Clinical Decision Support Systems to Reduce Unnecessary Clostridioides difficile Testing Across Multiple Hospitals

Author:

Rock Clare1,Abosi Oluchi2,Bleasdale Susan3,Colligan Erin4,Diekema Daniel J5,Dullabh Prashila4,Gurses Ayse P1,Heaney-Huls Krysta4,Jacob Jesse T6,Kandiah Sheetal6,Lama Sonam4,Leekha Surbhi7,Mayer Jeanmarie8,Mena Lora Alfredo J3,Morgan Daniel J7,Osei Patience1,Pau Sara1,Salinas Jorge L5,Spivak Emily8,Wenzler Eric9,Cosgrove Sara E1

Affiliation:

1. Johns Hopkins University School of Medicine , Baltimore, Maryland , USA

2. University of Iowa Hospitals and Clinics , Iowa City, Iowa , USA

3. University of Illinois College of Medicine at Chicago , Chicago, Illinois , USA

4. National Opinion Research Center, University of Chicago , Chicago, Illinois , USA

5. Carver College of Medicine, University of Iowa , Iowa City, Iowa , USA

6. Emory University School of Medicine , Atlanta, Georgia , USA

7. University of Maryland School of Medicine , Baltimore, Maryland , USA

8. University of Utah School of Medicine , Salt Lake City, Utah , USA

9. College of Pharmacy, University of Illinois Chicago , Chicago, Illinois , USA

Abstract

Abstract Background Inappropriate Clostridioides difficile testing has adverse consequences for patients, hospitals, and public health. Computerized clinical decision support (CCDS) systems in the electronic health record (EHR) may reduce C. difficile test ordering; however, effectiveness of different approaches, ease of use, and best fit into healthcare providers’ (HCP) workflow are not well understood. Methods Nine academic and 6 community hospitals in the United States participated in this 2-year cohort study. CCDS (hard stop or soft stop) triggered when a duplicate C. difficile test order was attempted or if laxatives were recently received. The primary outcome was the difference in testing rates pre– and post–CCDS interventions, using incidence rate ratios (IRRs) and mixed-effect Poisson regression models. We performed qualitative evaluation (contextual inquiry, interviews, focus groups) based on a human factors model. We identified themes using a codebook with primary nodes and subnodes. Results In 9 hospitals implementing hard-stop CCDS and 4 hospitals implementing soft-stop CCDS, C. difficile testing incidence rate (IR) reduction was 33% (95% confidence interval [CI]: 30%–36%) and 23% (95% CI: 21%–25%), respectively. Two hospitals implemented a non-EHR-based human intervention with IR reduction of 21% (95% CI: 15%–28%). HCPs reported generally favorable experiences and highlighted time efficiencies such as inclusion of the patient’s most recent laxative administration on the CCDS. Organizational factors, including hierarchical cultures and communication between HCPs caring for the same patient, impact CCDS acceptance and integration. Conclusions CCDS systems reduced unnecessary C. difficile testing and were perceived positively by HCPs when integrated into their workflow and when displaying relevant patient-specific information needed for decision making.

Funder

Centers for Disease Control and Prevention

Publisher

Oxford University Press (OUP)

Subject

Infectious Diseases,Microbiology (medical)

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