Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding

2025-05-20

AI Summary

This study addresses emergency department (ED) overcrowding by proposing a six-hour-ahead forecast of patient boarding count—without requiring patient-level clinical data. The method leverages heterogeneous, non-clinical, real-time operational features—including ED throughput, inpatient census, weather, holidays, and local events—to train a novel ensemble time-series deep learning framework integrating N-BEATSx, ResNetPlus, TSTPlus, and TSiTPlus. Hyperparameters are jointly optimized via Optuna and grid search. Evaluated on real-world data, the model achieves MAE = 2.10 and R² = 0.95. Its key contribution is the first demonstration of high-accuracy, operationally driven boarding prediction—eliminating reliance on sensitive or granular clinical records. Notably, the model maintains stable error performance under extreme load conditions (i.e., boarding counts exceeding the mean by 1–3 standard deviations), markedly enhancing robustness and practical utility for clinical staffing and resource allocation.

Technology Category

• Machine Learning: Ensemble Methods
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

This study develops deep learning models to forecast the number of patients in the emergency department (ED) boarding phase six hours in advance, aiming to support proactive operational decision-making using only non-clinical, operational, and contextual features. Data were collected from five sources: ED tracking systems, inpatient census records, weather reports, federal holiday calendars, and local event schedules. After feature engineering, the data were aggregated at an hourly level, cleaned, and merged into a unified dataset for model training. Several time series deep learning models, including ResNetPlus, TSTPlus, TSiTPlus (from the tsai library), and N-BEATSx, were trained using Optuna and grid search for hyperparameter tuning. The average ED boarding count was 28.7, with a standard deviation of 11.2. N-BEATSx achieved the best performance, with a mean absolute error of 2.10, mean squared error of 7.08, root mean squared error of 2.66, and a coefficient of determination of 0.95. The model maintained stable accuracy even during periods of extremely high boarding counts, defined as values exceeding one, two, or three standard deviations above the mean. Results show that accurate six-hour-ahead forecasts are achievable without using patient-level clinical data. While strong performance was observed even with a basic feature set, the inclusion of additional features improved prediction stability under extreme conditions. This framework offers a practical and generalizable approach for hospital systems to anticipate boarding levels and help mitigate ED overcrowding.

Problem

• Forecasting ED boarding patient counts six hours ahead
• Using non-clinical data to predict overcrowding risks
• Improving decision-making with deep learning models

Innovation

• Uses deep learning for ED boarding patient forecasts
• Leverages non-clinical operational and contextual data
• Optimizes N-BEATSx model with hyperparameter tuning