infection control & hospital epidemiology october 2017, vol. 38, no. 10 original article
The Impact of a Computerized Clinical Decision Support Tool on Inappropriate Clostridium difficile Testing
Duncan R. White, MD;1 Keith W. Hamilton, MD;1 David A. Pegues, MD;1 Asaf Hanish, MPH;2 Craig A. Umscheid, MD, MSCE1,2,3,4,5,6
objective. To evaluate the effectiveness of a computerized clinical decision support intervention aimed at reducing inappropriate Clostridium difficile testing.
design. Retrospective cohort study. setting. University of Pennsylvania Health System, comprised of 3 large tertiary-care hospitals. patients. All adult patients admitted over a 2-year period.
intervention. Providers were required to use an order set integrated into a commercial electronic health record to order C. difficile toxin testing. The order set identified patients who had received laxatives within the previous 36 hours and displayed a message asking providers to consider stopping laxatives and reassessing in 24 hours prior to ordering C. difficile testing. Providers had the option to continue or discontinue laxatives and to proceed with or forgo testing. The primary endpoint was the change in inappropriate C. difficile testing, as measured by the number of patients who had C. difficile testing ordered while receiving laxatives.
results. Compared to the 1-year baseline period, the intervention resulted in a decrease in the proportion of inappropriate C. difficile testing (29.6% vs 27.3%; P=.02). The intervention was associated with an increase in the number of patients who had laxatives discontinued and did not undergo C. difficile testing (5.8% vs 46.4%; P<.01) and who had their laxatives discontinued and underwent testing (5.4% vs 35.2%; P<.01). We observed a nonsignificant increase in the proportion of patients with C. difficile related complications (5.0% vs 8.9%; P=.11).
conclusions. A C. difficile order set was successful in decreasing inappropriate C. difficile testing and improving the timely discontinuation of laxatives.
Infect Control Hosp Epidemiol 2017;38:1204–1208
Clostridium difficile is common, accounting for 12.1% of all healthcare-associated infections.1 In recent decades, the rate of C. difficile infections has risen coinciding with increasing use of antibiotics in some populations.2 The rate of C. difficile infections in US hospitals doubled from 1996 to 2003, increasing from 31 to 61 infections per 100,000 patients.3 In 2008, C. difficile infections in acute-care settings cost between $1 billion and $4.8 billion.4 The 30-daymortality rate following C. difficile infection was recently estimated to be 9.3%in patients with healthcare-onset C. difficile.5 Research is increasingly focusing on asymptomatic carriage
of C. difficile. Studies looking at rates of asymptomatic carriage of toxigenic C. difficile in hospitalized patients have found rates ranging between 9% and 15%.6–8 Patients who are
asymptomatic carriers may be misdiagnosed as having C. dif- ficile infections if they undergo testing that is not clinically indicated. One such example is C. difficile testing in patients who experience diarrhea resulting from other causes such as the use of laxatives rather than infection. A study of hospital- onset C. difficile infections at an academic tertiary-care hospital found that 14.8%of patients with a positive C. difficile test did not meet an indication for testing.9 Treatment of asympto- matic carriers increases the risk of side effects and costs of care. We hypothesized that a computerized clinical decision
support (CCDS) tool could reduce inappropriate testing for C. difficile among patients receiving concurrent laxative therapy. In this study, we examined the impact of such aCCDS tool integrated into a commercial electronic health record
Affiliations: 1. Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; 2. Center for Evidence-Based
Practice, University of Pennsylvania Health System, Philadelphia, Pennsylvania; 3. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; 4. Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pennsylvania; 5. Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania; 6. Institute for TranslationalMedicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania.
PREVIOUS PRESENTATIONS: These data were presented as a poster at the Society of Hospital Medicine Annual Meeting on March 30, 2015, in National Harbor, Maryland, and at the 8th Annual Mid-Atlantic Healthcare Informatics Symposium, October 23, 2015, Philadelphia, Pennsylvania.
© 2017 by The Society for Healthcare Epidemiology of America. All rights reserved. 0899-823X/2017/3810-0010. DOI: 10.1017/ice.2017.161 Received March 24, 2017; accepted July 5, 2017; electronically published August 1, 2017
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