Emanuela Foglia

Healthcare systems are challenged to balance high-quality care with growing demands for green transition. Within the Italian context, PNRR encourages targeted investments and reforms promoting sustainability, resilience, and innovation in healthcare, while hospitals encounter barriers such as regulatory constraints, cultural resistance, and limited resources. This paper investigates how Italian hospitals are integrating environmental sustainability practices in response to increasing institutional pressures on environmental issues and green objectives achievement. Adopting a qualitative multiple case study approach, the paper examines four healthcare organizations varying in size, location, and sustainability degree. Results identify three organizational profiles: pioneers, regulatory responders and fragmented adopters. Sustainable practices mainly involve energy efficiency, waste management, and green mobility, but their implementation is influenced by internal resources, cross-functional competencies, and external incentives. The preliminary results show that sustainability in healthcare requires more than technological solutions, demanding organizational adaptation, skill development, and institutional alignment supporting a systemic and long-term transformation.

Of the drug distribution models implemented in the Italian National Healthcare Service provided to guarantee the administration of medication, the drug distribution performed through the hospital channel is an operative strategy that allows for savings in the public expenditure, but often creates higher social costs for patients and caregivers. This distribution model leads to high access to hospitals which, during pandemics, amplifies the risk of contagion, making these healthcare facilities a place where epidemics could spread and negatively affect high-risk patients. Considering their extensive local presence, primary care services and community pharmacies could play an active role to reach patients and ensure the proper distribution of drugs. Based on the differences in these two distribution models, a prescriptive tool could provide suggestions for the institutional decision-making process. When performed by different stakeholders (i.e., policy makers, health authorities or agencies), it could define which drugs should be distributed by primary care pharmacies for the treatment of chronic diseases and provide an answer to critical issues in case of future pandemic situations and healthcare emergencies. Prescriptive data analyses are known as the best methods for formulating prescriptions in the distribution field and constrained optimization sets the values of decision variables to achieve specific objectives, such as a reduction in the number of visitors to the hospital setting. Grounded on previous research in this field, the present study proposes a decision support tool based on a constrained optimization model, establishing which drugs currently dispensed by hospital pharmacies should be distributed by primary care pharmacies. This approach allows for limiting crowding and balances the distribution costs to guarantee equal access to care for patients. The model structure and the possible decision-making outputs reached by applying the prescriptive tool are discussed and the “what-if” analysis is used to ensure the robustness of the simulation approach.

Background and importance: Automation of hospital medication management demonstrated advantages to wards manual systems, especially in error reduction, improving patient safety and ensuring drugs’ traceability. Despite the existence of literature on benefits, no multidimensional evidence on automation of hospital medication management is available. Aim and objectives: The study aimed to demonstrate the value of four scenarios of automated technologies’ introduction, with a comprehensive health technology assessment (HTA) approach, comparing: (1) manual dispensing, (2) presence of only centralised automated systems in the hospital pharmacy, (3) presence of only decentralised automated systems in the wards and (4) integration of scenarios 2 and 3 into a full solution, with electronic prescription. Material and methods: The HTA involved 50 healthcare professionals (pharmacists, nurses, decision-makers and other professionals) in four European countries in 2021. After a structured literature review, the nine domains of the EunetHTA Core Model were deployed using validated questionnaires (with a seven-item Likert scale). Differences among groups and scenarios were studied by ANOVA test. All analyses were conducted considering a level of significance equal to 0.05 and were performed using IBM SPSS software (Version 22.0). Results: Results from the efficacy and safety questionnaires showed that the presence of automation resulted in a decrease in dispensing errors (1.75, 1.20, 1.88, 2.19, respectively, for scenarios 1, 2, 3, 4; p value = 0.000) and consequently in adverse events (–2.13, 1.18, 1.71, 2.46, respectively, for scenarios 1, 2, 3, 4; p value = 0.000), especially if associated with electronic prescribing, confirming the literature findings. A low organisational impact of automation was registered (–0.71, 0.50, 0.49, 0.63, respectively, for scenarios 1, 2, 3, 4) due to a trade-off between technological change efforts and efficiency beneficial effects in the first year. Ethical and social dimension results demonstrated a positive impact of automation (–0.93, 0.72, 1.03, 1.23, respectively for scenarios 1, 2, 3, 4; p value = 0.000) on patients’ perceived quality of life. The impact on drugs thefts and the identification of responsibility in cases of legal controversies were the most appreciated legal items. Conclusion and relevance: In a literature dominated by safety evidence on automated solutions, a complete HTA approach demonstrates its validity in communicating and demonstrating multidimensional and multidisciplinary values of hospital automated dispensing solutions.

