Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard

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.

Fig. 1

Overview of the stages of development in building an opioid-utilization dashboard with clinically meaningful comparisons. EHR, electronic health record.

Fig. 2

Stacked bar graph of oral morphine equivalent units (MEU) per kg per case by service line in the operating room (intraoperatively) and post-anesthesia recovery unit (PACU). MEU, oral morphine equivalent units; kg, kilogram; PACU, post anesthesia care unit.

Fig. 3

In the Provider Tab, we implement case-mix adjustment via interactive data filtration by any combination of anesthesiologist, primary procedure, service, date, and regional anesthesia. This mechanism allows clinically meaningful comparisons of individual providers to their peers: ( A ) Histograms compare spotlighted provider ( yellow ) to peers' ( green ) morphine equivalent units (MEU) per kilogram for tonsillectomy cases (includes both intraoperative and PACU opioid received); ( B ) Box and whisker plots compare tonsillectomy post anesthesia recovery unit (PACU) discharge time for spotlighted provider's patients ( above ) to peers' patients ; ( C ) Heat maps ( blue  = 0/10 pain, red  = 10/10 pain) compare PACU first conscious and max tonsillectomy pain scores of spotlighted provider's patients (above) to their peers' patients. MEU, oral morphine equivalent units; kg, kilogram; PACU, post anesthesia care unit; h, hours.

Fig. 4

Algorithmic case-mix adjustment enables visualization and comparison of an individual provider's normalized opioid-utilization “index,” which is equal to the total sum of observed utilization divided by the total sum of machine-learning expected predictions given case details. MEU, oral morphine equivalent units; Obs, observed; Exp, expected.