Smarter Hospitals: Machine Learning to Optimize Healthcare

The increasing challenges of healthcare systems demand innovative approaches to resource optimization, particularly for hospitals operating under economic and operational constraints. This study investigates the organizational and managerial factors influencing scale efficiency in 127 Italian hospitals, leveraging advanced machine learning (ML) techniques to identify key determinants of efficiency. A multi-level framework was developed, integrating Principal Component Analysis (PCA), Data Envelopment Analysis (DEA), and K-Means clustering to assess the interplay between energy costs, staff composition, and medical equipment across hospital levels. To enhance predictive capabilities, a classification model based on K-Nearest Neighbors (K-NN) was implemented, demonstrating high performance in distinguishing efficiency classes and confirming the importance of targeted resource management strategies. Additionally, the use of LIME (Local Interpretable Model-agnostic Explanations) provided actionable insights into the contribution of individual variables, enabling a deeper understanding of their impact on operational efficiency. In conclusion, this research highlights the importance of an integrated approach to support decision-makers in managing hospital resources, offering innovative tools to optimize efficiency and ensure the economic and operational sustainability of the healthcare system.