O0003 Oct.4: 3:25pm - Automated Analysis of Prescription Data to Reduce Prolonged Antibiotic Courses in the Pediatric Urgent Care Setting

Background: With the increase of multi-drug resistant organisms (MDRO) in the United States and abroad, good antibiotic stewardship has become an integral aspect of delivering appropriate care to patients. As utilization of retail clinics and urgent care centers increases in the U.S., it becomes especially important to evaluate the antibiotic prescribing practices in those settings. Existing research in this area has noted that urgent cares and retail clinics prescribe antibiotics for 39.0% and 36.4% of their visits respectively, compared to 13.8% and 7.1% for Emergency Department (ED) and medical office visits respectively[1]. At the pediatric urgent cares in our institution, we identified prolonged durations for antibiotic prescriptions for urinary tract infections (UTIs). Our aim was to reduce the proportion of cephalexin prescriptions for UTI with a prolonged duration from 81% to 20% within 6 months.

Methodology: Our team in conjunction with our infectious disease colleagues implemented guidelines for appropriate antibiotic courses. We developed an algorithm that automatically abstracts data from the electronic health record (EHR) and evaluates the appropriateness of antibiotic prescription in relation to diagnosis, taking into account both duration and dose. Plan-Do-Study-Act (PDSA) cycle #1 interventions were the development and dissemination of appropriate antibiotic dosing schedules for presumed UTI to our team and creating appropriate order-sets for antibiotic prescribing based on diagnosis and age. PDSA cycle #2 interventions were continued provider education, dissemination of guidelines to our moonlighting staff, and further improvement of order-sets. Our primary measure was the percent of antibiotic prescriptions for UTI that were adherent with the guidelines set forth by the team.

Discussion: From October to January 2019, 81% of antibiotic prescriptions for UTI had either a prolonged duration or a dose that was inconsistent with our internal guidelines. After PDSA#1, there was a notable reduction in the percentage of prescriptions that did not adhere to the recommended guidelines to 34%. After PDSA#2 we were able to reduce the percentage to 0% however after the COVID-19 stay-at-home order in our state was enacted we noted a sudden increase in our nonadherence to 62% which was well outside our goal of <20% (Figure 1).

Conclusion: Our fully automated algorithm allowed us to quickly identify non-adherence to our prescribing guidelines, automatically produce tailored education to individual providers and reports without the need for manual chart review. Using QI methodology, we demonstrated that education and EHR order-set implementation can successfully increase adherence to our organization’s prescribing guidelines, leading to improved antibiotic stewardship. Unfortunately, the COVID-19 pandemic likely led providers to focus on other areas of care leading to a sudden increase in nonadherence to guidelines. Therefore we are continuing our efforts to develop additional decision to support to guide providers to the recommended antibiotic duration.