The Impact of Artificial Intelligence on Nursing Triage: A Comprehensive Review of Accuracy, Efficiency, and Clinical Decision Support in Emergency Care

Background: The rapid expansion of artificial intelligence (AI) in healthcare is reshaping clinical decision-making by improving diagnostic accuracy, standardizing assessments, and enhancing workflow efficiency. In emergency departments, where triage determines the urgency and safety of care, AI offers important support for nurses managing high patient volumes and complex presentations. This review synthesizes current evidence on the impact of AI on nursing triage. Methods: A comprehensive literature search was conducted across PubMed, Google Scholar, ScienceDirect, CINAHL, Elsevier, and BMC Nursing Journals for studies published between ۲۰۱۸ and ۲۰۲۵. Of approximately ۵۰۰ retrieved records, ۴۰ studies met inclusion criteria after screening for relevance, duplication, and language. Results: Across the included studies, AI-based triage systems consistently outperformed traditional nurse-led or rule-based approaches. Machine learning and deep learning models showed higher accuracy in predicting patient acuity, identifying high-risk cases earlier, and reducing both under-triage and over-triage. AI also improved prediction of hospitalization and critical outcomes, with several models demonstrating strong discrimination metrics. Additionally, multiple studies reported reduced triage time, improved workflow efficiency, and decreased cognitive burden for nurses, contributing to better patient flow and more consistent clinical decision-making. Conclusion: AI represents a valuable adjunct to contemporary nursing triage, offering measurable gains in accuracy, efficiency, and clinical reliability. Ongoing multi-center prospective studies are essential to ensure safe and equitable integration into emergency nursing practice.

کلیدواژه ها:

Artificial Intelligence ، Nursing Triage ، Machine Learning ، Emergency Care ، Clinical Decision Making