An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study

2025-04-23

AI Summary

Emergency department (ED) overcrowding causes care delays and operational strain, with current management strategies largely reactive. This study proposes a dual-timescale AI forecasting framework: an hourly-scale (6-hour) model, TSiTPlus, and a daily-scale (24-hour) model, XCMPlus—both leveraging a novel multi-source feature engineering approach that jointly incorporates internal patient flow dynamics and external environmental factors. In benchmarking against 11 state-of-the-art algorithms, TSiTPlus achieves the best hourly performance (MAE = 4.19), while XCMPlus attains optimal daily accuracy (MAE = 2.00). Notably, both models maintain robustness under extreme congestion conditions (+3σ), with MAE < 15.6—significantly outperforming all baselines. The framework enables proactive staffing allocation and early intervention, offering a practical, deployable technical pathway for intelligent ED operations management.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Machine Learning: Learning on the Edge & Model Compression

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Background: Emergency department (ED) overcrowding remains a major challenge, causing delays in care and increased operational strain. Hospital management often reacts to congestion after it occurs. Machine learning predictive modeling offers a proactive approach by forecasting patient flow metrics, such as waiting count, to improve resource planning and hospital efficiency. Objective: This study develops machine learning models to predict ED waiting room occupancy at two time scales. The hourly model forecasts the waiting count six hours ahead (e.g., a 1 PM prediction for 7 PM), while the daily model estimates the average waiting count for the next 24 hours (e.g., a 5 PM prediction for the following day's average). These tools support staffing decisions and enable earlier interventions to reduce overcrowding. Methods: Data from a partner hospital's ED in the southeastern United States were used, integrating internal metrics and external features. Eleven machine learning algorithms, including traditional and deep learning models, were trained and evaluated. Feature combinations were optimized, and performance was assessed across varying patient volumes and hours. Results: TSiTPlus achieved the best hourly prediction (MAE: 4.19, MSE: 29.32). The mean hourly waiting count was 18.11, with a standard deviation of 9.77. Accuracy varied by hour, with MAEs ranging from 2.45 (11 PM) to 5.45 (8 PM). Extreme case analysis at one, two, and three standard deviations above the mean showed MAEs of 6.16, 10.16, and 15.59, respectively. For daily predictions, XCMPlus performed best (MAE: 2.00, MSE: 6.64), with a daily mean of 18.11 and standard deviation of 4.51. Conclusions: These models accurately forecast ED waiting room occupancy and support proactive resource allocation. Their implementation has the potential to improve patient flow and reduce overcrowding in emergency care settings.

Problem

• Predicting emergency department overcrowding using AI
• Forecasting patient flow metrics for resource planning
• Improving staffing decisions with machine learning models

Innovation

• Machine learning predicts emergency department overcrowding
• Hourly and daily forecasting models for patient flow
• Optimized feature combinations enhance prediction accuracy