Leveraging Electronic Health Record Clinical Decision Support to Identify Clostridium difficile Infection in Clinically Appropriate Patient Populations. (October 2020)

• Record Type: Journal Article
• Authors: Cook, Amy; Fruehan, Sharon; Goodling, Pamela
• Is Part Of: Infection control and hospital epidemiology. Volume 41(2020)Supplement 1
• Page Start: s311
• Page End: s311
• Publication Date: 2020-10
• Subjects: Nosocomial infections -- Epidemiology -- Periodicals; Health facilities -- Sanitation -- Periodicals; Hospital buildings -- Sanitation -- Periodicals; Cross Infection -- Periodicals; Epidemiology -- Periodicals; Hospitals -- Periodicals; Infection Control -- Periodicals; 614.44
• DOI: 10.1017/ice.2020.901
• Languages: English
• ISSNs: 0899-823X

Abstract

Abstract : Background: In 2017, the IDSA and the SHEA released updated Clostridium difficile practice guidelines. Implementing institutionally accepted criteria for identifying clinically appropriate patients for testing was endorsed. When utilizing NAAT as the sole laboratory testing methodology, testing clinically symptomatic patients is important to reduce inappropriate treatment of C. difficile colonization. C. difficile rates at a regional community health system were higher than expected, and patient case reviews identified inappropriate patient testing as an issue. Therefore, the infection prevention team sought to optimize and standardize protocols surrounding appropriate patient selection for C. difficile testing. Methods: Current recommendations were evaluated, and processes formulated to implement an innovative process to support our clinicians in identifying clinically appropriate patients to test for C. difficile infection. The electronic decision support is summarized as a bundled approach with 4 best practice alerts that incorporate algorithms that warn providers of potentially inappropriate testing scenarios. These alerts include the following criteria: (1) a laxative having been administered within 48 hours of attempted order, (2) a negative test resulted within 7 days, (3) a positive test resulted within 14 days, and (4) identification of patients at high risk for C. difficile infection (based on recent long-term care facility exposure, recent inpatient hospital visits, recent antimicrobial therapy). Outcomes of our acute-care hospitals were monitored by real-time evaluation of each hospital's quarterly C. difficile LabID standardized infection ratio (SIR) as defined by the NHSN. For statistical analyses, the cumulative second and third quarters of 2018 (before the intervention) were compared to the cumulative second and third quarters of 2019 to account for seasonality of C. difficile infections. Results: Utilizing the NHSN statistical calculator to compare 2 SIRs, there was a statistically significant decrease ( P = .0026) in the largest hospital's C. difficile LabID SIR when comparing representative preintervention cumulative quarters to the postintervention cumulative quarters. Although the other hospitals did not see a statistically significant decrease in their C. difficile LabID SIR, a clinically significant decrease was appreciated for 2 of our hospitals. Conclusions: Electronic health record–based decision support helps clinicians identify clinically appropriate patients to test by NAAT alone for C. difficile infection. By limiting the number of patients tested without clinical signs or symptoms of infection and/or after receiving laxatives, hospitals more accurately capture their true C. difficile rates and maximize reimbursement based on this measure within the CMS Safety of Care Measure. Funding: None Disclosures: None