Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard

Conrad Safranek, Lauren Elizabeth Feitzinger, Alice Kate Cummings Joyner, Nicole Woo, Virgil Smith, Elizabeth De Souza, Christos Vasilakis, T. Anthony Anderson, James Fehr, Andrew Shin, David Scheinker, Ellen Wang, James Xie

Research output: Contribution to journalArticlepeer-review

Abstract

Background Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers. Objectives This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data. Methods We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation. Results The dashboard design process identified two mechanisms-interactive data filtration and machine-learning-based normalization-that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data. Conclusion A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects.

Original languageEnglish
Pages (from-to)370-379
Number of pages10
JournalApplied Clinical Informatics
Volume13
Issue number2
Early online date23 Mar 2022
DOIs
Publication statusPublished - 31 Mar 2022

Keywords

  • anesthesiology
  • clinical decision-making
  • machine learning
  • patient information
  • pediatrics

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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