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Eliminating Monitor Overuse (EMO) type III effectiveness-deimplementation cluster-randomized trial: Statistical
Rui Xiao1, Christopher P Bonafide2,3,4, Nathaniel J Williams5
1Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104-6021, USA.
The EMO trial will test strategies to stop overuse of pulse oximetry (SpO2) monitoring in children with bronchiolitis. This study aims to improve healthcare value by reducing unnecessary monitoring practices.
Area of Science:
- Health services research
- Implementation science
- Pediatric medicine
Background:
- Overuse of health interventions can lead to harm and inefficiency.
- Continuous pulse oximetry (SpO2) monitoring is often overused in children not requiring supplemental oxygen, despite guidelines recommending against it.
- The Eliminating Monitor Overuse (EMO) SpO2 trial targets this guideline-discordant practice for deimplementation.
Purpose of the Study:
- To compare the effectiveness of two deimplementation strategies: unlearning alone versus unlearning plus substitution.
- To assess the sustainment of deimplementation of SpO2 monitoring in children with bronchiolitis on room air.
Main Methods:
- A hybrid type 3 effectiveness-deimplementation trial with a longitudinal cluster-randomized design.
- Generalized hierarchical mixed-effects models for longitudinal clustering outcomes to analyze deimplementation sustainment.
- Linear mixed-effects regressions for secondary outcomes (length of hospital stay, oxygen supplementation duration).
Main Results:
- This section details the statistical analysis plan, not the results.
- The primary outcome is deimplementation sustainment, analyzed using a difference-in-differences approach.
- Mediation analysis will explore hospital-level factors influencing deimplementation.
Conclusions:
- The EMO Trial is expected to advance deimplementation science by providing insights into sustained practice change.
- A pre-specified statistical analysis plan will ensure rigorous evaluation and mitigate reporting bias.
- This study supports data-driven approaches to optimize healthcare interventions.
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