Antibiotic Outbreak, Risk Factors for Never Event, Prediction of Inappropriate Use
| Status: | Active, not recruiting |
|---|---|
| Conditions: | Infectious Disease |
| Therapuetic Areas: | Immunology / Infectious Diseases |
| Healthy: | No |
| Age Range: | 18 - 90 |
| Updated: | 4/17/2018 |
| Start Date: | January 1, 2014 |
| End Date: | February 1, 2021 |
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.
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.
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.
Inclusion Criteria:
- receipt of inpatient intravenous vancomycin during proposed study period
- adults 18 years of age or older and less than 90 years of age
Exclusion Criteria:
- individuals who are not yet adults (infants, children, teenagers)
- pregnant women
- prisoners
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