A novel intelligent approach for infection protection using a multidisciplinary collaboration in regional general hospitals.

Hospital-acquired infections (HAIs) pose a severe and pervasive threat to patient safety and impose immense pressure on medical resources. These infections often occur during the treatment of other problems due to prolonged hospital stays and can lead to severe complications and increased mortality rates. The spread of HAIs not only compromises patient safety but also stresses medical resources, increasing treatment costs and placing additional demands on healthcare staff. Traditional infection control measures often struggle to deal with the complex and dynamic nature of the infection risks in regional hospitals where decision-making is necessary. The existing frameworks are unable to handle the ambiguity, stakeholder management conflicts and limitations involved in the infection control. So, this study introduces a novel intelligent approach by defining the combined compromised solution (COCOSO) method within the complex picture fuzzy (CPF) framework technology that combines expert evaluation methods with fuzzy reasoning to evaluate and rank different infection control prevention in regional general hospitals by integrating multidisciplinary collaboration as it is the cornerstone of intervention strategies. By incorporating expertise across healthcare professionals such as physicians, nurses, microbiologists, infection control specialists, and data analysts, this approach harnesses collective insight for more effective infection control. Moreover, this study employs robust multi-criteria group decision-making (MCGDM) approach by using a hypothetical case study which shows that antibiotic stewardship program ranks first and offers a scalable and adaptable model to systematically prioritize and optimize infection prevention measures across various healthcare departments and offers a safer and more resilient environment.