Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index.

Journal: Annals of emergency medicine
Published Date: Sep 6, 2017

Abstract

STUDY OBJECTIVE: 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.

Authors

  • Scott Levin
    Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.
  • Matthew Toerper
    Department of Emergency Medicine, Johns Hopkins University, Baltimore, MD 21218, United States.
  • Eric Hamrock
    Department of Operations Integration, Johns Hopkins Health System, Baltimore, MD, USA.
  • Jeremiah S Hinson
    Department of Emergency Medicine, Johns Hopkins University, Baltimore, Maryland, United States.
  • Sean Barnes
    Department of Decision, Operations & Information Technologies, Robert H. Smith School of Business, 4352 Van Munching Hall, University of Maryland, College Park, MD 20742, USA sbarnes@rhsmith.umd.edu.
  • Heather Gardner
    Emergency Medicine, Johns Hopkins University, Baltimore, MD.
  • Andrea Dugas
    Emergency Medicine, Johns Hopkins University, Baltimore, MD.
  • Bob Linton
    Emergency Medicine, Johns Hopkins University, Baltimore, MD.
  • Tom Kirsch
    National Center for Disaster Medicine and Public Health, Uniformed Services University, Bethesda, MD.
  • Gabor Kelen
    Emergency Medicine, Johns Hopkins University, Baltimore, MD.

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