applied

sciences
Article
A Visual Analytics Framework for Inter-Hospital Transfer
Network of Stroke Patients
Kyuhan Kwak, Jinu Park and Hyunjoo Song *

School of Computer Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea
* Correspondence: hsong@ssu.ac.kr; Tel.: +82-2-820-0676

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.

Keywords: inter-hospital coordination; community detection; hub-and-spoke structure; stroke

patient transfer data; endovascular thrombectomy (EVT)

1. Introduction
Citation: Kwak, K.; Park, J.; Song, H.
Improving the process and outcomes of stroke treatment requires effective inter-
A Visual Analytics Framework for
hospital coordination [1]. The introduction of endovascular thrombectomy (EVT) high-
Inter-Hospital Transfer Network of
Stroke Patients. Appl. Sci. 2023, 13,
lighted the significance of coordination even further [2]. The effectiveness of EVT was
5241. https://doi.org/10.3390/
proved in several studies conducted in 2015 [3,4]; however, it was challenging to introduce
app13095241 it in all hospitals. Since only some major hospitals can afford the environment, smaller hos-
pitals transfer patients who need EVT treatment to a capable hospital. Consequently, given
Academic Editors: Il Dong Yun and
this interest in transferring stroke patients, recent research focused on how the network
Walid Abdullah Al
between hospitals is structured [1,5].
Received: 14 February 2023 Despite the interest and importance, information on network structure for stroke treat-
Revised: 16 April 2023 ment is unknown and still needs to be studied [6]. In the Republic of Korea, 70 medium
Accepted: 19 April 2023 catchment areas have been established for inter-hospital coordination, and they are man-
Published: 22 April 2023 aged along with 17 large catchment areas centered on tertiary hospitals [7]. However,
there could be differences between the catchment area established by the government
and the service area in which healthcare institutions occupy the market through mutual
competition. Thus, there are ongoing discussions between policymakers of central and
Copyright: © 2023 by the authors. local governments about determining the catchment areas and inspecting whether the
Licensee MDPI, Basel, Switzerland.
hospitals within the areas are coordinating with each other [7].
This article is an open access article
Although previous studies considered various clinical problems besides stroke in
distributed under the terms and
terms of the network structure between hospitals, most of these studies performed only
conditions of the Creative Commons
quantitative analyses on the related clinical outcomes instead of topological analysis. For
Attribution (CC BY) license (https://
example, studies on hospital-acquired infections (HAIs) analyzed national hospital net-
creativecommons.org/licenses/by/
4.0/).
works in the United States [8] and France [9]. The network structure was used to develop

Appl. Sci. 2023, 13, 5241. https://doi.org/10.3390/app13095241 https://www.mdpi.com/journal/applsci

Appl. Sci. 2023, 13, 5241 2 of 21

a method for rapidly detecting disease prevalence [8] or a method to construct an opti-
mized 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 dy-
namically 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 prepro-
cessing, 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). The content
and results of the case study conducted with domain experts are described, and this is
followed by further discussions. Finally, we conclude with Section 6, recapitulating the
main findings and introducing future work.

2. Related Work
The patient transfer was visually analyzed with node-link diagrams for various clinical
purposes. Fernández-Gracia et al. [8] analyzed the spread of pathogens in the patient
transfer network in the United States. The network structure constructed based on patient
transfers for two years was plotted on a map with the number of transferred patients
indicated by the color of the link. The analysis results were used to introduce a method
for selecting sensor hospitals based on a network measure (i.e., in-degree) and for quickly
detecting disease prevalence based on temporal, geographical, and topological properties.
Nekkab et al. [9] divided the patient group (1) with HAI, (2) with suspected HAI, and
(3) all patients, and they derived the network connection structure based on the patient
transfer pattern for determining the optimal network for studying HAIs. The transfer
pattern was also presented on a map, and the authors identified notable transfer patterns
that could help find a critical hospital at the regional and county level in France. The
nodes in the diagram were colored according to the community derived from the detection
algorithm [10]. Dong et al. [11] used a community detection algorithm [12], which extends
the previous approach [10] for large networks, to analyze the clustering patterns of hospitals
Appl. Sci. 2023, 13, 5241 3 of 21

