ArticleJournal of the American Medical Informatics Association : JAMIA2024
Do you want to promote recall, perceptions, or behavior? The best data visualization depends on the communication goal.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed, 20 citations in OpenAlex.
- Using Separate Single-Outcome Risk Presentations Instead of Integrated Multioutcome Formats Improves Comprehension in Discrete Choice Experiments.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Trial
- Accurate is not enough: select formats for communicating probabilities to achieve specific outcomes.BMJ evidence-based medicine · 2026Article
- Building Public Health Data Dashboards: Tutorial Playbook.JMIR public health and surveillance · 2026Article
- Black Parent's Perceptions of Discussing Cardiovascular Disease Risk with Primary Care Clinicians: a Qualitative Study.Journal of racial and ethnic health disparities · 2026Article
- The superiority of graphics over text in long-term memory retention.Psychonomic bulletin & review · 2025Article
- Testing a Novel Design Framework for Patient-Facing Machine Learning-Based Predictions of Heart Failure Decompensation.JACC. Advances · 2025Article
- How to Report Research on the Communication of Health-Related Numbers: The Research on Communicating Numbers (ReCoN) Guidelines.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Evidence on Methods for Communicating Health-Related Probabilities: Comparing the Making Numbers Meaningful Systematic Review to the 2021 IPDAS Evidence Paper Recommendations.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Communicating cancer treatment with pictogram-based timeline visualizations.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Design of patient-facing immunization visualizations affects task performance: an experimental comparison of 4 electronic visualizations.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Health literacy, numeracy, graph literacy, and digital literacy: an overview of definitions, evaluation methods, and best practices.European journal of cardiovascular nursing · 2024Review
- Advancing the science of visualization of health data for lay audiences.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- How to Make Sense of the Making Numbers Meaningful Systematic Review.MDM policy & practiceArticle
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Data visualizations can be effective and inclusive means for helping people understand health-related data. Yet numerous high-quality studies comparing data visualizations have yielded relatively little practical design guidance because of a lack of clarity about what communicators want their audience to accomplish. When conducting rigorous evaluations of communication (eg, applying the ISO 9186 method), describing the process simply as evaluating "comprehension" or "interpretation" of visualizations fails to do justice to the true range of outcomes being studied. We present newly developed taxonomies of outcome measures and tasks that are guiding a large-scale systematic review of the health numbers communication literature. Using these taxonomies allows a designer to determine whether a specific data presentation format or feature supports or inhibits the desired audience cognitions, feelings, or behaviors. We argue that taking a granular, outcomes-based approach to designing and evaluating information visualization research is essential to deriving practical, actionable knowledge from it.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.