Antimicrobial resistance (AMR) has become one of the most critical public health threats worldwide. The inappropriate or excessive use of antibiotics creates exerts pressure that accelerates the emergence and dissemination of resistant organisms, particularly in healthcare environments where antibiotic exposure is high. The hospital setting concentrates vulnerable patients, invasive procedures, and complex care, creating favourable conditions for healthcare-associated infections (HAIs). Antimicrobial Stewardship (AMS) programs and Infection Prevention and Control (IPC) strategies are essential to combating AMR and reducing HAI. These fields are deeply interconnected: optimizing antibiotic use decreases AMR and Clostridioides difficile infections, while high-quality HAI surveillance allows targeted stewardship interventions and prevention measures. Both domains still rely on manual processes, which are resource-intensive, limited in scope, delayed and have limited accuracy. Digital tools could help to overcome these challenges by automating HAI surveillance and AMS interventions.

In this Privat-Docent thesis, I present a selection of my publications on research work conducted over the past five years in the field of AMS and IPC digitalization. It includes research on computerized tools to support antimicrobial prescribing, with a qualitative study exploring barriers and facilitators to adopting computerized decision support systems (CDSS) for antibiotic prescribing among physicians; the design, implementation, and evaluation of a CDSS deployed across three Swiss hospital networks and assessed in a cluster-randomized trial involving 24 wards; and the implementation of an AMS smartphone application and its evaluation in a cluster-randomized trial across 32 clusters in three European countries. Research work on automated surveillance of HAI includes a systematic review and meta-analysis of the predictive performance of automated surveillance algorithms for intravascular catheter-related bloodstream infections and the development and internal validation of a fully automated system for surveillance of central line–associated bloodstream infections (CLABSI). Key challenges faced by both domains are discussed, including clinical adoption, technical complexity, and organizational barriers. Perspectives for future research are outlined, with particular emphasis on the evolving role of artificial intelligence (AI).