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
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.