Development of an Electronic Algorithm to Target Outpatient Antimicrobial Stewardship Efforts for Acute Bronchitis and Pharyngitis

Abstract Background A major challenge for antibiotic stewardship programs is the lack of accurate and accessible electronic data to target interventions. We developed and validated separate electronic algorithms to identify inappropriate antibiotic use for adult outpatients with bronchitis and pharyngitis. Methods We used International Classification of Diseases, 10th Revision, diagnostic codes to identify patient encounters for acute bronchitis and pharyngitis at outpatient practices between 3/15/17 and 3/14/18. Exclusion criteria included immunocompromising conditions, complex chronic conditions, and concurrent infections. We randomly selected 300 eligible subjects each with bronchitis and pharyngitis. Inappropriate antibiotic use based on chart review served as the gold standard for assessment of the electronic algorithm, which was constructed using only data in the electronic data warehouse. Criteria for appropriate prescribing, choice of antibiotic, and duration were based on established guidelines. Results Of 300 subjects with bronchitis, 167 (55.7%) received an antibiotic inappropriately based on chart review. The electronic algorithm demonstrated 100% sensitivity and 95.3% specificity for detection of inappropriate prescribing. Of 300 subjects with pharyngitis, 94 (31.3%) had an incorrect prescribing decision. Among 29 subjects with a positive rapid streptococcal antigen test, 27 (93.1%) received an appropriate antibiotic and 29 (100%) received the correct duration. The electronic algorithm demonstrated very high sensitivity and specificity for all outcomes. Conclusions Inappropriate antibiotic prescribing for bronchitis and pharyngitis is common. Electronic algorithms for identifying inappropriate prescribing, antibiotic choice, and duration showed excellent test characteristics. These algorithms could be used to efficiently assess prescribing among practices and individual clinicians. Interventions based on these algorithms should be tested in future work.