Forecasting Hospitalization Trends in the Greek Elderly Population.

Abstract

This study examines the forecasting of all-cause hospitalizations in the Greek elderly population until 2032, using historical data from 2001 to 2019. We employed two forecasting models: Autoregressive Integrated Moving Average (ARIMA) and Prophet model. The ARIMA model demonstrated a conservative approach, generating stable forecasts with narrower confidence intervals, making it suitable for identifying gradual trends. In contrast, the Prophet model, with its flexibility in trend capture, produced forecasts with broader confidence intervals, capturing potential sharp increases but with greater uncertainty. Our findings underscore that forecasting accuracy varies across age groups, with the highest precision observed in the 80+ age cohort, reflecting the more predictable healthcare utilization patterns of older populations. These insights emphasize the value of a multi-model approach in healthcare planning, particularly for accurately predicting trends within aging populations and efficiently allocating healthcare resources.