# A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
## 4 PatientFlow: The Visual Analytics Framework
### Overview of Patient Transfer Network
One can click on a hospital in any chart to locate it in the topology view. During data analysis, we found far more stay cases than the other types (i.e., transfer-in or transfer-out). As a result, the number of stay cases hindered the visual examination of patient distribution for transferred patients. Thus, we placed a legend at the top right corner, which the user could toggle the visibility of each patient type by clicking an item in the legend. We also enabled grouping bars by provinces and changing the order of hospitals by the number of patients: ascending/descending order of stay, transfer-in, or transfer-out patients. One can also zoom in to narrow the analysis scope by brushing the chart. As there are more than 1000 hospitals, one might have to drill down several times by consecutive brushings. When a user brushes several times, buttons appear next to the title on top, indicating each drill-down step. One can click one of them to roll back to that state. If one finds a hospital of interest after brushing and browsing, one can locate it in another view by clicking on the bar. A user can change the mouse interaction mode (i.e., clicking or brushing) by toggling the button in the bottom right corner. Task analysis results indicated that the researchers demanded multi-perspective analysis with quantitative measures as well as analysis on topology and geographical distribution (T1). We placed several interactive charts on the left side of the overview area to comply with this demand (Figure 2a). With a histogram of link distance, a user can analyze the distribution and filter links by brushing the chart. A boxplot of link distance for each province depicts the distribution and outliers (Figure 3). Regarding inter-province transfers, we count them based on the origin province. We assumed that the origin of long-distance transfer would be more interesting than the destination, as it might indicate a poor local network at the origin province. For instance, KAW province has a comparably significant deviation. When one clicks the outlying circle, the corresponding link becomes highlighted in red (Figure 3). One can also perform a similar examination at the cluster level with a brushable bar chart. As some clusters only have a couple of hospitals as members, visualizing with a boxplot was not applicable in some cases. Thus, we introduced a bar chart showing each cluster's longest link distance (Figure 4a). We also prepared other bar charts depicting network measures of hub nodes and clusters (i.e., measures in Table 2). For example, there is a bar chart for degree centrality (Figure 4b), where the color strip on the right shows the value range for the primary hub (red), secondary hub (orange and yellow), and spoke hospital. It could aid in selecting each class of hospital. We also included a bar chart (Figure 2a) to visualize the number of hospitals by size and SCH designation status (e.g., A-SCH, B-SCH, B, C, and D). Figure 3: Box plot of the patient transfer distance aggregated by province. One can click the outlier dots in the box plot to highlight the transfer link in the map.