Within the healthcare setting, knowledge management would result in better addressing the patients’ needs, enabling greater efficiency in diagnosis and research activities, and improving the decision-making process. One of the main assets that could be used to ameliorate delivery of services, quality, and efficiency performance, through an analysis of data and evidence (Alsunaidi et al, 2021), is represented by use of big data, generated at hospital level, that if accordingly handled, could support the management of complex situations. The success of big data in healthcare sector, depends also on the skills, competencies and expertise of the professionals that are demanded to acquire, manage, process data and extract knowledge from them, known as Data Scientists. Due to the current lack of a dashboard of specific skills and competencies devoted to such innovative professional role, the present paper aims to structure and validate a shared framework required to cover this role in the healthcare sector, based on the scientific literature on the topic and specific needs directly derived from real practice. For the achievement of the above objective a multi-methods approach was used, with the development of a 3-stage study design. At first, an in-depth examination of potential Data Scientist skills and competencies was conducted, by reviewing literature evidence, university undergraduate and graduate international courses, and examining job postings (Stage 1). Secondly, a synthesis of the main skills and competencies was produced, with the creation of a structured framework (Stage 2). In conclusion, a specific survey was conducted by involving 125 healthcare professionals, to gather their perceptions on the relative importance of the specific skills and competencies collected in Stage 1 and in Stage 2, thus defining the main assets leveraging on the proper introduction of a Data Scientist in any healthcare organization (Stage 3). The proposed framework identified three main categories of skills and competencies (i.e. Technical ones, Business & workplace-related, Soft and other personal skills) and related items of interest. This study represents a first attempt to rationalize the fragmented body of knowledge deriving from different stream of literature on the Data Scientist topic, focusing on the healthcare sector, validating their importance and related prioritization, thus offering theoretical and practical implications.

Background: Thoracoabdominal aortic aneurysms (TAAAs) are defined as those aortic aneurysms involving renovisceral arteries. They account for around 10% of aortic aneurysms, and their treatment is burdened by considerable mortality and morbidity. Open surgical repair has been practised as the standard of care since the 1950s. In 2001 endovascular repair was introduced to reduce treatment invasiveness, and the technology is still evolving. The potential benefits of endovascular repair over open surgery should be carefully weighed in a multidimensional framework. Methods: We applied the Health Technology Assessment (HTA) framework (EUnetHTA core model with 9 dimensions) to conduct a report comparing the two technologies. A multidisciplinary working group was established. We derived and pooled: i) secondary data derived from systematic literature reviews, and ii) original data from IRCCS San Raffaele Hospital, Milan, a national referral centre for TAAA (qualitative and economic data). Results: Endovascular repair resulted superior to the traditional open surgery in terms of efficacy and safety, as justified by the meta-analysis we performed. Despite the higher costs, a significant impact on budget and slightly lower cost-effectiveness, the endovascular protheses’ adoption could provide conspicuous benefits in terms of social and ethical dimensions without affecting long-term organisational and legal aspects. Conclusions: The multi-criteria decision analysis carried out from a hospital point of view shows that there is no significant difference (final score endovascular repair 0.68 vs open surgery 0.63) between the two procedures considering all the dimensions. Still, the endovascular repair is slightly superior to the open surgery in terms of safety, effectiveness, social, ethical, legal, and organisational impact. From the policy maker’s point of view, technologies with a score superior to 0.6 are equally valuable, and the final decision should be left to the clinicians. Key messages: Further research is needed to compare endovascular prostheses and open surgery’s long-term population benefits, balancing clinical, economic, organisational and patient-related outcomes. HTA methodology offers substantial support to compare in-use technologies, informing clinicians’ and decision-makers’ choices to strengthen healthcare provision equity and preparedness.

In this paper we propose a fuzzy logic-based approach to analyze UK National Health Service (NHS) public administrative data related to pre-and post-pandemic claims filed by patients, analyzing the legal and ethical issues connected to the use of Artificial Intelligence systems, including our own, to take critical decisions having a significant impact on patients, such as employing computational intelligence to justify the management choices related to Intensive Care Unit (ICU) bed allocation. Differently from previous papers, in this work we follow an unsupervised approach and, specifically, we perform an analysis of UK hospitals by means of a computational intelligence algorithm integrating Fuzzy C-Means and swarm intelligence. The dataset that we analyse allows us to compare pre-and post-pandemic data, to analyze the ethical and legal challenges of the use of computational intelligence for critical decision-making in the health care field.