To provide sufficient healthcare to patients, it is important to visualize patient journey(s), i.e., the journey from sickness to recovery. However, current visualization tools do not allow us to imagine patient journeys at both the individual and aggregate levels. In this paper, we aim to understand patient journeys via powerful visualization charts, that help mine patterns in Big-Data relating to patients at both the individual and aggregate levels. We developed a Patient Journey Visualizer (PJV) tool that can help in visualizing patient journeys via Parallel Coordinates, Sankey, and Sunburst charts. Parallel Coordinates assists in visualizing multivariate data concerning patient journeys in PJV at the individual level. Sankey charts help in visualizing the aggregate flow of patients between various phases of patient journeys in PJV. Sunburst charts represent hierarchical relationships between diagnoses, procedures, and prescription medications in PJV. Different visualization charts in PJV were compared across increasing number of data points. Results revealed that the Parallel Coordinates chart took less time to render compared to the Sankey and Sunburst charts when dataset size increased. The main implications of our findings are for improving healthcare by providing useful visualizations of patient journeys.