CPViz: Visualizing clinical pathways represented in higher-order networks

To improve clinical care practice, it is important to understand the variability of pathways executed in different contexts (e.g., geographical locations, demographics, and phenotypic groups). A common way representing through network-based representations that capture trajectories treatment steps. However, first-order networks, which are based on Markovian property de facto standard model represent transitions between steps, often fail real trajectories. This paper introduces a visual analytic tool explore compare represented higher-order networks. Because each higher node network subtrajectory (i.e., partial or full history steps), can display true sequences steps compute similarity two networks space nodes. The also highlights areas similar dissimilar how certain realized differently pathways. demonstrates tool's usefulness by applying multiple antidepressant pharmacotherapy for veterans diagnosed with major depressive disorder illustrating heterogeneity prescription patterns across