Annals of emergency medicine
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Multicenter Study Comparative Study
Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index.
Standards for emergency department (ED) triage in the United States rely heavily on subjective assessment and are limited in their ability to risk-stratify patients. This study seeks to evaluate an electronic triage system (e-triage) based on machine learning that predicts likelihood of acute outcomes enabling improved patient differentiation. ⋯ E-triage more accurately classifies ESI level 3 patients and highlights opportunities to use predictive analytics to support triage decisionmaking. Further prospective validation is needed.
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Although often the focus of quality improvement efforts, emergency medical services (EMS) advanced airway management performance has few national comparisons, nor are there many assessments with benchmarks accounting for differences in agency volume or patient mix. We seek to assess variations in advanced airway management and conventional intubation performance in a national cohort of EMS agencies. ⋯ In this national series, EMS advanced airway management and initial conventional intubation performance varied widely. Reliability adjustment and risk standardization may influence EMS airway management performance assessments.