El Arab Rabie Adel, Al Moosa Omayma Abdulaziz
Almoosa College of Health Sciences, Alhsa, Saudi Arabia.
Almoosa College of Health Sciences, Alhsa, Saudi Arabia.
Intensive Crit Care Nurs. 2025 Apr 29;89:104058. doi: 10.1016/j.iccn.2025.104058.
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.
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.
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.
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.
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.
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.
急诊科过度拥挤会导致治疗延误、患者预后不良以及工作人员工作量增加。人工智能（AI）和机器学习（ML）已成为优化分诊的有前景的工具。
本系统评价评估急诊科中基于AI/ML的分诊和风险分层模型，重点关注预测性能、关键预测因素、临床和运营结果以及实施挑战。
遵循PRISMA 2020指南，我们系统检索了PubMed、CINAHL、Scopus、Web of Science和IEEE Xplore，以查找截至2025年1月发表的关于基于AI/ML的急诊科分诊的研究。两名独立评审员筛选研究、提取数据并使用PROBAST评估质量，研究结果进行主题综合分析。
26项研究符合纳入标准。基于ML的分诊模型始终优于传统工具，对于高 acuity 结局（如住院、转入重症监护病房），其AUC通常>0.80。关键预测因素包括生命体征、年龄（此处原文“age arrival mode”有误，推测应为“age”）、到达方式和疾病特异性标志物。通过自然语言处理纳入自由文本数据可提高准确性和敏感性。先进的ML技术，如梯度提升和随机森林，在不同人群中通常优于更简单的模型。报告的益处包括减少急诊科过度拥挤、改善资源分配、减少分诊错误的患者以及可能改善患者结局。
基于AI/ML的分诊模型在提高急诊科效率和患者结局方面具有巨大潜力。进行透明报告并实现无缝电子健康记录整合的前瞻性多中心试验对于证实这些益处至关重要。
将AI和ML整合到急诊科分诊中可提高评估准确性和资源分配。通过简化患者转接和减少过度拥挤，早期识别高危患者有助于包括重症监护和重症监护病房护士在内的更好的临床决策。可解释的AI模型可增强信任并在压力下做出明智决策。为实现这些益处，医疗保健组织必须投资强大的基础设施，为所有临床工作人员提供全面培训，并实施支持急诊科和重症监护病房团队之间跨学科协作的道德、标准化做法。
