Abstract REACT

Nosocomial infections, often caused by Antibiotic-resistant bacteria, pose a heavy healthcare and economic burden worldwide. We propose an actionable genomic epidemiology platform across 3 hospitals in Lombardy to monitor antimicrobial-resistant Klebsiella pneumoniae lineages. The platform will combine patient data and genomic analysis to map transmission and predict outbreaks. Machine learning will help identify high-risk patients and detect known clones from MALDI-TOF Mass Spectrometry data, enabling fast and scalable surveillance. The platform impact and cost-effectiveness will be evaluated.

Antibiotic-resistant bacteria are a growing threat in hospitals. We are developing a digital surveillance system that uses genomic and clinical data to track their spread and support early intervention. Tested in three major Lombardy hospitals, it will integrate AI-like tools to improve speed, reduce costs, and ensure accessibility for smaller healthcare facilities across the region.