Near-real-time whole-genome sequencing in hospital infection control: a narrative review.

BACKGROUND: Hospital-acquired infections (HAIs) continue to pose a significant challenge to patient safety, partly because traditional microbiological methods slow down the identification of pathogens and outbreak detection. Near-real-time whole-genome sequencing (WGS) and point-of-care (POC) sequencing workflows offer the potential for quicker pathogen characterization and tracking of transmission, which could lead to better infection control and improved use of antibiotics. OBJECTIVE: To review rapid and POC WGS technologies and workflows used in hospital settings, and to summarize evidence on typical turnaround times, impacts on outbreak detection, and implications for clinical decision-making. METHOD: A narrative review was conducted using literature retrieved from major biomedical databases, including PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar. Relevant studies addressing whole-genome sequencing, rapid/near-real-time sequencing, healthcare-associated infections, outbreak investigation, and hospital infection control were identified and reviewed. RESULTS: Streamlined WGS workflows cut turnaround times from several days (typical of traditional methods) to between 13.5 and 48 h in many rapid implementations. Some nanopore-based protocols report initial species identification in minutes and complete genomic data within a few hours. Faster sequencing and real-time analytics lead to earlier identification of resistance markers and quicker detection of transmission chains. Several studies indicate outbreak detection is easier than with traditional epidemiological methods. This allows for more timely targeted therapy and can enhance the responsible use of antibiotics when combined with clinical decision processes. CONCLUSION: Near-real-time and point-of-care whole-genome sequencing significantly reduces the time needed to identify pathogens and speeds up outbreak detection. However, it requires consistent reporting of turnaround metrics, and careful evaluation of how faster genomic data affects clinical decisions and patient outcomes.