# A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
## 5 Evaluation
### Comparative Analysis with Prior Work  
A network analysis approach is in its early stage in clinical research after the appearance of EVT, and visualization emerged as an essential part of studying hospital coordination. Still, the prior work left a possibility to reach a higher level of maturity in contrast to the information visualization domain. To the best of our knowledge, this is the first paper to propose an interactive framework for the analysis of stroke care hospitals. Moreover, some research used commercial tools in the analysis process, which made quantitative comparison (e.g., task completion time, error rate) with our method difficult. Thus, we compared our work with previous research regarding data, analysis tasks, analyzed network levels, research focus, and visual representations (Table 3).  
Prior work focused on analyzing patient transfer patterns (e.g., transfer volumes, the number of connections between hospitals, and the existence of community) and some related them to clinical variables (e.g., the number of performed treatments and clinical outcomes). Regarding the data, it ranged from a single state (or province) to a whole nation. The number of patients varied according to the target disease and collection period. The number of hospitals was primarily affected by the collection coverage. The research focus determined the analysis task. As a result, the previous studies partially conducted the analysis compared to ours. However, the case study participants generally agreed that the capability to perform additional tasks would benefit the prior work. Some studies investigated network changes over time, which this study did not cover; we only had access to patient transfer data within a single interval. We covered this limitation as a future research direction in the next section. Regarding the analyzed network levels, two previous works collected nationwide data, but only one examined the network in multiple scales. Even with the limited data coverage, some studies [1; 5; 11] tried to investigate networks at a community level.  
Our study took a step forward from the prior work with multiple coordinated views. Most studies used node-link diagrams with geographic layouts to present spatial relationships between the hospitals and used only a few additional visualizations for analysis. Moreover, some studies [5; 11; 13] relied on external tools (e.g., ArcGIS, Gephi, and Google Maps) to show network structures. Thus, introducing and coordinating additional visualizations for multi-perspective analysis was challenging. PatientFlow can even apply data cleansing and filtering dynamically during a study. For instance, prior work [1] pointed out that link distances longer than 96 miles between hospitals can be an outlier since stroke requires immediate treatment. The user can interactively adjust the threshold and conduct a follow-up investigation immediately with PatientFlow.  
Figure 7: Comparative cluster analysis view with multiple network measures. In this example, a user selected five clusters (i.e., cluster ID 1, 3, 5, 11, and 14). (**a**) The centralization degree for five clusters shows identical values. (**b**) However, inter-cluster densities vary among the five clusters. (**c**) Users can select multiple measures to analyze them with a stacked bar chart.