Evidence map›Paper›PMID 41709497›Full record

ReviewJournal of evaluation in clinical practice2026

Advancing Methods to Study Clinical Variation in Healthcare.

Jason E Black, Derek S Chew, Tyler S Williamson, Braden Manns, Amity Quinn

Abstract readReview
In one paragraph

Review in Journal of evaluation in clinical practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Jason E BlackDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.ORCID 0000-0002-3762-3231
Derek S ChewDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Tyler S WilliamsonDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Braden MannsDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
Amity QuinnDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical variation (a.k.a. medical practice variation) describes how patient care and outcomes differ across patients, providers, hospitals, geographic regions or other dimensions. By understanding clinical variation in patient care or outcomes that cannot be explained by patient differences, researchers and health systems can (1) highlight overuse, underuse, inefficiencies or inequities impacting patient care, (2) identify the level (e.g., patient, provider, hospital) contributing the most variation and (3) interrogate the reasons that explain the observed variation. Numerous methods exist to understand clinical variation; however, these are not well identified or characterized in the existing body of health services research literature. We aim to provide non-technical methodological guidance to researchers interested in describing and quantifying clinical variation by characterizing and comparing methods suited to this task. We present several plots to display clinical variation, including point and jitter plots, scatterplots, caterpillar plots, box plots and funnel plots. While most plots simply visualize the variation, funnel plots can characterize whether the variation is larger than expected after adjusting for relevant clinical factors that might reasonably explain clinical variation, such as case-mix. We present several basic statistical approaches to measure clinical variation, including variance, standard deviation, coefficient of variation, systematic coefficient of variation and interquartile range. Further, we describe multilevel models that measure clinical variation beyond its magnitude, including the level(s) at which variation occurs and the level-specific factors that explain the variation. We describe several statistics that quantify the clinical variation that exists in a multilevel model, such as the intraclass correlation coefficient and median odds, risk, rate and hazard ratios. We discuss key considerations when describing and quantifying clinical variation, including the appropriate measurement of patient care and outcomes, the factors explaining variation and whether it is warranted and what interventions might help reduce the variation. The objective of this review is to provide an overview of techniques to consider when describing clinical variation, serving as a resource for those examining clinical variation.

Indexed as

Delivery of Health CareHealth Services ResearchHumansclinical variationfunnel plotshealth system performancemedical practice variationmethodsmultilevel models

Identifiers

PMID41709497
PMCPMC12917294

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.