Forecasting hospital bed occupancy: a time series approach with prophet

Accurate hospital bed occupancy forecasting is essential for effective resource planning and patient flow management. While complex machine learning models have gained popularity in healthcare forecasting, their operational utility often falls short due to high maintenance costs and limited interpretability. This study evaluates the performance and practicality of Prophet, a parsimonious time-series model, for mid-term hospital bed occupancy forecasting.

We applied the Prophet model to daily bed occupancy data from the Medical Center - University of Freiburg (2010-2023), incorporating public holidays and a COVID-19 pandemic indicator as exogenous regressors. Prophet decomposes time series into trend, seasonality, and holiday effects, offering interpretable components. Forecast accuracy was assessed via rolling cross-validation over 2022-2023 for horizons of 30, 60, 90, and 180 days. A production-ready forecasting pipeline and dashboard were also implemented using cloud-native tools.

Prophet achieved low MAPE values across all horizons (3.21%-3.53%) with coverage above 80%, demonstrating reliable accuracy comparable to or better than more complex models that often require higher computational resources and operational costs, such as deep neural networks. Component analysis revealed patterns aligned with hospital operations; weekly and yearly cycles, and holiday effects, highlighting the model's interpretability.

This study shows that mid-term hospital bed occupancy can be accurately forecasted using a simple, interpretable model like Prophet. In contrast to more complex architectures, Prophet offers robust performance with minimal tuning, faster deployment, and clearer insights that are critical in operational settings. These findings reinforce the argument that, for structured forecasting tasks like bed occupancy, simple models can rival complex ones, not only in accuracy, but also in reproducibility, scalability, and operational value.