# A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
## 5 Evaluation  
### Case Study  
We conducted a case study with two neurologists and a clinical statistician to evaluate the effectiveness of the proposed visualization tool. Since these researchers intended to derive clinical insights such as EVT outcome and mortality rate based on the findings through this framework, they attempted to use the tool in their own space. Moreover, due to the COVID-19 pandemic, we had to conduct the study online. Thus, we deployed the tool to the experts and encouraged to use it for a couple of weeks and then interviewed them for an hour. One of the common requests of the participants was verifying whether community detection was successful. They could validate the results by comparing the three density values in Table 2 and checking the modularity calculated from the Louvain algorithm. The validation result indicated that the communities were well derived. Then, they tried to examine the derived structure visually. At first, they used a node-link diagram, but thousands of nodes caused excessive visual clutters. Thus, they switched to a geographical layout to analyze distributions on a map and used the bubble sets to examine the community's coverage (Figure 2b). Since the bubbles were semi-transparent, the participants could notice that the east and southwest regions had a relatively small number of clusters. They found several outlying clusters with long-distance transfers by hovering a cursor on the bubbles. After a few filtering interactions in the distance bar chart (Figure 2b), they concluded that excluding links over 150 km of transfer distance would be appropriate for their dataset. A priori, neurologists expected that patient transfer in a specific province would show different patterns compared to other regions. They tried to verify this widely known expectation. In the province view, they browsed patient transfers for each region and noticed singularities in some province pairs. Most provinces showed mutual patient transfers between them, even if the number of patients for each direction were somewhat biased toward one side. However, visual analysis with a node-link diagram and diverging bar chart identified some province pairs (e.g., CLN and KWJ) with dominant transfer directions (Figure 6a,b). In order to take a closer look, they clicked hospital ID 425 in the table (Figure 6c), which showed the highest number of patients among the SCHs in the CLN province. As they checked the belonging cluster, they found that the hospital received patients from the rest of the cluster, resulting in a degree centrality of 1.0. In the geographical map, they could find a reason for this discrepancy hospitals sent patients to the hospitals in neighboring provinces. The case study participants observed the potential of applying a network analysis (i.e., analysis of community detection results and network measures) to the cases of hospitals in the Republic of Korea. The preliminary result from the case study led to a clinical research opportunity. In this section, we summarized additional use cases where PatientFlow could aid in finding notable patient transfer patterns. With limited access to medical data, we could not draw clinical implications from the following cases at the time of writing. Thus, we asked the case study participants to review them with their domain knowledge, and they regarded the cases as prominent patterns for future research. In the bar chart of hospital types (Figure 2a), one could notice that all quaternary hospitals (i.e., A) are SCHs, about 75% of tertiary hospitals (i.e., B) are SCHs; and none of the secondary (i.e., C) and primary hospitals (i.e., D) are SCHs. One could assume that SCH is designated to lead the care of stroke patients among hospitals nearby. Thus, the user searched for uncommon hospitals with a degree centrality smaller than 0.5 in A-SCH hospitals. The user clicked the A-SCH bar in the bar chart of hospital types to filter out other types.