Evidence map›Paper›PMID 37933789›Full record

ReviewJournal of evaluation in clinical practice2024

Identification and classification of principal features for analyzing unwarranted clinical variation.

Apollo O McOwiti, Wei Tao, Cui Tao

Open access · greenAbstract readReview
In one paragraph

Review in Journal of evaluation in clinical practice, 2024. 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
0.3field-weighted citation impact, top 36% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

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

3 authors at 1 institution in 1 country.

Apollo O McOwitiMcWilliams School of Biomedical Informatics, The University of Texas Health Center at Houston, Houston, USA.ORCID 0000-0001-7804-3631
Wei TaoBiostatistics and Data Science Department, The University of Texas Health Center at Houston, Houston, USA.
Cui TaoMcWilliams School of Biomedical Informatics, The University of Texas Health Center at Houston, Houston, USA.ORCID 0000-0002-4267-1924
The University of Texas Health Science Center at Houston · US

Funding

ACTS (AD Clinical Trial Simulation): Developing Advanced Informatics Approaches for an Alzheimer's Disease Clinical Trial Simulation SystemR01AG084236 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Jiang Bian, Cui Tao · 2023 to 2026
$4.1M
ACTS (AD Clinical Trial Simulation): Developing Advanced Informatics Approaches for an Alzheimer's Disease Clinical Trial Simulation System # R01AG084236NIA NIH HHS R01 AG084236
6 · The paper itself

Abstract

RATIONALE, AIMS, AND

objectiveUnwarranted clinical variation (UCV) is an undesirable aspect of a healthcare system, but analyzing for UCV can be difficult and time-consuming. No analytic feature guidelines currently exist to aid researchers. We performed a systematic review of UCV literature to identify and classify the features researchers have identified as necessary for the analysis of UCV.

methodsThe literature search followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We looked for articles with the terms 'medical practice variation' and 'unwarranted clinical variation' from four databases: Medline, Web of Science, EMBASE and CINAHL. The search was performed on 24 March 2023. The articles selected were original research articles in the English language reporting on UCV analysis in adult populations. Most of the studies were retrospective cohort analyses. We excluded studies reporting geographic variation based on the Atlas of Variation or small-area analysis methods. We used ASReview Lab software to assist in identifying articles for abstract review. We also conducted subsequent reference searches of the primary articles to retrieve additional articles.

resultsThe search yielded 499 articles, and we reviewed 46. We identified 28 principal analytic features utilized to analyze for unwarranted variation, categorised under patient-related or local healthcare context factors. Within the patient-related factors, we identified three subcategories: patient sociodemographics, clinical characteristics, and preferences, and classified 17 features into seven subcategories. In the local context category, 11 features are classified under two subcategories. Examples are provided on the usage of each feature for analysis.

conclusionTwenty-eight analytic features have been identified, and a categorisation has been established showing the relationships between features. Identifying and classifying features provides guidelines for known confounders during analysis and reduces the steps required when performing UCV analysis; there is no longer a need for a UCV researcher to engage in time-consuming feature engineering activities.

Indexed as

Practice Patterns, Physicians'Humansanalytic featuresmedical practice variationunwarranted clinical variation

Identifiers

PMID37933789
PMCPMC11460437
OpenAlexW4388455582

What OpenQuestion holds

Textmetadata
LicenceTDM
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.