We updated the design of this site on December 18, 2017. Learn more.
ClinicalTrials.gov Menu

Evaluation of an Algorithm to Reduce Antibiotic Prescribing for Acute Bronchitis

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
ClinicalTrials.gov Identifier: NCT00981994
Recruitment Status : Completed
First Posted : September 22, 2009
Last Update Posted : November 28, 2016
Information provided by (Responsible Party):

September 14, 2009
September 22, 2009
November 28, 2016
October 2009
May 2011   (Final data collection date for primary outcome measure)
Proportion of visits to primary care clinic associated with antibiotic prescriptions [ Time Frame: 30 days ]
Same as current
Complete list of historical versions of study NCT00981994 on ClinicalTrials.gov Archive Site
Not Provided
Not Provided
Not Provided
Not Provided
Evaluation of an Algorithm to Reduce Antibiotic Prescribing for Acute Bronchitis
Development, Implementation, and Evaluation of Novel Strategies to Reduce Inappropriate Antimicrobial Use in Community and Healthcare Settings
Inappropriate use of antibiotics to treat patients with acute bronchitis is a significant factor contributing to the selection of antimicrobial drug resistant pathogens, which threaten the effectiveness of available therapies to treat common community-acquired bacterial infections. A key factor driving overuse of antibiotics is inaccurate estimation of pneumonia risk among patients with acute cough illnesses. This study will use a cluster randomized trial design within the Geisinger Health System's integrated clinic network to measure the efficacy of an algorithm driven clinical decision support tool to safely reduce the frequency of unnecessary antibiotic prescriptions for adult patients with lower respiratory tract infections.
The rapid rise in antibiotic resistance among common bacteria are adversely affecting the clinical course and health care costs of community-acquired infections. Because antibiotic resistance patterns are strongly correlated with antibiotic use patterns, multiple organizations have declared reductions in unnecessary antibiotic use to be critical components of efforts to combat antibiotic resistance. Among humans, the vast majority of unnecessary antibiotic prescriptions are used to treat acute respiratory tract infections (ARIs) that have a viral etiology. In particular, despite the fact that numerous controlled trials have demonstrated no benefit of antibiotic therapy for patients with acute bronchitis, the majority of patients diagnosed with acute bronchitis continue to receive antibiotic therapy across diverse treatment settings. Recently, the National Committee on Quality Assurance adopted the proportion of adult visits diagnosed as acute bronchitis when an antibiotic was NOT prescribed as a quality measure within the HEDIS data set. Recent results from the HEDIS dataset emphasize the continued high rates of antibiotic prescribing for patients with acute bronchitis. One key factor driving overuse of antibiotics in the management of patients with lower respiratory tract infections—such as acute bronchitis—is diagnostic uncertainty and inaccurate risk estimation of underlying pneumonia in such patients. Recently, our study team has observed substantial reductions in antibiotic prescribing following the incorporation of a diagnostic and treatment algorithm into an acute care setting. This acute cough management algorithm incorporates data on vital signs and symptoms distinguishing patients with community-acquired pneumonia from other patients with acute cough illness, specifically those with acute bronchitis. The acute cough management algorithm has become even more valuable in recent years due to the introduction of quality measures that emphasize the timely administration of antibiotics for patients with community-acquired pneumonia. Thus, strong empirical evidence of the effectiveness of such an algorithm could lead to wide adoption of the algorithm and substantial improvements in antibiotic prescribing. The investigative team is proposing a unique partnership with Geisinger Health System, a large integrated health network, to implement and evaluate the algorithm. Utilizing a cluster-randomized trial design across 33 practice sites, we will address the following aims: 1) To measure the reduction in antibiotic prescribing resulting from incorporation of the algorithm compared to usual care sites utilizing two different implementation strategies, one poster-based and one electronic health record-based, 2) To measure revisits, delayed hospitalizations and net economic costs associated with algorithm implementation, and 3) To evaluate local practice characteristics influencing the level of implementation and ultimate performance success at intervention sites. In a final component of the study, the investigators will partner with NCQA to disseminate study results through the national network of participating plans and stimulate wide spread adoption of the algorithm and quality improvement methods.
Not Provided
Allocation: Randomized
Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Treatment
Acute Respiratory Tract Infection
Behavioral: Decision Support for ARI Management
Use of history and physical examination findings to estimate probability of pneumonia in patients with acute respiratory infections and thereby guide treatment decisions
  • Experimental: Electronic Decision Support
    Use of electronic decision support to provide the treatment algorithm for providers managing patients with acute respiratory infections.
    Intervention: Behavioral: Decision Support for ARI Management
  • Experimental: Paper Decision Support
    Use of paper based tools to provide the treatment algorithm for providers managing patients with acute respiratory infections.
    Intervention: Behavioral: Decision Support for ARI Management
  • No Intervention: Usual Care
    Usual Care
Gonzales R, Anderer T, McCulloch CE, Maselli JH, Bloom FJ Jr, Graf TR, Stahl M, Yefko M, Molecavage J, Metlay JP. A cluster randomized trial of decision support strategies for reducing antibiotic use in acute bronchitis. JAMA Intern Med. 2013 Feb 25;173(4):267-73. doi: 10.1001/jamainternmed.2013.1589.

*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
September 2012
May 2011   (Final data collection date for primary outcome measure)

Inclusion Criteria:

  • Primary care practice sites within the Geisinger Health System

Exclusion Criteria:

  • Sites with < 1000 visits per year for acute respiratory infection
Sexes Eligible for Study: All
16 Years and older   (Child, Adult, Senior)
Contact information is only displayed when the study is recruiting subjects
Not Provided
5R01CI000611( U.S. NIH Grant/Contract )
Not Provided
Not Provided
University of Pennsylvania
University of Pennsylvania
  • University of California, San Francisco
  • Geisinger Clinic
Principal Investigator: Joshua P Metlay, MD, PhD University of Pennsylvania
Principal Investigator: Ralph Gonzales, MD,MS University of California, San Francisco
University of Pennsylvania
November 2016

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP