Abstract
Objectives As 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.
Methods We 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.
Results In 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.
Conclusion In 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.
Keywords
data visualization - comprehension - patient engagement - health literacy - consumer
health information
