Evidence map›Paper›PMID 35322398›Full record

ArticleApplied clinical informatics2022

Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard.

Conrad W Safranek, Lauren Feitzinger, Alice Kate Cummings Joyner, Nicole Woo, Virgil Smith, Elizabeth De Souza, Christos Vasilakis, Thomas Anthony Anderson, James Fehr, Andrew Y Shin and 3 more

Open access · bronzeAbstract read
In one paragraph

Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Observational
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 2 institutions in 2 countries.

Conrad W SafranekDepartment of Biology: Computational Biology, Stanford University, Stanford, United States.
Lauren FeitzingerDepartment of Management Science and Engineering, Stanford University, Stanford, United States.
Alice Kate Cummings JoynerDepartment of Management Science and Engineering, Stanford University, Stanford, United States.
Nicole WooDepartment of Management Science and Engineering, Stanford University, Stanford, United States.
Virgil SmithDepartment of Management Science and Engineering, Stanford University, Stanford, United States.
Elizabeth De SouzaDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States.
Christos VasilakisBath Centre for Healthcare Innovation and Improvement, School of Management, Centre for Healthcare Innovation and Improvement, University of Bath, Bath, United Kingdom.
Thomas Anthony AndersonDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States.
James FehrDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States.
Andrew Y ShinDepartment of Pediatrics-Cardiology, Stanford University School of Medicine, Stanford, California, United States.
David ScheinkerDepartment of Management Science and Engineering, Stanford University, Stanford, United States.
Ellen WangDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States.
James XieDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States.
Stanford University · USUniversity of Bath · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAnesthesiologists 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.

objectivesThis 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.

methodsWe 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.

resultsThe 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.

conclusionA 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.

Indexed as

AnesthesiaAnesthesiologyPhysiciansAnalgesics, OpioidChildHumansAnalgesics, Opioid

Identifiers

PMID35322398
PMCPMC8942721
OpenAlexW4220985549

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.