JMIR Publications

ABSTRACT

Background:

The visual analysis and delivery of data in the form of visualizations is of great importance in healthcare, as such forms of presentation can reduce errors and improve care, and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating focus on underlying and hidden patterns.

Objective:

Aim of this review is to give an overview of visualization techniques of time-oriented data in health care supporting the comparison of patients. We systematically collect literature and report on visualization techniques supporting the task of comparing time-based datasets of single patients with those of multiple patients or their cohorts, and summarize the usage of these techniques. Visualization techniques are grouped according to the medical characteristics and other relevant visualization aspects like data types, interactions and tasks.

Methods:

This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding.

Results:

Out of 249 screened articles, we identified 22 fitting all criteria, and reviewed these in-depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization technique, interaction technique, and supported tasks. The extracted articles were published between 2003 and 2019, and mostly situated in clinical research. The systems use a wide range of visualization techniques, most frequently showing some change over time. Timelines and temporal line charts occur eight times each, followed by histograms with seven occurrences and scatterplots with five. We report on the findings quantitatively through visual summarization, and qualitatively.

Conclusions:

The articles under review in general mitigate complexity through visualization and support diverse medical objectives. We identified three distinct patient entities: single patients, multiple patients, and cohorts. Cohorts typically are visualized in condensed form either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contains finer details. All systems provide mechanisms to view and compare patient data. Explicitly comparing a single patient to multiple patients or a cohort, however, is supported only by a few systems. These systems mainly use basic visualization techniques with some employing novel visualizations tailored for a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and argue for further research in a systematic review, and the usefulness of a design space.