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Cited by 424Emergency and Acute Care StudiesMachine Learning in HealthcareArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

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

Levin, Scott, Toerper, Matthew, Hamrock, Eric, Hinson, Jeremiah S., Barnes, Sean, Gardner, Heather, Dugas, Andrea, Linton, Bob, Kirsch, Tom, Kelen, Gabor·2018·Annals of Emergency Medicine
DOI 10.1016/j.annemergmed.2017.08.005
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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.Methods A multisite, retrospective, cross-sectional study of 172,726 ED visits from urban and community EDs was conducted. E-triage is composed of a random forest model applied to triage data (vital signs, chief complaint, and active medical history) that predicts the need for critical care, an emergency procedure, and inpatient hospitalization in parallel and translates risk to triage level designations. Predicted outcomes and secondary outcomes of elevated troponin and lactate levels were evaluated and compared with the Emergency Severity Index (ESI).Results E-triage predictions had an area under the curve ranging from 0.73 to 0.92 and demonstrated equivalent or improved identification of clinical patient outcomes compared with ESI at both EDs. E-triage provided rationale for risk-based differentiation of the more than 65% of ED visits triaged to ESI level 3. Matching the ESI patient distribution for comparisons, e-triage identified more than 10% (14,326 patients) of ESI level 3 patients requiring up triage who had substantially increased risk of critical care or emergency procedure (1.7% ESI level 3 versus 6.2% up triaged) and hospitalization (18.9% versus 45.4%) across EDs.Conclusion 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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  1. Gilboy N, Tanabe T, Travers D, et al. Emergency Severity Index (ESI): A Triage Tool for Emergency Department Care, Version 4. Implementation Handbook 2012 Edition. Rockville, MD: Agency for Healthcare Research and Quality; 2012.

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Metadata

Authors
Levin, S., Toerper, M., Hamrock, E., Hinson, J. S., Barnes, S., Gardner, H., Dugas, A., Linton, B., Kirsch, T., & Kelen, G.
Affiliations
  • Johns Hopkins University (US)
  • University of Maryland, College Park (US)
  • Uniformed Services University of the Health Sciences (US)
Corresponding author
Levin, Scott
ORCID
Venue
Annals of Emergency Medicine
Publisher
Elsevier BV
Published
2018-05
Volume
71
Issue
5
Pages
565-574.e2
ISSN
0196-0644 (ISSN-L)
DOI
10.1016/j.annemergmed.2017.08.005
OpenAlex
W2752349109
Cited by
424
References
52
Citations by year
2026: 71 · 2025: 60 · 2024: 64 · 2023: 53 · 2022: 54 · 2021: 37 · 2020: 39 · 2019: 29 · 2018: 16
FWCI
29.57
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Annals of Emergency Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
Agency for Healthcare Research and Quality (R21HS023641)
Topics
  • Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
emergency department triage, Emergency Severity Index, risk stratification, random forest, machine learning prediction, hospitalization prediction, troponin, lactate, triage decision support
MeSH terms
Machine Learning, Adult, Algorithms, Cross-Sectional Studies, Emergency Service, Hospital, Female, Humans, Male, Retrospective Studies, Triage, United States, Area Under Curve, Vital Signs

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