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Related Experiment Video

Updated: Jan 18, 2026

Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies

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Quantifying Practice Variability to Inform the Design of Implementation Programs in Critical Care and Assess Their

Alison E Turnbull¹, Siyao Zhang², Elizabeth Colantuoni³

¹Division of Pulmonary and Critical Care Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD; Outcomes After Critical Illness and Surgery (OACIS) Group, Johns Hopkins University, Baltimore, MD.

|September 12, 2025

Summary

This summary is machine-generated.

Implementing evidence-based practices (EBPs) in critical care is challenging. Analyzing practice variability in observational data can improve EBP adoption and implementation strategies.

Keywords:

critical care implementation science multilevel analyses quality improvement

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Area of Science:

Implementation Science
Health Services Research
Critical Care Medicine

Background:

Uptake of evidence-based practices (EBPs) in pulmonary and critical care is often incomplete.
Implementation scientists study contexts and design interventions to improve EBP adoption.
Existing methods can be strengthened by analyzing practice variability.

Purpose of the Study:

To propose a combined methodology using hierarchical data analysis to identify practice variability.
To streamline the understanding of clinical contexts for EBP implementation.
To test the assumption of the proposed methodology with national observational data.

Main Methods:

Review of dominant frameworks for understanding clinical implementation contexts.
Description of how measuring practice variability can enhance implementation approaches.
Testing a methodological assumption using observational data from a national mechanical ventilation study.

Main Results:

The proposed combined approach can focus the search for practice determinants.
It allows quantification of evidence generation impact and evaluation of implementation success.
It facilitates comparisons between multiple, simultaneously trialed implementation strategies.

Conclusions:

Integrating hierarchical data analysis with implementation science offers a robust approach.
This methodology enhances the evaluation and comparison of interventions for EBP adoption.
It provides a framework for optimizing critical care practice and patient outcomes.