Evidence map›Paper›PMID 32326895›Full record

ArticleBMC medical research methodology2020

What was visualized? A method for describing content of performance summary displays in feedback interventions.

Dahee Lee, Veena Panicker, Colin Gross, Jessica Zhang, Zach Landis-Lewis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Precision feedback: A conceptual model.Learning health systems · 2024
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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.

Dahee LeeDepartment of Learning Health Sciences, University of Michigan Medical School, 1161J NIB, 300 N., Ingalls Street, SPC 5403, Ann Arbor, MI, 48109-5403, USA.
Veena PanickerDepartment of Learning Health Sciences, University of Michigan Medical School, 1161J NIB, 300 N., Ingalls Street, SPC 5403, Ann Arbor, MI, 48109-5403, USA.
Colin GrossDepartment of Learning Health Sciences, University of Michigan Medical School, 1161J NIB, 300 N., Ingalls Street, SPC 5403, Ann Arbor, MI, 48109-5403, USA.
Jessica ZhangDepartment of Learning Health Sciences, University of Michigan Medical School, 1161J NIB, 300 N., Ingalls Street, SPC 5403, Ann Arbor, MI, 48109-5403, USA.
Zach Landis-LewisDepartment of Learning Health Sciences, University of Michigan Medical School, 1161J NIB, 300 N., Ingalls Street, SPC 5403, Ann Arbor, MI, 48109-5403, USA. zachll@umich.edu.

Funding

A knowledge-based message tailoring systemK01LM012528 · NLM · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LANDIS-LEWIS, ZACHARY · 2017 to 2019
$523k
NLM NIH HHS K01 LM012528U.S. National Library of Medicine 1K01LM012528-01
6 · The paper itself

Abstract

backgroundVisual displays such as charts and tables may significantly moderate the effects of audit and feedback interventions, but the systematic study of these intervention components will likely remain limited without a method for isolating the information content of a visual display from its form elements. The objective of this study is to introduce such a method based on an application of visualization frameworks to enable a systematic approach to answer the question, "What was visualized?" in studies of audit and feedback.

methodsThe proposed method uses 3 steps to systematically identify and describe the content of visual displays in feedback interventions: 1) identify displays, 2) classify content, and 3) identify elements. The use of a visualization framework led us to identify information content types as representations of measures (metrics or indicators), ascribees (feedback recipients and comparators), performance levels, and time intervals. We illustrate the proposed method in a series of 3 content analyses, one for each step, to identify visual displays and their information content in published example performance summaries.

resultsWe analyzed a convenience sample of 44 published studies of audit and feedback. Through each step, two coders had good agreement. We identified 42 visual displays of performance, containing 6 unique combinations of content types. What was visualized most commonly in the sample was performance levels across a recipient and comparators (i.e. ascribees) for a single measure and single time interval (n = 16). Content types varied in their inclusion of measures, ascribees, and time intervals.

conclusionsThe proposed method appears to be feasible to use as a systematic approach to describing visual displays of performance. The key implication of the method is that it offers more granular and consistent description for empirical, theoretical, and design studies about the information content of feedback interventions.

Indexed as

BenchmarkingFeedbackResearch DesignData VisualizationHumansAudit and feedbackClinical quality improvementContent analysisVisualization

Identifiers

PMID32326895
PMCPMC7181510

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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.