Patient-centered visualization of multistage cancer treatment trajectories
2026-06-15 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors looked at how to better show cancer treatment plans to patients, especially those who might have trouble understanding complex medical information. They focused on a type of blood cancer called acute myeloid leukemia and created two timeline pictures: one simple and easy to follow, and one interactive with more details. Their study found that the simpler timeline helped patients understand the treatment steps better, while the interactive one made it harder to use and didn’t improve understanding. The authors suggest that clear, straightforward visuals are more helpful than complicated interactive ones when explaining medical timelines to patients.
acute myeloid leukemiaoncology treatmentmedical visualizationhealth literacypatient-centered designtimeline visualizationcognitive psychologyuser experience designinformation visualizationinteractive visualization
Authors
Laura Lackner, Marius Bill, Martin Bornhaeuser, Karolin Trautmann-Grill, Helena Klara Jambor
Abstract
Effective communication of multistage cancer treatment trajectories remains a major challenge, particularly for patients with limited health literacy. We present a patient-centered visualization approach for representing complex, phase-based oncology treatments, integrating principles from information visualization, user experience (UX) design, and cognitive psychology. Using acute myeloid leukemia (AML) as a case study, we developed two timeline-based representations: a static, visually simplified trajectory emphasizing structure and hierarchy, and an interactive variant with layered information. We evaluated both approaches in a quantitative survey, measuring comprehension of treatment sequences, perceived confidence, and information quality. Results show that the static visualization significantly improves understanding and clarity, highlighting the importance of visual hierarchy, consistent encoding, and reduced complexity when communicating temporal medical processes compared to the baseline. In contrast, additional interactivity did not improve performance and introduced navigational overhead, suggesting that interaction must be carefully aligned with cognitive demands. Our findings contribute to visualization research by demonstrating how patient-centered design can improve the interpretability of multistage treatment trajectories. We derive design implications for temporal medical visualizations, emphasizing simplicity, structural clarity, and accessibility to support informed decision-making in clinical contexts.