The role of AI in emergency department triage: An integrative systematic review

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Detalles Bibliográficos
Publicado en:	Intensive & Critical Care Nursing vol. 89 (Aug 2025)
Autor principal:	Rabie Adel El Arab
Otros Autores:	Omayma Abdulaziz Al Moosa
Publicado:	Elsevier Limited
Materias:	Emergency medical care Accuracy Clinical training Mortality Risk assessment Nurses Emergency services Multicenter studies Health care industry Stratification Clinical outcomes Business metrics Overcrowding Infrastructure Artificial intelligence Patients Electronic health records Delayed Resource allocation Systematic review Clinical medicine Clinical decision making Intensive care Decision making Triage High risk Design Interdisciplinary aspects Critical care Hospitalization Clinical assessment Forests Ethics Medical records Machine learning Data quality Benefits Data processing Health services Treatment methods Medical decision making Natural language processing
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Descripción
Resumen:	Background Overcrowding in emergency departments (EDs) leads to delayed treatments, poor patient outcomes, and increased staff workloads. Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools to optimize triage. Objective: This systematic review evaluates AI/ML-driven triage and risk stratification models in EDs, focusing on predictive performance, key predictors, clinical and operational outcomes, and implementation challenges. Methods Following PRISMA 2020 guidelines, we systematically searched PubMed, CINAHL, Scopus, Web of Science, and IEEE Xplore for studies on AI/ML-driven ED triage published through January 2025. Two independent reviewers screened studies, extracted data, and assessed quality using PROBAST, with findings synthesized thematically. Results Twenty-six studies met inclusion criteria. ML-based triage models consistently outperformed traditional tools, often achieving AUCs > 0.80 for high acuity outcomes (e.g., hospital admission, ICU transfer). Key predictors included vital signs, age, arrival mode, and disease-specific markers. Incorporating free-text data via natural language processing enhances accuracy and sensitivity. Advanced ML techniques, such as gradient boosting and random forests, generally surpassed simpler models across diverse populations. Reported benefits included reduced ED overcrowding, improved resource allocation, fewer mis-triaged patients, and potential patient outcome improvements. Conclusion AI/ML-based triage models hold substantial promise in improving ED efficiency and patient outcomes. Prospective, multi-center trials with transparent reporting and seamless electronic health record integration are essential to confirm these benefits. Implications for Clinical Practice Integrating AI and ML into ED triage can enhance assessment accuracy and resource allocation. Early identification of high-risk patients supports better clinical decision-making, including critical care and ICU nurses, by streamlining patient transitions and reducing overcrowding. Explainable AI models foster trust and enable informed decisions under pressure. To realize these benefits, healthcare organizations must invest in robust infrastructure, provide comprehensive training for all clinical staff, and implement ethical, standardized practices that support interdisciplinary collaboration between ED and ICU teams.
ISSN:	0964-3397 1532-4036 0266-612X
DOI:	10.1016/j.iccn.2025.104058
Fuente:	Sociology Database