Background: Hospitalists play a critical role in antimicrobial stewardship as the primary antibiotic prescriber for many inpatients. We sought to describe antibiotic prescribing variation among hospitalists within a healthcare system. Methods: We created a novel metric of hospitalist-specific antibiotic prescribing by linking hospitalist billing data to hospital medication administration records in 4 hospitals (two 500-bed academic (AMC1 and AMC2), one 400-bed community (CH1), and one 100-bed community (CH2)) from January 2016 to December 2018. We attributed dates that a hospitalist electronically billed for a given patient as billed patient days (bPD) and mapped an antibiotic day of therapy (DOT) to a bPD. Each DOT was classified according to National Healthcare Safety Network antibiotic categories: broad-spectrum hospital-onset (BS-HO), broad-spectrum community-onset (BS-CO), anti-MRSA, and highest risk for Clostridioides difficile infection (CDI). DOT and bPD were pooled to calculate hospitalist-specific DOT per 1,000 bPD. Best subsets regression was performed to assess model fit and generate hospital and antibiotic category-specific models adjusting for patient-level factors (eg, age ≥65, ICD-10 codes for comorbidities and infections). The models were used to calculate predicted hospitalist-specific DOT and observed-to-expected ratios (O:E) for each antibiotic category. Kruskal-Wallis tests and pairwise Wilcoxon rank-sum tests were used to determine significant differences between median DOT per 1,000 bPD and O:E between hospitals for each antibiotic category. Results: During the study period, 116 hospitalists across 4 hospitals contributed a total of 437,303 bPD. Median DOT per 1,000 bPD varied between hospitals (BS-HO range, 46.7–84.2; BS-CO range, 63.3–100; anti-MRSA range, 48.4–65.4; CDI range, 82.0–129.4). CH2 had a significantly higher median DOT per 1,000 bPD compared to the academic hospitals (all antibiotic categories P < .001) and CH1 (BS-HO, P = .01; anti-MRSA, P = .02) (Fig. 1A). The 4 antibiotic groups at 4 hospitals resulted in 16 models, with good model fit for CH2 (R2 > 0.55 for all models), modest model fit for AMC2 (R2 = 0.46–0.55), fair model fit for CH1 (R2 = 0.19–0.35), and poor model fit for AMC1 (R2 < 0.12 for all models). Variation in hospitalist-specific O:E was moderate (IQR, 0.9–1.1). AMC1 showed greater variation than other hospitals, but we detected no significant differences in median O:E between hospitals (all antibiotic categories P > .10) (Fig. 1B). Conclusions: Adjusting for patient-level factors significantly reduced much of the variation in hospitalist-specific DOT per 1,000 bPD in some but not all hospitals, suggesting that unmeasured factors may drive antibiotic prescribing. This metric may represent a target for stewardship intervention, such as hospitalist-specific feedback of antibiotic prescribing practices.
Disclosures: Scott Fridkin, consulting fee - vaccine industry (various) (spouse)