Statistical Analysis and Forecasts of Performance Indicators in the Romanian Healthcare System

Background/Objectives: Globally, healthcare systems face challenges in optimizing performance, particularly in the wake of the COVID-19 pandemic. This study focuses on the analysis and forecasting of key performance indicators (KPIs) for the County Emergency Clinical Hospital in Craiova, Romania. The study evaluates indicators such as average length of stay (ALoS), bed occupancy rate (BOR), number of cases (NC), case mix index (CMI), and average cost per hospitalization (ACH), providing insight into their dynamics and future trends. Methods: We performed statistical analyses on quarterly data from 2010 to 2023, employing descriptive statistics and stationarity tests (e.g., Dickey-Fuller), using ARIMA models to forecast each KPI, ensuring model validation through tests for autocorrelation, heteroscedasticity, and stationarity. The model selection prioritized Akaike and Schwarz criteria for robustness. Results: The findings reveal that ALoS and BOR demonstrate seasonality and are influenced by colder months, and it is expected that the ALoS will stabilize to around five days by 2025. Moreover, we predict that the BOR will range between 46 and 52%, reflecting these seasonal variations. The NC forecasts indicate a post-pandemic recovery but to below pre-pandemic levels, and we project the CMI to stabilize at around 1.54, suggesting a return to consistent case complexity. The ACH showed significant growth, particularly in the fourth quarter, driven by inflation and seasonal costs, and it is projected to reach more than RON 3000 by 2025. Conclusions: This study highlights the utility of ARIMA models in forecasting healthcare KPIs, enabling proactive resource planning and decision-making. The findings underscore the impact of seasonality and economic factors on hospital operations, offering valuable insights for improving efficiency and adapting to post-pandemic challenges.