In Tuscany, Italy, New Delhi metallo-beta-lactamase-producing carbapenem-resistant Enterobacterales (NDM-CRE) have increased since November 2018. Between November 2018 and October 2019, 1,645 samples were NDM-CRE-positive: 1,270 (77.2%) cases of intestinal carriage, 129 (7.8%) bloodstream infections and 246 (14.9%) infections/colonisations at other sites. Klebsiella pneumoniae were prevalent (1,495; 90.9%), with ST147/NDM-1 the dominant clone. Delayed outbreak identification and response resulted in sustained NDM-CRE transmission in the North-West area of Tuscany, but successfully contained spread within the region.

We describe a nosocomial outbreak of measles that occurred in an Italian hospital during the first months of 2017, involving 35 persons and including healthcare workers, support personnel working in the hospital, visitors and community contacts. Late diagnosis of the first case, support personnel not being promptly recognised as hospital workers and diffusion of the infection in the emergency department had a major role in sustaining this outbreak.

We describe the epidemiology of New Delhi Metallo-Beta-Lactamase-Producing Carbapenem-Resistant Enterobacterales (NDM-CRE) colonization/infection in a cohort of COVID-19 patients in an Italian teaching hospital. These patients had an increased risk of NDM-CRE acquisition versus the usual patients (75.9 vs. 25.3 cases/10,000 patient days). The co-infection significantly increased the duration of hospital stay (32.9 vs. 15.8 days).

Healthcare-associated infections (HAIs) pose significant challenges in healthcare systems, with preventable surveillance playing a crucial role. Traditional surveillance, although effective, is resource-intensive. The development of new technologies, such as artificial intelligence (AI), can support traditional surveillance in analysing an increasing amount of health data or meeting patient needs. We conducted a scoping review, following the PRISMA-ScR guideline, searching for studies of new digital technologies applied to the surveillance, control, and prevention of HAIs in hospitals and LTCFs published from 2018 to 4 November 2023. The literature search yielded 1292 articles. After title/abstract screening and full-text screening, 43 articles were included. The mean study duration was 43.7 months. Surgical site infections (SSIs) were the most-investigated HAI and machine learning was the most-applied technology. Three main themes emerged from the thematic analysis: patient empowerment, workload reduction and cost reduction, and improved sensitivity and personalization. Comparative analysis between new technologies and traditional methods showed different population types, with machine learning methods examining larger populations for AI algorithm training. While digital tools show promise in HAI surveillance, especially for SSIs, challenges persist in resource distribution and interdisciplinary integration in healthcare settings, highlighting the need for ongoing development and implementation strategies.