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

Meghan Reading Turchioe
1  Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States
,
Annie Myers
1  Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States
,
Samuel Isaac
1  Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States
,
Dawon Baik
2  Columbia University School of Nursing, New York, New York, United States
,
Lisa V. Grossman
3  Department of Biomedical Informatics, Columbia University, New York, New York, United States
,
Jessica S. Ancker
1  Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States
,
Ruth Masterson Creber
1  Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medical College, New York, New York, United States
› Author Affiliations
Funding Research reported in this publication was primarily supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number R00NR016275 (mHealth for Heart Failure Symptom Monitoring; PI: Masterson Creber).
Further Information

Publication History

15 April 2019

02 August 2019

Publication Date:
09 October 2019 (online)

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.

Protection of Human and Animal Subjects

This project did not involve human patients' research.