AI/ML-Based Triage Models Hold Promise in Improving ED Efficiency and Patient Outcomes
A systematic review of 26 studies in Intensive Critical Care Nursing evaluated AI/ML-driven triage and risk stratification models in EDs, focusing on predictive performance, key predictors, clinical and operational outcomes, and implementation challenges. 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. Reported benefits included reduced ED overcrowding, improved resource allocation, fewer mistriaged patients, and potential patient outcome improvements. Integrating AI and ML into ED triage can enhance assessment accuracy and resource allocation.