A cluster randomized trial of decision support strategies for reducing antibiotic use in acute bronchitis

Abstract

Background: National quality indicators show little change in the overuse of antibiotics for uncomplicated acute bronchitis. We compared the effect of 2 decision support strategies on antibiotic treatment of uncomplicated acute bronchitis.

Methods: We conducted a 3-arm cluster randomized trial among 33 primary care practices belonging to an integrated health care system in central Pennsylvania. The printed decision support intervention sites (11 practices) received decision support for acute cough illness through a print-based strategy, the computer-assisted decision support intervention sites (11 practices) received decision support through an electronic medical record-based strategy, and the control sites (11 practices) served as a control arm. Both intervention sites also received clinician education and feedback on prescribing practices, as well as patient education brochures at check-in. Antibiotic prescription rates for uncomplicated acute bronchitis in the winter period (October 1, 2009, through March 31, 2010) following introduction of the intervention were compared with the previous 3 winter periods in an intent-to-treat analysis.

Results: Compared with the baseline period, the percentage of adolescents and adults prescribed antibiotics during the intervention period decreased at the printed decision support intervention sites (from 80.0% to 68.3%) and at the computer-assisted decision support intervention sites (from 74.0% to 60.7%) but increased slightly at the control sites (from 72.5% to 74.3%). After controlling for patient and clinician characteristics, as well as clustering of observations by clinician and practice site, the differences for the intervention sites were statistically significant from the control sites (P = .003 for control sites vs printed decision support intervention sites and P = .01 for control sites vs computer-assisted decision support intervention sites) but not between themselves (P = .67 for printed decision support intervention sites vs computer-assisted decision support intervention sites). Changes in total visits, 30-day return visit rates, and proportion diagnosed as having uncomplicated acute bronchitis were similar among the study sites.

Conclusions: Implementation of a decision support strategy for acute bronchitis can help reduce the overuse of antibiotics in primary care settings. The effect of printed vs computer-assisted decision support strategies for providing decision support was equivalent.

Trial registration: clinicaltrials.gov Identifier: NCT00981994.

Figure 1. Printed decision support tool

This 11x17 inch poster was laminated and posted in each examination room of practices assigned to the Printed Decision Support intervention arm during the intervention period. The algorithm is based on an evidence-informed approach to assessing pneumonia in adults with acute cough illness.

Figure 2. Determination of uncomplicated acute bronchitis visits in final study population

*Comorbidities include chronic lung disease, congestive heart failure, HIV, cystic fibrosis, and malignancy. **Antibiotic-responsive secondary diagnoses include sinusitis, pharyngitis, otitis media, pneumonia.

Figure 3. Impact of decision support strategies on antibiotic prescription rates for adolescents and adults diagnosed with uncomplicated acute bronchitis

Error bars for each estimate reflect 95% confidence intervals. Statistical comparison between groups yielded the following p-values based on interaction terms from multivariable analysis: Δ Control vs. Δ Printed Decision Support = 0.003; Δ Control vs. Δ Computer Decision Support = 0.014; Δ Printed Decision Support vs. Δ Computer Decision Support = 0.67

Figure 4. Distribution of changes in provider-level antibiotic prescription rates for adolescents and adults diagnosed with uncomplicated acute bronchitis following implementation of the decision support interventions

Each bar represents the absolute difference in antibiotic prescription rates between intervention and baseline years for an individual provider. Providers required to have at least 10 visits in baseline and intervention periods to be eligible for this analysis, representing 46% of PDS providers (n=31), 63% of CDS providers (n=26) and 59% of control providers (n=27). Statistical comparison between groups yielded the following p-values: Δ Control vs. Δ Printed Decision Support = 0.006; Δ Control vs. Δ Computer Decision Support = 0.003; Δ Printed Decision Support vs. Δ Computer Decision Support = 0.084