ReviewJournal of Korean biological nursing science2025
Artificial intelligence in emergency department triage: a scoping review on workload reduction and patient safety enhancement.
Review in Journal of Korean biological nursing science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Impact of Artificial Intelligence-supported Triage Systems on Emergency Department Management: A Comparison of Infermedica, Emergency Severity Index, and Manchester Triage System.The western journal of emergency medicine · 2026Trial
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: This scoping review aimed to evaluate current evidence regarding the application of artificial intelligence (AI)-based triage systems in emergency departments (EDs), with a focus on their contributions to workload reduction, patient safety, and decision-making accuracy from a nursing perspective. Methods: A scoping review was conducted in accordance with PRISMA-ScR guidelines. Six electronic databases (PubMed, CINAHL Plus with Full Text, JSTOR, IEEE Xplore, ProQuest, and Web of Science) were searched for articles published between January 2014 and December 2024. Studies were included if they applied AI techniques to ED triage and reported outcomes related to workload, safety, or triage performance. Data were extracted and thematically analyzed to identify key contributions of AI-based triage systems. Eight studies met the inclusion criteria. Results: Three major themes were identified: (1) improvement in decision-making accuracy through AI-assisted triage models, (2) reduction in clinician workload, and (3) enhanced identification of critically ill patients contributing to patient safety. Some models achieved high predictive performance, with Area Under the Receiver Operating Characteristic Curve scores reaching up to 0.96. However, heterogeneity in study designs and limited nurse involvement restricted the generalizability and clinical applicability of these studies. Conclusion: This review synthesizes existing literature on AI-supported triage systems in emergency care and provides foundational insights into their potential to support nursing decision-making. Future research should focus on nurse-centered system design, usability testing in real-world settings, and evaluation of clinical outcomes to ensure effective and ethical integration into nursing practice.
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What OpenQuestion holds
Registered trials
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 OpenQuestion graph.