Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review

Abstract

Background: Visual analysis and data delivery in the form of visualizations are of great importance in health care, 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 a focus on underlying and hidden patterns.

Objective: This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques.

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: Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them 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 techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively.

Conclusions: The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.

Keywords: cohorts; comparison; information visualization; multiple patients; patient data; single patients; time-oriented data; visual analytics; visualization systems.

Figure 1

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram showing the identification, screening, and inclusion of articles.

Figure 2

Visualization techniques in the selected articles. Each dot indicates the existence of the technique in a system, with full saturated dots representing the application of the technique for the explicit task of comparison. References within the gray background were identified to support the comparison of multiple patients (single-to-cohort or single-to-multiple). Colors indicate the visualization category, with the bars on the right showing the distribution of the techniques. Bars at the bottom represent the number of techniques identified for each article. Techniques are sorted based on the number of occurrences, and articles are sorted based on the year of publication.

Figure 3

Identified tasks (actions and targets) in the included articles. The plot shows the tasks as actions (analyze, query, and search) and targets (all data, attributes, and network data) that could be completed by visualization systems presented in the articles in our selection. For the categorization of tasks, we used the taxonomy by Munzner [6]. Gray backgrounds indicate articles where patients (single-to-cohort or single-to-multiple) could be compared. Bars on the right-hand side represent the number of articles that used the displayed task category.

Figure 4

Interaction techniques identified in all included articles. Dark blue dots indicate the explicit application of interaction for the task of comparison. Light blue dots indicate the existence of interactions in the system. References within a gray background were identified to support the comparison of multiple patients (single-to-cohort or single-to-multiple).

Figure 5

Examples showing line charts as the primary visualization technique for change over time. Use of a line chart to display the cohort (mean, maximum, and minimum boundaries) and a single selected patient (top left) (reproduced from Klimov and Shahar [15], an Open Access article). Use of a line chart to display multiple patients (unselected, selected, and queried individuals) (center left) (reproduced from Stubbs et al [35], an Open Access article). Use of a line chart to display and compare 2 cohorts (mean and quantiles) over time (bottom left). In addition, selected single patients are displayed below as Priestley timelines (reproduced from Rogers et al [36], an Open Access article, which is published under Creative Commons Attribution 4.0 International License [53]). Use of a line chart to display multiple patients (top right). In addition, small multiples (line charts) display more parameters for a selected patient (reproduced from Wildefire et al [34], with permission from Springer, conveyed through Copyright Clearance Center, Inc). Another view in the system in shows Priestley-like timelines (dot-stripe) for individual patients (bottom right) (reproduced from Wildefire et al [34], with permission from Springer, conveyed through Copyright Clearance Center, Inc).

Figure 6

Examples of histograms to display distribution over time. Use of a histogram to display the occurrences of procedures before and after an event (left) (reproduced from Wang et al [16], with permission from IEEE). Use of histograms to display the distribution for a selected measurement of a cohort and an individual (center). Additionally, line charts display the raw data (reproduced from Kamaleswaran et al [40], with permission from the authors). Use of a histogram as an interactive filter (right) (reproduced from van Dortmont et al [33], with permission from the authors).

Figure 7

Examples of scatterplots to display correlation. Use of a scatterplot to display a 2D projection of values of a cohort and a single patient (top left) (reproduced from Borhani et al [38], with permission from IEEE). Use of a scatterplot to display measurements over time for multiple patients (top center) (reproduced from Gschwandtner et al [18], with permission from IEEE). A scatterplot to show similarity scores across multiple patients (top right) (reproduced from Stubbs et al [35], an Open Access article). Use of a scatterplot (bubble chart) to display the positive and negative outcome contributions of a selected sequence of procedures (bottom left) (reproduced from Gotz et al [42], with permission from the authors). A scatterplot to display a 2D projection of event co-occurrences (bottom center) (reproduced from Guo et al [43], with permission from IEEE). A scatterplot to display occurrences and duration of consultation events (bottom right) (reproduced from Magallanes et al [44], with permission from IEEE).

Figure 8

Schematic overview of the possible comparisons between different patient entities ranging from single patient to multiple patients and cohorts. Multiple-to-cohort emphasizes the distinction between the visual representation of multiple patients versus an aggregated view of the cohort.

Figure 9

All possible combinations for comparing 1 measurement over time between different patient entities (single patient, multiple patients, and cohorts) in the case of line charts. For single and multiple patients, each line represents 1 measurement, whereas, for cohorts, the chart represents the mean and range. All combinations are shown in juxtapositioned and superpositioned layouts, with the colors supporting legibility in the latter.