TY - JOUR
T1 - Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard
AU - Safranek, Conrad
AU - Feitzinger, Lauren Elizabeth
AU - Cummings Joyner, Alice Kate
AU - Woo, Nicole
AU - Smith, Virgil
AU - De Souza, Elizabeth
AU - Vasilakis, Christos
AU - Anderson, T. Anthony
AU - Fehr, James
AU - Shin, Andrew
AU - Scheinker, David
AU - Wang, Ellen
AU - Xie, James
N1 - Funding Information:
Stanford University supplied a one-time research grant to Conrad Safranek during summer 2020 to continue development of the dashboard and to write a research manuscript. No other authors received any funding for this research.
Publisher Copyright:
© 2022 American Institute of Physics Inc.. All rights reserved.
PY - 2022/3/31
Y1 - 2022/3/31
N2 - 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.
AB - 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.
KW - anesthesiology
KW - clinical decision-making
KW - machine learning
KW - patient information
KW - pediatrics
UR - http://www.scopus.com/inward/record.url?scp=85126856180&partnerID=8YFLogxK
U2 - 10.1055/s-0042-1744387
DO - 10.1055/s-0042-1744387
M3 - Article
VL - 13
SP - 370
EP - 379
JO - Applied Clinical Informatics
JF - Applied Clinical Informatics
SN - 1869-0327
IS - 2
ER -