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

doi:10.1055/s-0039-1697592

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2019; 10(04): 751-770
DOI: 10.1055/s-0039-1697592

Special Topic: Visual Analytics in Healthcare

Georg Thieme Verlag KG Stuttgart · New York

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

Authors

Meghan Reading Turchioe

¹Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States
Annie Myers

¹Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States
Samuel Isaac

¹Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States
Dawon Baik

²Columbia University School of Nursing, New York, New York, United States
Lisa V. Grossman

³Department of Biomedical Informatics, Columbia University, New York, New York, United States
Jessica S. Ancker

¹Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States
Ruth Masterson Creber

¹Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States

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

Full Text

References

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