Development of a Financial Forecasting System for the Healthcare Sector

Healthcare expenditures constitute a significant portion of national budgets in many countries, and striking a balance between cost-effectiveness and service quality is becoming increasingly important. In this context, financial analyses play a critical role in strategic decision-making processes for healthcare organizations. In particular, forecasting hospital turnover guides in many areas, such as budgeting, resource management, and personnel planning. This study aims to develop revenue forecasting models using the hospital’s historical turnover data to gain a better understanding of its financial status and to provide a more solid foundation for future financial decision-making in the healthcare sector. The present work reports a comparative time-series analysis of deep learning techniques (Recurrent Neural Networks with the Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM) cells, and Neural Basis Expansion Analysis Time Series (N-Beats)) and statistical techniques (ARIMA and SARIMA) and the Naïve algorithm to forecast revenue using the hospital’s historical turnover data. The findings reveal that the SARIMA model provides high accuracy by successfully analyzing seasonal and trend components. In contrast, the N-BEATS model exhibits the best overall performance with the lowest error rate, highlighting its strong ability to capture complex temporal dependencies and outperforming both traditional recurrent architectures and conventional statistical methods. The models developed as a result of the study are evaluated as contributing to the financial planning processes of healthcare organizations and supporting strategic decision-making mechanisms.