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

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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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Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure<SUBTITLE>The JNC 7 Report</SUBTITLE>
Chobanian, A. V. (2003) · JAMA · doi:10.1001/jama.289.19.2560 · Cited by 25,386
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
An Introduction to Statistical Learning
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013) · Springer Texts in Statistics · doi:10.1007/978-1-4614-7138-7 · Cited by 12,106
Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J. E., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e31827e83af · Cited by 9,694
Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock, 2012
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Intensive Care Medicine · doi:10.1007/s00134-012-2769-8 · Cited by 7,371
Third universal definition of myocardial infarction
the Writing Group on behalf of the Joint ESC/ACCF/AHA/WHF Task Force for the Universal Definition of Myocardial Infarction, Thygesen, K., Alpert, J. S., Jaffe, A. S., Simoons, M. L., CHAITMAN, B. R., & White, H. D. (2012) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2012.122 · Cited by 6,585
Research guidelines for the Delphi survey technique
Hasson, F. (2000) · Journal of Advanced Nursing · doi:10.1046/j.1365-2648.2000.01567.x · Cited by 5,951

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Show all 52 references

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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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Lam, R. P. K., Kwok, S. L., Chaang, V. K., Chen, L., Lau, E. H. Y., & Chan, K. L. (2020) · Figshare · doi:10.6084/m9.figshare.12462920
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Boonstra, A., & Laven, M. (2022) · Figshare · doi:10.6084/m9.figshare.19793490
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Aubrion, A., Clanet, R., Jourdan, J., Créveuil, C., Roupie, É., & Macrez, R. (2024) · Figshare · doi:10.6084/m9.figshare.26553101
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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: Levin, Scott
Toerper, Matthew
Hamrock, Eric
Hinson, Jeremiah S.
Barnes, Sean
Gardner, Heather
Dugas, Andrea
Kelen, Gabor
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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Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index

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