Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard

Area of Science:

• Anesthesiology and Pain Management
• Health Informatics and Data Science
• Pediatric Healthcare

Background:

• Anesthesiologists manage complex patient variables for optimal opioid dosing, balancing pain relief with adverse effects.
• Clinical dashboards can reduce practice variation but struggle with complex case-mix differences in opioid use.
• Visualizing opioid use patterns requires sophisticated methods to account for patient and surgical complexities.

Purpose of the Study:

• To develop a pediatric anesthesia dashboard for visualizing and comparing opioid utilization.
• To demonstrate advanced computational techniques for normalizing complex clinical data.
• To facilitate meaningful comparisons of opioid use among providers by adjusting for case-mix.

Main Methods:

• Engaged perioperative stakeholders to define relevant variables and user requirements for an opioid visualization tool.
• Extracted, aggregated, and standardized case data from a tertiary care pediatric hospital.
• Employed multivariable machine learning for variable identification and integrated findings into an interactive, normalized dashboard.

Main Results:

• The dashboard utilizes interactive data filtration and machine-learning-based normalization for rigorous opioid utilization monitoring.
• Case-mix adjustment enabled meaningful comparisons of opioid use across 24,332 surgical cases.
• The tool successfully identified high and low opioid-use outliers linked to clinical outcomes.

Conclusions:

• An interactive dashboard provides anesthesiologists with timely, case-mix-adjusted data on opioid administration practices.
• Such tools empower physicians to track variations, initiate discussions, and drive continuous improvement in opioid management.
• Clinical analytics dashboards motivate behavioral changes, reduce practice variation, and minimize adverse effects associated with high-impact medications.