Effectiveness of AI-assisted ESI triage on accuracy and selected outcomes in emergency nursing: A systematic review

IF 1.8 4区医学 Q2 NURSING

International Emergency Nursing Pub Date : 2025-09-20 DOI:10.1016/j.ienj.2025.101680

Aekkachai Fatai , Chakrit Sattayarom , Wiwat Laochai , Ekkalak Faksook

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引用次数: 0

Abstract

Aims

To evaluate the effectiveness of artificial intelligence (AI) assisted Emergency Severity Index (ESI) triage systems in improving triage accuracy, selected outcomes including under-triage and over-triage, waiting time and patient workflow, and barriers to implementation in emergency nursing.

Design

Systematic review.

Methods

A narrative synthesis was used to evaluate findings from eligible studies. The Mixed Methods Appraisal Tool (MMAT) was applied for quality assessment. Studies were included if they examined AI-assisted ESI triage systems involving emergency nurses and reported on triage performance and implementation challenges.

Data Sources

Search was performed in CINAHL, Medline, PsycINFO, PubMed, and Google Scholar for English-language articles published between 2018 and 2025.

Results

Ten studies met the inclusion criteria. AI-assisted ESI triage systems improved accuracy, demonstrating higher AUC, F1 score, sensitivity, and specificity compared to traditional triage nursing. These systems also reduced rates of over-triage and under-triage, minimized long waiting times, and enhanced patient flow. However, barriers included reliance on retrospective data, the need for model validation, and potential resistance from nurses.

Conclusion

AI-assisted ESI triage systems demonstrate promising benefits in enhancing triage accuracy and efficiency in emergency nursing. While AI can be a valuable decision-support tool, it should complement rather than replace clinical judgment. Integrating AI into emergency triage may streamline workflows, reduce workload, and improve the accuracy of patient assessments.

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人工智能辅助ESI分诊对急诊护理准确性和选择结果的有效性：一项系统综述

目的评估人工智能（AI）辅助急诊严重程度指数（ESI）分诊系统在提高分诊准确性、选择结果（包括分诊不足和分诊过度）、等待时间和患者工作流程以及急诊护理实施障碍方面的有效性。DesignSystematic审查。方法采用叙事综合法对符合条件的研究结果进行评价。采用混合方法评价工具（MMAT）进行质量评价。如果研究检查了涉及急诊护士的人工智能辅助ESI分诊系统，并报告了分诊表现和实施挑战，则纳入研究。在CINAHL、Medline、PsycINFO、PubMed和谷歌Scholar中检索2018年至2025年间发表的英语文章。结果10项研究符合纳入标准。与传统的分诊护理相比，人工智能辅助ESI分诊系统提高了准确性，显示出更高的AUC、F1评分、敏感性和特异性。这些系统还降低了分诊过度和分诊不足的比率，最大限度地减少了漫长的等待时间，并提高了患者流量。然而，障碍包括对回顾性数据的依赖、模型验证的需要以及护士的潜在阻力。结论人工智能辅助ESI分诊系统在提高急诊护理分诊的准确性和效率方面具有良好的应用前景。虽然人工智能可以成为一种有价值的决策支持工具，但它应该补充而不是取代临床判断。将人工智能集成到紧急分类中可以简化工作流程，减少工作量，并提高患者评估的准确性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Emergency Nursing NURSING-

CiteScore

3.20

自引率

11.10%

发文量

期刊介绍： International Emergency Nursing is a peer-reviewed journal devoted to nurses and other professionals involved in emergency care. It aims to promote excellence through dissemination of high quality research findings, specialist knowledge and discussion of professional issues that reflect the diversity of this field. With an international readership and authorship, it provides a platform for practitioners worldwide to communicate and enhance the evidence-base of emergency care. The journal publishes a broad range of papers, from personal reflection to primary research findings, created by first-time through to reputable authors from a number of disciplines. It brings together research from practice, education, theory, and operational management, relevant to all levels of staff working in emergency care settings worldwide.