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Achieving Clostridioides difficile infection Health and Human Services 2020 goals: Using agile implementation to bring evidence to the bedside

Published online by Cambridge University Press:  05 December 2019

Lana DbeiboOpen the ORCID record for Lana Dbeibo[Opens in a new window],
Kristen Kelley,
Cole Beeler,
Areeba Kara,
Patrick Monahan,
Anthony J. Perkins,
Yun Wang,
Allison Brinkman,
William Snyderman and
Nicole Hatfield
...Show all authors
Lana Dbeibo*
Affiliation:
Division of Infectious Diseases, Department of Medicine, Indiana University School of Medicine, Indianapolis, IndianaDepartment of Infection Prevention, Indiana University Health, Indianapolis, Indiana
Kristen Kelley
Affiliation:
Department of Infection Prevention, Indiana University Health, Indianapolis, Indiana
Cole Beeler
Affiliation:
Division of Infectious Diseases, Department of Medicine, Indiana University School of Medicine, Indianapolis, IndianaDepartment of Infection Prevention, Indiana University Health, Indianapolis, Indiana
Areeba Kara
Affiliation:
Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana
Patrick Monahan
Affiliation:
Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana
Anthony J. Perkins
Affiliation:
Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana
Yun Wang
Affiliation:
Department of Infection Prevention, Indiana University Health, Indianapolis, Indiana
Allison Brinkman
Affiliation:
Department of Infection Prevention, Indiana University Health, Indianapolis, Indiana
William Snyderman
Affiliation:
Department of Infection Prevention, Indiana University Health, Indianapolis, Indiana
Nicole Hatfield
Affiliation:
Department of Infection Prevention, Indiana University Health, Indianapolis, Indiana
Justin Wrin
Affiliation:
Department of Pharmacy, Indiana University Health, Indianapolis, Indiana
Joan Miller
Affiliation:
Department of Quality and Safety, Indiana University Health, Indianapolis, Indiana
Douglas Webb
Affiliation:
Department of Infection Prevention, Indiana University Health, Indianapolis, Indiana
Jose Azar
Affiliation:
Department of Medicine, Indiana University School of Medicine, Indianapolis, IndianaDepartment of Quality and Safety, Indiana University Health, Indianapolis, IndianaCenter for Health Innovation and Implementation Science, Indiana University School of Medicine, Indianapolis, Indiana
*
Author for correspondence: Lana Dbeibo, E-mail: ldbeibo@iu.edu
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Abstract

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Information

Type
Research Brief
Information
Infection Control & Hospital Epidemiology , Volume 41 , Issue 2 , February 2020, pp. 237 - 239
Copyright
© 2019 by The Society for Healthcare Epidemiology of America. All rights reserved.

Clostridioides difficile infection (CDI) is the leading cause of hospital-acquired infections (HAI) in the United States.Reference Magill, Edwards and Bamberg1 Although multiple interventions have been shown to reduce CDI, the adoption of these evidence-based practices remains suboptimal, and the burden of CDI remains high.Reference Yanke, Moriarty, Carayon and Safdar2 There is a pressing need to develop strategies that bridge the gap between the available evidence and clinical practice to reduce harm from CDI.

The ‘Agile Implementation’ (AI) framework was used to reduce central-line–associated bloodstream infections (CLABSIs) at our institution.Reference Azar, Kelley and Dunscomb3 In study described here, we used the AI model to achieve reductions in CDI.

Methods

Setting

The study was conducted in 2 large academic hospitals in the Midwest between October 2016 and December 2018.

AI model

The AI model was developed at the Indiana University School of Medicine Center for Innovation and Implementation Science. It recognizes the healthcare system as a complex adaptive system (CAS) and acknowledges the importance of individual barriers and enablers in influencing implementation outcomes. It therefore incorporates the CAS theory and the social cognitive theories of behavioral economics to facilitate implementation.

The first step is to confirm opportunities and engage stakeholders. The second step is to identify evidence-based solutions. The third step is to develop an evaluation strategy in which process and outcome measures are defined and a termination plan is agreed upon if these measures are not met. An interdisciplinary team then converts the evidence-based solution into a minimum viable service, the minimum specifications required to implement the intervention effectively, while taking into consideration the local environment in which it is applied. Implementation occurs in “sprints” (repeatable tests of change) with modification through continuous feedback. Impact on the organization is also measured. If the process results in the desired outcome, then the team develops a minimally standardized operating procedure, an implementation blueprint that can be utilized by other departments.

Implementation

An interdisciplinary team met in October 2016 to create a CDI reduction strategy. A gap analysis was conducted and opportunities were identified in environmental services (EVS), antibiotic stewardship, hand hygiene, and CDI testing practices. Four subteams were created to address each identified opportunity using evidence-based guidelines.Reference McDonald, Gerding and Johnson4

The subteams met monthly to strategize and modify interventions based on feedback from the implementation sprints. Localization of the implementation of the minimal viable solutions was left to individual units where appropriate.

