Forecasting hospital resource demand using time series and machine learning models

Abstract

Efficient allocation of hospital resources is essential for ensuring timely and equitable healthcare delivery, particularly during periods of fluctuating patient demand such as pandemics, seasonal disease outbreaks, or disaster scenarios. This study presents a hybrid approach combining time series analysis and machine learning models to forecast hospital resource demand, including bed occupancy, ICU capacity, staffing requirements, and medical supplies. By integrating historical admission data, disease incidence trends, demographic information, and external factors such as weather and public health interventions, the model enables healthcare administrators to anticipate resource needs with greater precision. The forecasting framework employs autoregressive integrated moving average (ARIMA) models to capture temporal patterns, seasonality, and autocorrelation in hospital usage data. In parallel, machine learning algorithms such as Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) neural networks are used to model complex, nonlinear relationships and exogenous variable impacts. The ensemble method leverages the strengths of both statistical and machine learning approaches, enhancing forecast robustness and adaptability. The model is trained and validated using real-world datasets from national health services and regional hospitals, spanning both normal and surge conditions. Evaluation metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used to assess performance across different time horizons and resource types. Results show that the hybrid model significantly outperforms traditional single-method approaches in terms of forecast accuracy and responsiveness to sudden demand changes. This research provides a decision-support tool for proactive hospital resource management, facilitating dynamic planning and improving preparedness during crises. The model’s ability to generate interpretable and timely forecasts can assist hospital administrators, policymakers, and emergency response teams in optimizing staffing schedules, managing inventory, and minimizing care delays. The study advocates for the integration of advanced predictive analytics into hospital operations as a pathway to more resilient and data-driven healthcare systems.

Keywords: Hospital Resource Forecasting, Time Series Analysis, Machine Learning, ARIMA, LSTM, Random Forest, Healthcare Planning, Surge Capacity, Predictive Analytics, Hospital Operations.