# A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
###### Abstract  
Effective inter-hospital coordination is crucial in improving the stroke treatment process and outcomes. The introduction of endovascular thrombectomy (EVT) further emphasized the importance of coordination. Although previous studies considered various clinical data besides stroke in terms of the network structure between hospitals, a majority of these studies performed only quantitative analyses instead of topological analyses. This study proposes a new framework (PatientFlow) for constructing a network based on stroke patient transfer data and performing exploratory analysis. The proposed framework can visualize the network structure among hospitals at the national level and analyze the detailed structure through dynamic queries. The hub-and-spoke structure for each cluster derived through community detection can be compared visually and analyzed quantitatively using network measures. Further, the relationship between regions can be analyzed by aggregating the transfer of patients by province. PatientFlow allows medical researchers to perform an exploratory analysis to understand the network at the national, provincial, and community levels with multiple coordinated views. inter-hospital coordination; community detection; hub-and-spoke structure; stroke patient transfer data; endovascular thrombectomy (EVT) +
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a method for rapidly detecting disease prevalence [8] or a method to construct an optimized network for HAIs from patient transfer data [9]. Although stroke-related network studies [1; 5] used geospatial data for the analysis, they focused on changes in the stroke treatment outcomes following the introduction of EVT or the increase or decrease in the number of patients transferred between hospitals rather than deriving and analyzing a hub-and-spoke structure of communities. Therefore, deriving clear quantitative criteria for well-formed clusters from existing studies was challenging. As a result, it was necessary to perform clustering on the given data and then visually explore the well/poorly formed cases based on domain knowledge by looking at the topology of each cluster. Based on the results of this exploratory analysis, one could examine the network metrics of well/poorly formed clusters and heuristically derive quantitative commonalities for each case. However, there are difficulties in dynamically analyzing partial data of interest (e.g., clusters showing similar network metric values) with the abovementioned approaches since they did not focus on performing exploratory analysis. This study proposes an interactive framework, PatientFlow, for network analysis of stroke care hospitals. It introduces a community-detection algorithm for patient transfer data at the national level to create an environment that facilitates an exploratory analysis by reflecting the geospatial characteristics of hospitals. The contributions of this study are summarized as follows:  
1. Introduction of an integrated visual analysis environment supporting data preprocessing, cleansing, and filtering, allowing users to apply them dynamically;
2. Introduction of three-level exploratory analysis of patient transfers networks (i.e., national, provincial, and community levels) with multiple coordinated views;
3. Introduction of a comparative analysis of commonly used network measures between communities in clinical research;
4. Evaluation of efficacy through case studies with clinical researchers and comparison with previous studies. The rest of this paper is organized as follows. Section 2 reviews the cases of analyzing stroke networks and visually analyzing network data in the healthcare field. We summarize the data covered in this study, describe the data types and preprocessing procedures, and provide an abstraction of the network analysis tasks performed by medical researchers (Section 3). Then, we introduce the visual analysis framework and summarize how the visualization has been constructed to reflect the task analysis results (Section 4).