Visualizing and Interpreting Temporal Care Team Dynamics for Cancer Survival Risk Analysis

Yuhua Huang, Hsiao-Ying Lu, Kwan-Liu Ma

Cancer care is a longitudinal, time-sensitive process requiring coordination among diverse healthcare professionals (HCPs), whose collaboration evolves throughout treatment. Prior studies link care team structure to patient outcomes, but the temporal dynamics of collaboration and their relationship to evolving patient risk remain poorly understood. We introduce a visual analytics framework for modeling and interpreting time-varying care team interactions in oncology. We construct dynamic collaboration networks from electronic health record (EHR)-mediated communication data and use a temporal graph neural network (TGNN) to estimate patient outcome risk over time. We then investigate how changes in care team composition and specialist participation relate to shifts in estimated risk across treatment stages. Our multi-view interactive system combines TGNN-based risk modeling with coordinated visualizations of care team networks, feature-level explanations from a surrogate logistic regression model, and exploratory what-if analysis. Without making causal claims, the system helps analysts examine how risk estimates co-evolve with collaboration patterns and identify care team…