Measurements

Outcome measures. The primary outcome was the CDI rate, as defined by the National Healthcare Safety Network. A standardized incidence ratio (SIR) was calculated based on the Centers for Disease Control definition.

Implementation outcomes. Data regarding the implementation were obtained from direct feedback received during team meetings. Changes in practice habits were assessed by tracking antibiotic days of therapy (DOT), acceptance rate for antibiotic stewardship recommendations, black-light data for monitoring of environmental cleaning, number of CDI tests ordered, and hand hygiene compliance data.

System outcomes. Non-CDI hospital-acquired infections were monitored.

Statistical analysis

We used Poisson regression to test whether the outcomes (incidence rates of infections, SIR, DOT) differed between the baseline and the implementation period. Time period was included as a fixed effect in all models and an offset equal to the log(exposure) to account for the differential exposure time across periods. Relative risks (RRs) and 95% confidence intervals (CIs) were reported. Logistic regression was used to determine whether hand hygiene and EVS compliance differed between the baseline period and the implementation period. The odds ratios (OR) and 95% CIs were reported. Linear regression was used to determine change in CDI testing. All analyses were conducted using SAS version 9.4 statistical software (IBM, Armonk, NY).

Results

The CDI rate decreased from 12.47 to 7.23 cases per 10,000 patient days during the implementation period (RR, 0.58; 95% CI, 0.51–0.66; P < 0.001). Data on implementation and system outcomes are summarized in Table 1.

Table 1.

Calculated Rates (95% confidence intervals) of Hospital-Acquired Harms and Regression Results

Note. CLABSI, central line-associated bloodstream infection; CAUTI, catheter-associated urinary tract infection; CDI, Clostridioides difficile infections; DOT, days of therapy; SIR, standardized incidence ratio (observed/expected infections); RR, relative risk; OR, odds ratio. Poisson and logistic regression, respectively, were used to compare implementation to baseline periods, for infections and compliance procedures.

a Per 1,000 central-line days.

b Per 1,000 Foley catheter days.

c Per 10,000 patient days.

The stewardship recommendations acceptance rate started at 45% in January 2017 and increased to an average of 84% in 2018. CDI testing orders were static in 2017 then decreased by 38.5% in 2018, coinciding with the implementation period of the intervention.

Discussion

We utilized the AI model for implementing evidence-based CDI guidelines, and we observed a 42% reduction in CDI. This model focuses on the characteristics of the users and adopters of implementation as well as the organizational context in implementing these processes. This process results in the engagement of key stakeholders and localization of measures to adapt to the local environment within the organization, leading to increased and rapid adoption of practices and sustainability in processes.Reference Azar, Kelley and Dunscomb3 By viewing the healthcare system through the lens of complexity science, taking into account nonlinear and unpredictable interactions among its individuals, monitoring for unintended consequences, and using minimal viable solutions to allow for localization,Reference Pype, Mertens, Helewaut and Krystallidou5 the AI model successfully facilitated the implementation of evidence into practice in the real-world care setting, thereby leading to a significant reduction in CDI. In this model of implementation, a termination plan is developed initially, which eases stakeholders’ loss aversion during change and reduces waste. Additionally, the model focuses on local adaptation of solutions, and on monitoring of consequences of the implementation on other system outcomes.

This is a single-center study and our results may not be generalizable to other institutions. We did not perform a cost–benefit analysis. We successfully utilized the AI framework to impact clinician behavior and to implement evidence-based practices to prevent CDI. We hope that others can learn from our journey.

References

Magill, SS, Edwards, JR, Bamberg, W, et al.Multistate point-prevalence survey of health care-associated infections. N Engl J Med 2014;370:1198–1208.CrossRefGoogle ScholarPubMed
Yanke, E, Moriarty, H, Carayon, P, Safdar, N.A qualitative, interprofessional analysis of barriers to and facilitators of implementation of the Department of Veterans’ Affairs Clostridium difficile prevention bundle using a human factors engineering approach. Am J Infect Control 2018;46:276–284.CrossRefGoogle Scholar
Azar, J, Kelley, K, Dunscomb, J, et al.Using the agile implementation model to reduce central line-associated bloodstream infections. Am J Infect Control 2018. doi:10.1016/j.ajic.2018.07.008.CrossRefGoogle Scholar
McDonald, LC, Gerding, DN, Johnson, S, et al.Clinical practice guidelines for Clostridium difficile Infection in adults and children: 2017 update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA). Clin Infect Dis 66:987–994.CrossRefGoogle Scholar
Pype, P, Mertens, F, Helewaut, F, Krystallidou, D. 2018. Healthcare teams as complex adaptive systems: understanding team behaviour through team members’ perception of interpersonal interaction. BMC Health Serv Res 18:570.CrossRefGoogle ScholarPubMed
Figure 0

Table 1.Calculated Rates (95% confidence intervals) of Hospital-Acquired Harms and Regression Results