in China. The authors neglected geographic information in analyzing the network and
plotting the nodes and links. We presented a hospital network using both geographical
layout and force-directed layout algorithms for analysis of geographical relationships and
topological analysis within each cluster, respectively.
A study on patient transfers was conducted regarding treatments for stroke patients.
Adeoye et al. [2] studied the geographic access of stroke patients in the United States.
They argued that a more efficient stroke treatment system was required by analyzing
country-level data on the accessibility to hospitals and the number of patients who received
treatments from the hospitals. Accessibility to treatment facilities was visualized using
a choropleth map. Zachrison et al. [7] argued that the study of networks could help im-
prove stroke treatment systems, and they introduced examples of community-detection
algorithms employed in other medical studies. It was followed by studies that grafted a
network analysis on various regions. For example, Zachrison et al. [5] analyzed stroke
patient transfer data in the northeastern United States and visualized the network us-
ing ArcGIS. Each network constructed based on patient transfer data at two time-points
(2007 and 2011) was expressed as a node-link diagram on a map; hospitals that sent a
significant number of patients and hospitals that received them were analyzed. Another
network analysis of the stroke treatment system was conducted for California [1]. The
two networks constructed based on patient transfer data (between 2013 and 2014; and
2016 and 2017) were visualized on a map to examine changes from the introduction of
EVT; the rate of progression of the EVT treatment and the rate of patient transfer were ana-
lyzed. Meanwhile, a study on telestroke care analyzed the hub-and-spoke structures and
investigated how the network changed over time [13]. Our study analyzed the community
in the network based on patient transfer data using the Louvain algorithm for directed
networks [14]. We provided tailored visualization without commercial tools by identifying
tasks through collaboration with medical researchers.
Prior work explored methods to visualize networks based on the data size (i.e., number
of nodes and links) and the purpose of visualization. Komarek et al. [15] classified the
layout method of placing nodes and links on a screen into seven types: force-directed,
hive plot, adjacency matrix, arc diagram, Sankey diagram, chord diagram, and pivot
graph. They compared the number of nodes and links suitable for network visualization.
Vehlow et al. [16] conducted a survey and analyzed previous studies that visualized the
group structure of graphs. According to the criteria from prior work [15], the number of
nodes (i.e., about 1000 hospitals) and the number of links (i.e., about 2000 patient transfers)
in our study were in the range for a force-directed layout. Nonetheless, we placed the
nodes reflecting their actual locations on a map as it was necessary to support the analysis
considering the geographical location of hospitals. Further, grouping was visualized
through superimposition among the methods summarized in a previous study [16] to
demonstrate the group structure derived through community detection. The map was
designed to reduce the scope of analysis through various interactions. There were many
overlapping nodes with many groups, which cannot be distinguished by color when
visualizing them on the map.
Andrienko et al. [17] surveyed visual analytic research of movement and transporta-
tion systems. Patient transfers in this study are origin-destination (OD) travel data without
start and end times. According to the survey, one commonly aggregates OD data into
matrices or flows for visual analysis. While visualizing flows as lines on a map has been
widely used [18], the following research worked on resolving visual clutters for more
significant numbers of flow. For instance, Wood et al. [19] proposed an OD map in which
each cell represents an OD vector as in an OD matrix. It preserved the spatial layout by
introducing a spatial treemap, and reduced visual clutters caused by intersecting links.
Andrienko et al. [20] worked on long-term flow data and proposed OD flow aggrega-
tion by direction and distance ranges. They used the aggregated flow to cluster time
intervals and presented the flow with diagram maps. Von Landesberger et al. [21] also
adopted spatial simplification of locations to avoid visual clutter in analyzing mass mo-
 Appl. Sci. 2023, 13, x FOR PEER REVIEW 4 of 21

Appl. Sci. 2023, 13, 5241 4 of 21

by direction and distance ranges. They used the aggregated flow to cluster time intervals
and presented the flow with diagram maps. Von Landesberger et al. [21] also adopted
spatial simplification of locations to avoid visual clutter in analyzing mass mobility data.
bility
Vrotsoudata. Vrotsou
et al. et al. [22]direction-based
[22] proposed proposed direction-based filteringthe
filtering to address to problem.
address theTheyproblem.
intro-
They
duced introduced an interactive
an interactive visualization
visualization technique technique called Flowcube,
called Flowcube, whichflows
which presents presents
as
flows as three-dimensional
three-dimensional arcs, andarcs, anddirection-based
offered offered direction-based
filtering tofiltering
reduceto reduce
the the
clutter. clutter.
Yang et
Yang et al.
al. [23] [23] proposed
proposed an interactive
an interactive visual analytics
visual analytics system system for epidemic
for epidemic control control and
and intro-
introduced
duced mouse mouse hover
hover interaction
interaction to focus
to focus on on a specific
a specific regioninstead
region insteadofofvisualizing
visualizingthe the
whole network. We adopted both approaches in visualizing patient
whole network. We adopted both approaches visualizing patient transfers. As spatial transfers. As spatial
information
informationwas wascrucial
crucialfor
for the
the target users, we presented flows flows asas lines
lineson
onaamap
mapwithout
without
any
anyadditional
additionalvisual
visualencodings,
encodings, which
which might
mighthinder the perception
hinder the perception of spatial information.
of spatial infor-
However, we provided
mation. However, interactiveinteractive
we provided filters to narrow the
filters to analysis
narrow thescope andscope
analysis dedicated views
and dedi-
for force-directed
cated node-link diagrams
views for force-directed node-linkwhere
diagrams eachwhere
nodeeachrepresents aggregated
node represents locations
aggregated
(i.e., province).
locations (i.e., province).

