# A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
## 4 PatientFlow: The Visual Analytics Framework
### Overview of Patient Transfer Network  
The overview area shows the distribution of hospitals, connectivity between hospitals, and quantitative measures depicting the network structure. We used two layout methods to place the hospitals and visualize links between them: force-directed and geographic layout (T1). Since the nodes in the force-directed layout push the others away, a user can observe the community detection result without visual clutters from overlapping nodes [30]; however, it is difficult to reflect the geographical location. Geographic layout might result in overlapped nodes if there are multiple closely located hospitals. Thus, we employed both methods so one can toggle between the options by clicking the icons in the top left corner of the topology view (Figure 2b). In the map, the node's shape indicates the type of the hospital (i.e., circle for SCH and square for non-SCH). It aids analysis of the geographical distribution by hospital types. In such cases, one could hide the links between the nodes to minimize occlusion. On the contrary, one could only leave links in the view and hide node markers to focus on connection density. For each cluster derived from the community-detection results, we used bubble sets [28] to show the assumed coverage. (T4) It could show the geographical size of the cluster more clearly than simply coloring the node markers and could also aid in estimating the catchment area. However, the patient's address or point of departure when visiting the hospital was missing from our data. Thus, it does not represent accurate catchment areas as in [2], where [PERSON] et al. assessed the accessibility of hospitals by region and visualized it. Still, it is possible to indirectly identify regions not participating in the treatment network for stroke patients. Moreover, any patient transfer between distant hospitals can be confirmed immediately through more giant bubbles spanning several areas. The total number of patients that passed through each hospital was another aspect that collaborating researchers wanted to examine besides topology (T5). For each hospital, there are patients transferred to another hospital (transfer-out), transferred from other hospitals (transfer-in), and received treatment without being transferred (stay). The researchers wanted to examine the distribution of all three kinds of patients. Therefore, we included a view showing the number of patients per hospital with a stacked bar chart under the topology view (Figure 2c). The color encoding is kept consistent throughout the system, with blue, orange, and red indicating transfer-in, transfer-out, and stay, respectively. Figure 2: PatientFlow interface. (**a**) A list of interactive charts presents network characteristics. One can filter or highlight items by brushing or pointing. Patient transfer distances less than 150 km are selected, and one can confirm the applied filter with chips on top of the screen. (**b**) The topology view presents hospitals and patient transfers between them on a map. (**c**) The bar chart depicts the number of patients for each hospital in three categories (i.e., transfer-in, transfer-out, and stay). (**d**) A list of clusters yielded from a community detection algorithm is presented in three visualizations (node-link diagram, chord diagram, and bar chart). Each is designed to support analyzing topology, patient transfer patterns, and the number of patients for each hospital. The total number of clusters and selected clusters are shown on top of the view (e.g., 5 out of 95 clusters are selected) (**e**) The comparison view supports quantitative comparison between the selected clusters. One can select a network measure from the list on the left to make a comparison. (**f**) The province view presents patient transfers between provincial-level regions with a directed node-link diagram and a diverging bar chart. Depending on the transfer direction, the number of patients is colored in blue or orange. A table and a bar chart on the bottom show the hub hospitals within the region.