Antibiotic Outbreak, Risk Factors for Never Event, Prediction of Inappropriate Use
Condition: Infectious Disease
MeSH terms: Communicable Diseases; Infections
Study type: Observational

Summary: 
      In order to decrease inappropriate antibiotic use, drivers of inappropriate use must be
      identified locally. This study will focus on the MOST inappropriate use, which are defined as
      'never events'. Previous work has shown that antibiotic use clusters over time. It is
      hypothesized that never events also cluster over time. Using electronic data capture
      strategies, an algorithm will be developed to quickly and accurately identify areas of
      antibiotic use concern. Secondly, a framework will be developed, utilizing antimicrobial
      consumption data and captured signals of inappropriate antimicrobial use to provide targets
      for antimicrobial stewardship efforts.
    

Detailed description: 
      Appropriateness in antimicrobial prescribing has become a focal national and international
      issue. It has been estimated that upwards of 50% of antibiotic use is inappropriate. With
      this backdrop, a national strategic goal has been set by the United States White House to
      decrease inappropriate antibiotic use by 20% and 50%, respectively for inpatient and
      outpatient settings. In order to decrease inappropriate use, drivers of incorrect use must be
      identified at each local setting. The actual drivers of confirmed inappropriate use have been
      difficult to identify except when using time and resource intense chart reviews. Even the
      largest contemporary antibiotic consumption studies have not assessed appropriateness as it
      was 'outside of study scope'. Further, there is no consensus or agreement on what constitutes
      inappropriate use. These apparent omissions underscore the difficulty and complexity in
      attributing appropriateness of use for antimicrobials. Importantly, this study will focus on
      the MOST inappropriate use, which are defined as 'never events'. Previous work has shown that
      antibiotic use clusters over time. It is hypothesized that never events also cluster over
      time. Using electronic data capture strategies, an algorithm will be developed to quickly and
      accurately identify areas of antibiotic use concern. Secondly, a framework will be developed,
      utilizing antimicrobial consumption data and captured signals of inappropriate antimicrobial
      use to provide targets for antimicrobial stewardship efforts.
    

Observational model: Cohort
Time perspective: Retrospective
Sampling method: Non-Probability Sample
Study population: 
        The retrospective study population will be based on all inpatient intravenous vancomycin
        used during the proposed 36-month study period. Patient level data that identifies adults
        unable to consent, individuals who are not yet adults, pregnant women, or prisoners will
        not be collected.
      
Keywords: Anti-Bacterial Agents/therapeutic use; Drug Utilization/statistics & numerical data; Hospitals/statistics & numerical data; Data Analysis, Statistical; Interpretation, Statistical Data; Antibiotic Stewardship; Electronic Medical Record