Evidence map›Paper›PMID 39262027›Full record

SynthesisJournal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing2025

The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.

Nayeon Yi, Dain Baik, Gumhee Baek

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. The effects of applying artificial intelligence to triage in the emergency department: A systematic review of prospective studies.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025
    Pooled it
  4. Perception and challenges of artificial intelligence (AI) in Emergency Medicine: A multi-country study in Sub-Saharan Africa.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026
    Article
  5. Subgroup Differences in Agreement Between an Algorithm Guided Large Language Model and Routine Emergency Department Triage.Medical science monitor : international medical journal of experimental and clinical research · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Nayeon YiCollege of Nursing, Ewha Womans University, Seoul, South Korea.ORCID 0000-0001-5215-1597
Dain BaikCollege of Nursing, Ewha Womans University, Seoul, South Korea.ORCID 0000-0002-3997-1491
Gumhee BaekSystem Health Science & Engineering Program, Ewha Womans University, Seoul, South Korea.ORCID 0000-0003-1999-0158

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAccurate and rapid triage can reduce undertriage and overtriage, which may improve emergency department flow. This study aimed to identify the effects of a prospective study applying artificial intelligence-based triage in the clinical field.

designSystematic review of prospective studies.

methodsCINAHL, Cochrane, Embase, PubMed, ProQuest, KISS, and RISS were searched from March 9 to April 18, 2023. All the data were screened independently by three researchers. The review included prospective studies that measured outcomes related to AI-based triage. Three researchers extracted data and independently assessed the study's quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) protocol.

resultsOf 1633 studies, seven met the inclusion criteria for this review. Most studies applied machine learning to triage, and only one was based on fuzzy logic. All studies, except one, utilized a five-level triage classification system. Regarding model performance, the feed-forward neural network achieved a precision of 33% in the level 1 classification, whereas the fuzzy clip model achieved a specificity and sensitivity of 99%. The accuracy of the model's triage prediction ranged from 80.5% to 99.1%. Other outcomes included time reduction, overtriage and undertriage checks, mistriage factors, and patient care and prognosis outcomes.

conclusionTriage nurses in the emergency department can use artificial intelligence as a supportive means for triage. Ultimately, we hope to be a resource that can reduce undertriage and positively affect patient health. PROTOCOL REGISTRATION: We have registered our review in PROSPERO (registration number: CRD 42023415232).

Indexed as

Artificial IntelligenceEmergency Service, HospitalTriageHumansMachine LearningObservational Studies as TopicProspective Studiesartificial intelligencedecision support systems, clinicalemergency service, hospitalnursestriage

Identifiers

PMID39262027
PMCPMC11771688

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.