italic>rib<\/jats:italic>) LSI rates. Calibration analysis showed that models could prioritize correct LSI identification (i.e., high sensitivity) or accurate triage (i.e., low false\u2010positive rate). Sensitivity analyses showed that model performance was robust when removing from medic impressions words that directly labeled an LSI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>ML based on free\u2010text medic impressions can help identify patient need for prehospital LSI. We discuss future work, such as applying similar methods to 9\u20101\u20101 call requests, and potential applications, including voice\u2010to\u2010text translation of medic impressions.<\/jats:p><\/jats:sec>","DOI":"10.1111\/acem.15067","type":"journal-article","created":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T05:00:05Z","timestamp":1737781205000},"page":"516-525","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Words to live by: Using medic impressions to identify the need for prehospital lifesaving interventions"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9395-4051","authenticated-orcid":false,"given":"Aaron C.","family":"Weidman","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania 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Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonard S.","family":"Weiss","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald K.","family":"Poropatich","sequence":"additional","affiliation":[{"name":"Center for Military Medicine Research University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael R.","family":"Pinsky","sequence":"additional","affiliation":[{"name":"Department of Critical Care Medicine University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2015.10.016"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.annemergmed.2024.01.036"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/10903127.2022.2072984"},{"issue":"1","key":"e_1_2_9_5_1","first-page":"78","article-title":"Identifying patient phenotype cohorts using prehospital electronic health record data","volume":"26","author":"Stemerman R","year":"2022","journal-title":"Prehosp Emerg Care"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/10903127.2022.2074180"},{"key":"e_1_2_9_7_1","first-page":"599\u2010610","article-title":"Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models","volume":"31","author":"Iscoe 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