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Advancing Methods to Study Clinical Variation in Healthcare.

Jason E Black1,2, Derek S Chew1,3, Tyler S Williamson1,2,4

  • 1Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

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|February 19, 2026
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Summary
This summary is machine-generated.

Understanding clinical variation, or differences in medical practice, is key to improving patient care. This review guides researchers on methods to describe and quantify this variation to identify and address inefficiencies.

Keywords:
clinical variationfunnel plotshealth system performancemedical practice variationmethodsmultilevel models

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Area of Science:

  • Health Services Research
  • Clinical Epidemiology
  • Biostatistics

Background:

  • Clinical variation, also known as medical practice variation, refers to differences in patient care and outcomes across various dimensions.
  • Understanding unexplained clinical variation is crucial for identifying overuse, underuse, inefficiencies, and inequities in healthcare.
  • Existing literature lacks a comprehensive characterization of methods used to study clinical variation.

Purpose of the Study:

  • To provide non-technical methodological guidance for researchers on describing and quantifying clinical variation.
  • To characterize and compare various methods for analyzing clinical variation.
  • To serve as a resource for examining clinical variation in health services research.

Main Methods:

  • Visualizations: Point and jitter plots, scatterplots, caterpillar plots, box plots, and funnel plots are presented to display clinical variation.
  • Statistical Approaches: Basic measures like variance, standard deviation, coefficient of variation, and interquartile range are discussed.
  • Multilevel Models: Advanced methods are described to measure variation magnitude, identify contributing levels, and explore explanatory factors using statistics like the intraclass correlation coefficient and odds ratios.

Main Results:

  • Funnel plots can identify variation exceeding expected levels after adjusting for case-mix.
  • Multilevel models quantify variation and identify factors at different levels (patient, provider, hospital) that explain it.
  • Key considerations include appropriate measurement of care/outcomes, identifying warranted vs. unwarranted variation, and potential interventions.

Conclusions:

  • This review offers an overview of techniques for describing and quantifying clinical variation.
  • It aims to equip researchers with the knowledge to effectively analyze and understand differences in medical practice.
  • The guidance supports efforts to improve healthcare quality by addressing unwarranted clinical variation.