Evidence map›Paper›PMID 31597182›Full record

SynthesisApplied clinical informatics2019

A Systematic Review of Patient-Facing Visualizations of Personal Health Data.

Meghan Reading Turchioe, Annie Myers, Samuel Isaac, Dawon Baik, Lisa V Grossman, Jessica S Ancker, Ruth Masterson Creber

Abstract readSystematic Review
In one paragraph

Synthesis in Applied clinical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 4 pooled it
–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

54 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  3. Integrating Shared Decision-Making into Undergraduate Oncology Education: A Pedagogical Framework.Journal of cancer education : the official journal of the American Association for Cancer Education · 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

7 authors.

Meghan Reading TurchioeDivision of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States.
Annie MyersDivision of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States.
Samuel IsaacDivision of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States.
Dawon BaikColumbia University School of Nursing, New York, New York, United States.
Lisa V GrossmanDepartment of Biomedical Informatics, Columbia University, New York, New York, United States.
Jessica S AnckerDivision of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States.
Ruth Masterson CreberDivision of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States.

Funding

Making Numbers Meaningful: Promoting Evidence-based Communication of Numbers in HealthR01LM012964 · NLM · WEILL MEDICAL COLL OF CORNELL UNIV · PI ANCKER, JESSICA S. · 2018 to 2021
$1.4M
Should we return patient reported outcomes to human subjects participants?-exploring ethical obligationsR00NR016275 · NINR · WEILL MEDICAL COLL OF CORNELL UNIV · PI MASTERSON CREBER, RUTH MARIE · 2018 to 2020
$896k
NINR NIH HHS R00 NR016275NLM NIH HHS R01 LM012964
6 · The paper itself

Abstract

objectivesAs personal health data are being returned to patients with increasing frequency and volume, visualizations are garnering excitement for their potential to facilitate patient interpretation. Evaluating these visualizations is important to ensure that patients are able to understand and, when appropriate, act upon health data in a safe and effective manner. The objective of this systematic review was to review and evaluate the state of the science of patient-facing visualizations of personal health data.

methodsWe searched five scholarly databases (PubMed, Embase, Scopus, ACM Digital Library [Association for Computing Machinery Digital Library], and IEEE Computational Index [Institute of Electrical and Electronics Engineers Computational Index]) through December 1, 2018 for relevant articles. We included English-language articles that developed or tested one or more patient-facing visualizations for personal health data. Three reviewers independently assessed quality of included articles using the Mixed methods Appraisal Tool. Characteristics of included articles and visualizations were extracted and synthesized.

resultsIn 39 articles included in the review, there was heterogeneity in the sample sizes and methods for evaluation but not sample demographics. Few articles measured health literacy, numeracy, or graph literacy. Line graphs were the most common visualization, especially for longitudinal data, but number lines were used more frequently in included articles over past 5 years. Article findings suggested more patients understand the number lines and bar graphs compared with line graphs, and that color is effective at communicating risk, improving comprehension, and increasing confidence in interpretation.

conclusionIn this review, we summarize types and components of patient-facing visualizations and methodologies for development and evaluation in the reviewed articles. We also identify recommendations for future work relating to collecting and reporting data, examining clinically actionable boundaries for diverse data types, and leveraging data science. This work will be critically important as patient access of their personal health data through portals and mobile devices continues to rise.

Indexed as

Health Records, PersonalUser-Computer InterfaceData MiningHumans

Identifiers

PMID31597182
PMCPMC6785326

What OpenQuestion holds

Textmetadata
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.