Visualization of Age Distributions as Elements of Medical Data-Stories

TL;DR

The study tackles how to present disease age distributions within narrative medical visualizations to improve public health understanding. It employs an adapted Double Diamond process, analyzing 18 visualizations, conducting a 72-person user study, and obtaining three expert reviews to build a 40-design-choice DC system, focusing on pictogram usage. Key findings show that annotations excel for comprehension and aesthetics, while traditional bar charts outperform for engagement; pictograms as bars or annotations also enhance memorability in different ways. The work provides actionable design recommendations for health communication and outlines avenues for expanding the design space, including interactive and immersive visualization approaches for future work.

Abstract

In various fields, including medicine, age distributions are crucial. Despite widespread media coverage of health topics, there remains a need to enhance health communication. Narrative medical visualization is promising for improving information comprehension and retention. This study explores the most effective ways to present age distributions of diseases through narrative visualizations. We conducted a thorough analysis of existing visualizations, held workshops with a broad audience, and reviewed relevant literature. From this, we identified design choices focusing on comprehension, aesthetics, engagement, and memorability. We specifically tested three pictogram variants: pictograms as bars, stacked pictograms, and annotations. After evaluating 18 visualizations with 72 participants and three expert reviews, we determined that annotations were most effective for comprehension and aesthetics. However, traditional bar charts were preferred for engagement, and other variants were more memorable. The study provides a set of design recommendations based on these insights.

Figures (7)

• Figure 1: Modified form of the Double Diamond process DouDiam to visualize the methodological approach.
• Figure 2: Process of the Research phase in the Problem Space.
• Figure 3: Illustrative overview of the DC system with the different dimensions.
• Figure 4: Process of low-fidelity wireframes to final implementation of the three pursued ideas for breast cancer (first row), salmonellosis (second row) and BPD (third row). Left column: low-fidelity wireframes from the Scribble phase in the Solution Space. Middle Column: Wireframes with specific design aspects like color from the Selection phase in the Solution Space. Right column: Final visualizations implemented.
• Figure 5: SPLIT visualization of BPD for DC Variant C. It can be observed that for the age groups 10-24 and 50-69, the pictograms partially protrude.