Trasformazione Digitale e Apprendimento Automatico Applicati alla Sanità Pubblica

Abstract: For over two decades, the Italian healthcare system has been facing increasingly complex challenges that compromise its long-term sustainability. Among the most significant issues are persistent cuts to public funding; the progressive demographic shift towards an aging population and the consequent increase in chronic diseases; and a significant delay in digital transformation of public infrastructures. These factors collectively contribute to increasing the pressure on the National Health System (SSN), limiting its capacity to respond adequately and effectively to the growing and diverse needs of users. This dissertation aims to explore and analyze the application of Computer Vision (CV) and Machine Learning (ML) within the context of Italian public healthcare. Through a series of case studies and practical applications implemented in various healthcare settings nationwide, the present work demonstrates the innovative potential of these enabling technologies in enhancing operational efficiency, augmenting user-perceived quality, and optimizing resource management. The first case study examines the application of CV in the automated recognition of nasopharyngeal swabs used in COVID-19 self-testing. This research demonstrates how the implementation of an automatic outcome classification system, integrated with the Electronic Health Record (FSE), can contribute not only to tracking new infections and containing viral spread but also to alleviating the workload of healthcare and administrative personnel during critical phases of a pandemic. Subsequently, the application of ML techniques for predicting the Length of Stay (LOS) in Emergency Departments is investigated. Through the analysis of data collected during the triage phase, the predictive models developed in this study facilitate more effective management of patient flow in emergency care departments, enabling the implementation of targeted and proactive interventions. A further application of ML that has been explored involves the prediction of LOS in hospital wards. The implemented models exhibit notable accuracy in estimating the duration of patient hospitalization, favouring the timely identification of potential “bed blockers”, more precise scheduling of discharges and admissions, and optimal use of available beds. Finally, a distributed variant of a bed management system, designated as Distributed Electronic Bed Management System (DEBMS), is presented. The system, specifically designed for Bed Managers and Hospital Administrations, collects and aggregates information from primary hospital management flows, processing them through predictive models to provide valuable insights that support operational decision-making. The DEBMS facilitates real-time sharing of statistics among nearby hospitals, and enables distributed management of patient admission requests, fostering more effective synergy among hospital structures at the provincial level and improving coordination and response capacity to healthcare needs. Digital transformation represents a strategic opportunity to reform healthcare systems, modernize their infrastructures, and enhance their resilience in the face of recent challenges, such as the 2020 pandemic, and future exigencies. Through the analysis of concrete cases and the discussion of related practical implications, this work offers an important contribution to the debate on how the judicious adoption of Artificial Intelligence (AI) in Italian healthcare settings can lead to substantial evolutions in the current landscape, while maintaining an unwavering focus on the quality of care provided and the sustainability of a public healthcare system internationally recognized for its excellence.

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PERLITI SCORZONI, Paolo "Trasformazione Digitale e Apprendimento Automatico Applicati alla Sanità Pubblica" 2025

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