Infectious Disease Clinical Trial
Official title:
A Retrospective Study to Understand the Risk Factors/Drivers of "Inappropriate" Antimicrobial Use and the Performance Evaluation of a Clinical Decision Support Tool That Facilitates Prediction of Outbreaks of Inappropriate Antibiotic Use
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.
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. ;
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