Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025
Words to live by: Using medic impressions to identify the need for prehospital lifesaving interventions.
СтатьяПоддержка исследования: вне правительства СШАПоддержка правительства СШАПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Weidman Aaron C, Sedor-Schiffhauer Zach, Zikmund Chase, Salcido David D, Guyette Francis X, Weiss Leonard S, Poropatich Ronald K, Pinsky Michael R
Аннотация
BACKGROUND: Prehospital emergencies require providers to rapidly identify patients' medical condition and determine treatment needs. We tested whether medics' initial, written impressions of patient condition contain information that can help identify patients who require prehospital lifesaving interventions (LSI) prior to or during transport.
METHODS: We analyzed free-text 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-inverse 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).
RESULTS: A total of 12,913 prehospital patients were included in our sample (mean age = 52.3 years, 63% men). We observed good ML performance in predicting overall LSI (area under the receiver operating curve = 0.793, 95% confidence interval = [0.776-0.810]; average precision = 0.670, 95% confidence interval = [0.643-0.695] vs. LSI rate of 0.282) and equivalent-or-better performance in predicting each LSI subcategory except for crystalloid fluid administration. We identified individual words within medic impressions that portended high (e.g., unresponsive, hemorrhage) or low (e.g., droop, rib) LSI rates. Calibration analysis showed that models could prioritize correct LSI identification (i.e., high sensitivity) or accurate triage (i.e., low false-positive rate). Sensitivity analyses showed that model performance was robust when removing from medic impressions words that directly labeled an LSI.
CONCLUSIONS: ML based on free-text medic impressions can help identify patient need for prehospital LSI. We discuss future work, such as applying similar methods to 9-1-1 call requests, and potential applications, including voice-to-text translation of medic impressions.
METHODS: We analyzed free-text 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-inverse 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).
RESULTS: A total of 12,913 prehospital patients were included in our sample (mean age = 52.3 years, 63% men). We observed good ML performance in predicting overall LSI (area under the receiver operating curve = 0.793, 95% confidence interval = [0.776-0.810]; average precision = 0.670, 95% confidence interval = [0.643-0.695] vs. LSI rate of 0.282) and equivalent-or-better performance in predicting each LSI subcategory except for crystalloid fluid administration. We identified individual words within medic impressions that portended high (e.g., unresponsive, hemorrhage) or low (e.g., droop, rib) LSI rates. Calibration analysis showed that models could prioritize correct LSI identification (i.e., high sensitivity) or accurate triage (i.e., low false-positive rate). Sensitivity analyses showed that model performance was robust when removing from medic impressions words that directly labeled an LSI.
CONCLUSIONS: ML based on free-text medic impressions can help identify patient need for prehospital LSI. We discuss future work, such as applying similar methods to 9-1-1 call requests, and potential applications, including voice-to-text translation of medic impressions.