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

• URL: http://arxiv.org/abs/2504.18578v1
• Date: Wed, 23 Apr 2025 00:23:13 GMT
• Title: An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study
• Authors: Orhun Vural, Bunyamin Ozaydin, Khalid Y. Aram, James Booth, Brittany F. Lindsey, Abdulaziz Ahmed,
• Abstract summary: This study develops machine learning models to predict Emergency Department (ED) waiting room occupancy at two time scales.<n>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.<n>These tools support staffing decisions and enable earlier interventions to reduce overcrowding.
• Score: 2.3933684192993177
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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