Forecasting plays a crucial role in enhancing the efficiency and effectiveness of logistics and supply chain management in the healthcare sector, particularly in financial management within healthcare facilities. Modeling and forecasting techniques serve as valuable tools in this domain, with Artificial Neural Networks (ANN), especially Deep Neural Networks (DNN), emerging as promising options, as indicated by the scientific literature. Furthermore, combining ANN with other methodologies has been a subject of frequent discussion. This study builds on previous research that used historical data to predict expenditure on medicines in Portuguese NHS Hospitals. The focus of this study is to evaluate advantages of Deep Learning methodologies. In addition to traditional approaches as Exponential Smoothing (ES), hybrid models are explored, specifically the combination of DNN with the Box and Jenkins methodology (BJ-DNN). A comparative analysis is conducted to assess the predictive quality and computational cost associated with the forecast models. The findings reveal that ES models provide overall suitability and low computational cost. However, considering the Mean Absolute Percentage Error (MAPE), the BJ-DNN model demonstrates robust forecasting accuracy. In conclusion, this study highlights the potential of Deep Learning methodologies for improving expenditure forecasting in healthcare, while maintaining the favorable attributes of traditional Exponential Smoothing models. These findings contribute to the broader understanding of forecasting techniques in the healthcare sector.

This work is partially financed by national funds through FCT – Fundação para a Ciência e a Tecnologia under the project UIDB/00006/2020. https://doi.org/10.54499/ UIDB/00006/2020.

BJ-DNN model,Deep learning,ES models,Forecasting,National health service,Prediction error,Time series

• Mathematics - Natural Sciences
• Computer and Information Sciences - Natural Sciences
• Other Medical Sciences - Medical and Health Sciences
• Economics and Business - Social Sciences