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Cited by 132Emergency and Acute Care StudiesTrauma and Emergency Care StudiesSepsis Diagnosis and Treatment

An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes

Dugas, Andrea Freyer, Kirsch, Thomas D., Toerper, Matthew, Korley, Fred, Yenokyan, Gayane, France, Daniel, Hager, David, Levin, Scott·2016·The Journal of Emergency Medicine
DOI 10.1016/j.jemermed.2016.02.026
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Abstract

Background Patient triage is necessary to manage excessive patient volumes and identify those with critical conditions. The most common triage system used today, Emergency Severity Index (ESI), focuses on resources utilized and critical outcomes.Objective This study derives and validates a computer-based electronic triage system (ETS) to improve patient acuity distribution based on serious patient outcomes.Methods This cross-sectional study of 25,198 (97 million weighted) adult emergency department visits from the 2009 National Hospital Ambulatory Medical Care Survey. The ETS distributes patients by using a composite outcome based on the estimated probability of mortality, intensive care unit admission, or transfer to operating room or catheterization suite. We compared the ETS with the ESI based on the differentiation of patients, outcomes, inpatient hospitalization, and resource utilization.Results Of the patients included, 3.3% had the composite outcome and 14% were admitted, and 2.52 resources/patient were used. Of the 90% triaged to low-acuity levels, ETS distributed patients evenly (Level 3: 30%; Level 4: 30%, and Level 5: 29%) compared to ESI (46%, 34%, and 7%, respectively). The ETS better-identified patients with the composite outcome present in 40% of ETS Level 1 vs. 17% for ESI and the ETS area under the receiver operating characteristic curve (AUC) was 0.83 vs. ESI 0.73. Similar results were found for hospital admission (ETS AUC = 0.83 vs. ESI AUC = 0.72). The ETS demonstrated slight improvements in discriminating patient resource utilization.Conclusions The ETS is a triage system based on the frequency of critical outcomes that demonstrate improved differentiation of patients compared to the current standard ESI.

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Crossref · 10.1016/j.jemermed.2016.02.026

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  • Most prior models predicting hospital admission at ED triage include only information collected at triage such as demographics, vital signs, chief complaint, nursing notes, and early diagnostics.

    “Most models only include information collected at triage such as demographics, vital signs, chief complaint, nursing notes, and early diagnostics [11,14–19]”

    From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [14]

  • A patient's likelihood of admission may serve as a proxy for acuity, which is used in downstream decisions such as bed placement and the need for emergency intervention.

    “From the perspective of patient care in the ED setting, a patient's likelihood of admission may serve as a proxy for acuity, which is used in a number of downstream decisions such as bed placement and the need for emergency intervention [12–14].”

    From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [14]

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Authors
Dugas, A. F., Kirsch, T. D., Toerper, M., Korley, F., Yenokyan, G., France, D., Hager, D., & Levin, S.
Affiliations
  • Johns Hopkins University (US)
  • Vanderbilt University (US)
Corresponding author
Dugas, Andrea Freyer
ORCID
Venue
The Journal of Emergency Medicine
Publisher
Elsevier BV
Published
2016-06
Volume
50
Issue
6
Pages
910-918
ISSN
0736-4679 (ISSN-L)
DOI
10.1016/j.jemermed.2016.02.026
OpenAlex
W2345073978
Cited by
132
References
24
Citations by year
2026: 7 · 2025: 16 · 2024: 15 · 2023: 8 · 2022: 11 · 2021: 22 · 2020: 24 · 2019: 14 · 2018: 9 · 2017: 6
FWCI
12.80
Type
journal article
Language
en
Update notices
erratum (2016-08-01)
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Journal of Emergency Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
PACER: National Center for Study of Preparedness and Response (2010-ST-061-PA0001)
Topics
  • Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
  • Health Sciences › Medicine › Emergency Medicine › Trauma and Emergency Care Studies
  • Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
Keywords
emergency department triage, Emergency Severity Index, mortality prediction, intensive care unit admission, hospitalization, resource utilization (health care), critical outcomes
MeSH terms
Adolescent, Adult, Aged, Aged, 80 and over, Cross-Sectional Studies, Emergency Service, Hospital, Female, Humans, Male, Middle Aged, Retrospective Studies, Severity of Illness Index, Triage, Area Under Curve, Young Adult, Electronic Health Records

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