AI driven decision support reduces antibiotic mismatches and inappropriate use in outpatient urinary tract infections - Shirley Shapiro Ben David; Roni Romano; Daniella Rahamim-Cohen; Joseph Azuri; Shira Greenfeld; Ben Gedassi; Uri Lerner (npj Digital Medicine)
Abstract Urinary tract infections (UTIs) often prompt empiric outpatient antibiotic prescriptions, risking mismatches. This study evaluates the impact of “UTI Smart-Set” (UTIS), an AI-driven decision-support tool, on prescribing patterns and mismatches in a large outpatient organization. UTIS integrates machine learning forecasts of antibiotic resistance, patient data, and guidelines into a user-friendly order set for UTI management. From 6/1/2021–8/31/2022, 171,010 UTI diagnoses were recorded, with UTIS used in 75,630 cases involving antibiotic prescriptions. Overall acceptance rate of UTIS recommendations was 66.0%. Among 19,287 cases with urine cultures, antibiotic mismatch rate was significantly lower when UTIS recommendations were followed (8.9% vs. 14.2%, p < 0.0001). Among women over 18, mismatch rate was 47.5% lower, and among women over 50, 55.6% lower (p < 0.001). Additionally, an overall reduction of 80.5% in ciprofloxacin usage (6.4% vs 32.9%, p < 0.0001) was observed. UTIS improved prescribing accuracy, reduced mismatches, and minimized quinolone use, highlighting AI’s potential for personalized infection management.