Implementation of machine learning in emergency departments: A systematic review

Abstract

Objectives: This systematic review aims to evaluate studies that implemented and evaluated machine learning models in emergency department settings, focusing on their clinical and operational impact. Methods: A comprehensive search was conducted across multiple databases from inception to January 2024. Studies were eligible if they assessed the implementation of machine learning models in emergency departments, with a particular focus on clinical and operational impact. Results: A total of 84 studies met the inclusion criteria. Gradient boosting and neural networks were the most frequently used models. Mortality prediction models achieved AUC values ranging from 0.618 to 0.978, with key predictors including age, sex, race, vital signs, and comorbidities. Disposition prediction models showed AUC values of 0.675–0.96, often incorporating age, sex, vital signs, triage data, and past medical history. Length of stay prediction studies identified demographic data, triage level, chief complaints, and comorbidities as significant predictors, with gradient boosting models yielding the highest predictive accuracy. Machine learning-based treatment decision models showed promise in sepsis detection and cardiovascular triage. Wait time prediction models using gradient boosting decreased patient wait times by 18%–26%. Emergency department cost prediction studies were limited, with logistic regression models achieving AUCs of 0.71–0.76 for identifying high-cost patients. Conclusion: Machine learning is widely used in emergency department research, but issues with generalizability and workflow integration limit its clinical use. Future work should improve data quality, representation, and ongoing model validation to enhance real-world utility.