Surgical site infections do not need to be a common complication in the healthcare field. They can be avoided through the use of surgical site infection prevention bundles. More specifically, the bundles can be personalized to each patient to offer further infection prevention when the patient presents with a higher comorbidity risk. Hospitals could reduce their surgical site infection rates through the use of artificial intelligence combing electronic health records and calculating the Charlson Comorbidity Index (CCI) and American Society of Anesthesiologists (ASA) scores to ultimately form an automatic operating room checklist. Low-risk patients will have a standard primary checklist of interventions. Higher risk patients have additional secondary and tertiary interventions added to their primary checklists.
Through a combination of literature, expert opinion, and various seminars at the APIC (Association for Professionals in Infection Control and Epidemiology), I determined an evidence based primary list of SSI prevention strategies that should be standard amongst all patients. I also gained information on interventions that should be included when patients have higher CCI and ASA scores. My presentation will demonstrate the need for standardization of surgical site infection prevention strategies, the ease that would come from using an artificial intelligence robot to derive the exact intervention checklist best suited for the patient and a cost analysis to demonstrate the current spending and potential savings from using such technology.