Discussion

Outpatient ASP metrics are often “encounter based” (eg, encounter-level prescribing rates), and encounter-level diagnosis codes are commonly leveraged to associate antimicrobial prescribing with specific infectious syndromes. ^{Reference Cubillos, Patch and Chandler3,Reference Stenehjem, Wallin and Fleming-Dutra4} A tiered diagnostic approach has commonly been applied wherein encounter ICD-10 codes are stratified into syndromes for which antibiotics are always, sometimes, or never appropriate (eg, tier I, II, and III, respectively). ^{Reference King, Tsay, Hicks, Bizune, Hersh and Fleming-Dutra5,Reference Fleming-Dutra, Hersh and Shapiro6} This approach has allowed institutions to stratify encounters by syndrome(s) within data models; however, reliance on diagnoses coding also introduces inaccuracies when code selection is incorrect or lacks specificity.

Accurate diagnosis code selection has important implications for ASP data modeling in UTIs. Apart from asymptomatic bacteriuria (ASB), other UTIs (ie, complicated cystitis, uncomplicated cystitis, catheter associated cystitis, and pyelonephritis) would all be categorized as tier I in the aforementioned structure. However, optimal drug selection, dosing, and durations of therapy vary widely across diagnoses. ^{Reference Goebel, Trautner and Grigoryan7} Therefore, if diagnosis codes are used to describe trends and/or identify opportunities in prescribing optimization, then coding specificity is paramount to data modeling and subsequent intervention development. Others attempting to steward antimicrobials in ambulatory UTI encounters have also attempted improvements in diagnostic specificity through CDS tools and found improvements in coding specificity. ^{Reference Eudaley, Mihm, Higdon, Jeter and Chamberlin8}

We evaluated outpatient UTI-related ICD-10 code utilization before and after implementation of a diagnosis CDS (ie, diagnosis calculator). Diagnostic calculator implementation resulted in significant improvement in coding specificity. Our study was limited by the exclusion of some infrequently utilized antimicrobials from the antimicrobial group and by lack of chart review in each individual case to confirm appropriate code selection from the calculator. Nevertheless, these findings add to the existing body of evidence suggesting that CDS as an effective means for improving diagnostic specificity that can facilitate ASP efforts toward accurate data modeling and prescribing assessments.