Predictive modeling of hospital emergency department demand using artificial intelligence: A systematic review

Accurately forecasting patient arrivals in hospital emergency departments (EDs) is critical for hospital capacity and planning and clinical decision-making. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has shown promising performance over traditional time series approaches. However, the extent to which these models are validated and generalizable remains uncertain.

To systematically review the literature on predictive models for hospital ED demand forecasting, focusing on algorithms used, internal and external variables, validation strategies and limitations pre- and post-pandemic developments.

A systematic literature review (SLR) was conducted following PRISMA guidelines. Five databases (PubMed, IEEE, Springer, ScienceDirect, ACM) were searched for peer-reviewed articles published between January 2019 and July 2025. Eligible studies applied predictive algorithms - excluding those focused on COVID-19 - to forecast ED visits. Extracted data included modeling approaches, feature types, evaluation metrics, and validation methods.

Eleven studies met the inclusion criteria. Classical models such as ARIMA and SARIMA remain in use, but ML (e.g., XGBoost, Random Forest) and DL (e.g., LSTM, CNN) showed higher predictive accuracy, especially with high-dimensional, nonlinear data. Incorporating external variables-such as weather (temperature, humidity, wind), air quality, and calendar events-consistently improved performance. Common metrics included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), with MAPE ranging from 3 % to 18 %. Few studies performed external validation, and only a minority employed explainable AI methods (e.g., SHAP) to address interpretability.

AI-based models offer strong potential for ED demand forecasting, particularly when integrating environmental and temporal features. However, limited external validation and lack of interpretability remain significant barriers to clinical adoption. Future research should prioritize multicenter validation, standardized evaluation, and explainable AI to support reliable, transparent, and scalable use in hospital emergency departments.