# Machine Learning for Evaluating Hospital Mobility: An Italian Case Study
## 5 Conclusions  
The research presented herein explored healthcare mobility within the Emilia-Romagna and Apulia regions, utilizing advanced machine learning methodologies to decipher the factors influencing patient choices regarding healthcare facilities based on the perception of service quality. Results derived from logistic regression techniques and data envelopment analysis (DEA) provide a nuanced portrait of how geographic distribution and operational efficiency of healthcare facilities impact patient mobility. Perceived quality, which includes variables such as mortality rates and medical staff availability, was identified as the dominant factor in patient choice, suggesting that improvements in hospital performance could attract more patients, thus more evenly distributing workload and enhancing access to care. Specifically, hospitals with lower mortality rates and higher availability of medical professionals were associated with increased patient mobility, indicating that patients are willing to travel greater distances for higher-quality care.  
Machine learning techniques, including logistic regression for mobility prediction and the k-means algorithm for cluster analysis, facilitated an effective and detailed segmentation of mobility based on travel distances, enabling the identification of specific patient behavior patterns. These methodologies have not only confirmed the importance of clinical and structural variables in hospital choice but also highlighted how quality perceptions significantly influence such decisions. The adoption of advanced predictive models and DEA analysis allowed for a rigorous comparison of efficiency among healthcare facilities, revealing that private institutions tend to exhibit higher efficiency than public ones. These findings suggest that efficient management practices, commonly observed in the private sector, could be successfully implemented in the public sector to elevate the overall quality and efficiency of healthcare services.  
In conclusion, this study demonstrates how the integration of predictive technologies and analytical techniques can provide valuable insights for optimal public health management and evidence-based policy formulation. The implications of these findings are extensive and underscore a clear path toward enhancing healthcare planning and adopting advanced management strategies to overcome regional disparities and improve the effectiveness of the Italian healthcare system. These results emphasize the need for further research to explore the applicability of these techniques in other regions and contexts, to generalize and expand the effectiveness of the proposed solutions.  
Conceptualization, V.S., C.T., N.F., M.I., J.P. and A.M.; methodology, V.S., N.F., J.P. and A.M.; data curation, V.S., C.T., N.F., M.I., J.P. and A.M.; writing--original draft preparation, V.S. and A.M.; writing--review and editing, V.S., C.T., N.F., M.I., J.P. and A.M.; visualization, V.S. and A.M.; supervision, V.S., C.T., N.F., M.I., J.P. and A.M.; project administration, A.M.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.