3.3.Analysis
Analysisof of Data
Data and
and User
User Task
Task
As
Asthis
thisstudy
study aims
aims to
to propose
propose aa visual
visual analysis framework for
analysis framework for medical
medicalresearchers
researchers
interested
interestedin instroke
stroke patients,
patients, we
we collaborated with two
collaborated with two researchers
researchers (one
(oneneurologist
neurologistandand
one
oneclinical
clinicalstatistician)
statistician) from
from aa quaternary hospital. We
quaternary hospital. We met
met monthly
monthlyfor forthree
threeyears,
years,and
and
they participated in data and task analysis. We independently searched for relevant
they participated in data and task analysis. We independently searched for relevant prior prior
work
workand andreached
reacheda consensus
a consensus ononrelevance in the
relevance monthly
in the monthly meetings.
meetings.They shared
They insights
shared in-
over the literature review, provided domain knowledge to aid in determining
sights over the literature review, provided domain knowledge to aid in determining net- network
metrics, and summarized
work metrics, and summarizedtheir analytic requirements.
their analytic The overall
requirements. framework
The overall frameworkfromfrom
data
and
datauser
andtask
useranalysis results results
task analysis is summarized in Figure
is summarized in1,Figure
and the1, following section describes
and the following section
the results.the results.
describes

Figure 1. Exploratory visual analysis framework for stroke patients (PatientFlow).

Figure 1. Exploratory visual analysis framework for stroke patients (PatientFlow).

3.1.Hospital
3.1. Hospitaland
and Patient
Patient Transfer
Transfer Data
Data
InInthe
theRepublic
RepublicofofKorea,
Korea,thetheHIRA
HIRA (Health
(Health Insurance
Insurance Review
Review andand Assessment
Assessment ser-
service)
vice) accumulates and manages a list of hospitals registered as treatment institutions
accumulates and manages a list of hospitals registered as treatment institutions for specific for
specific diseases, as well as the data related to patient transfers between hospitals,
diseases, as well as the data related to patient transfers between hospitals, their treatmenttheir
treatment and
processes, processes, and outcomes.
outcomes. The data
The data used used byresearchers
by medical medical researchers who conducted
who conducted the joint
study consisted of the transfers of 19,113 stroke patients among 1009 hospitals from 1 July
to 31 December 2016. The data used in this study are summarized in Table 1.
Appl. Sci. 2023, 13, 5241 5 of 21

Table 1. Hospital and patient transfer data.

Category Attribute Description Notes

HID Unique ID for hospital -
Quaternary hospital, tertiary hospital,
Type Type of hospital
secondary hospital, and primary hospital
Address Address of hospital City, county, and district information
Hospital
GPS coordinates calculated
Coordinate Location of hospital
based on the government office
Stroke care hospital
SCH -
designation status
Type Type of patient transfer “Stay” or “transfer and stay”
Hospital ID from which
HID (from) -
the patient is transferred
Patient
Hospital ID to which
transfer HID (to) -
the patient is transferred
Total number of patients transferred
Patient count Number of patients transferred
within the aggregation period

Some data needed to be preprocessed before analysis, and parts of the provided data
were anonymized to prevent the hospital’s or personal information from being identified.
For example, the detailed address information that can specify a hospital in the hospital list
data is not provided, and only the regional administrative district (e.g., cities and provinces)
and second-level administrative district (e.g., cities, counties, or districts) information are
provided. The hospital ID is also randomly assigned to prevent the name of the hospital
from being identified. Accordingly, the GPS coordinates of the government office (e.g., city
hall, county office, or ward office) of the relevant administrative districts are used instead
of the actual location of the hospital to display the location of the hospital on a map. In this
case, random jittering is applied in the process of rendering on the screen to distinguish
them on a map because multiple hospitals within the same administrative district can
overlap. The distance between hospitals is calculated as a straight-line distance based on
the GPS coordinates before jittering is applied.
Other data about the hospital include classification based on the size of the hos-
pital (e.g., A = quaternary hospital, B = tertiary hospital, C = secondary hospital, and
D = primary hospital). One can also classify the hospital based on the stroke care hospital
(SCH) designation status. An SCH is a hospital equipped with healthcare professionals and
an environment that can provide intensive treatment, including EVT, for stroke patients;
the SCH designation status is considered important in the network analysis process in
previous related studies. Based on the data received from the HIRA, all quaternary hos-
pitals are designated as SCHs, and some tertiary hospitals are designated as SCHs. None
of the secondary or primary hospitals are designated as SCHs. Accordingly, the interface
is configured to facilitate the classification of B-type hospitals depending on their SCH
designation status in the analysis process (Section 4.4).
We complied with restrictions on the confidentiality of data: patient transfer data only
provides the sum of the number of patients transferred between hospitals to prevent the
identification of individual patients. The data contains the ID number of each hospital and
the number of patients transferred in and out. Among the types of patient transfer, those
discharged after receiving treatment at the first hospital visited (stay), and those transferred
to another hospital without receiving treatment at the first hospital visited and discharged
after receiving treatment at the transferred hospital (transfer and stay) are examined. In
practice, there are other cases where patients receive treatment at the first hospital and
are discharged from the transferred hospital. The last case involves three hospitals where
patients are transferred from the first hospital to the second hospital to receive treatment
and then transferred to a third hospital to get discharged. As such cases account for about
Appl. Sci. 2023, 13, 5241 6 of 21

0.43% of the total number of patients, this study only examined the two transfer types that
accounted for the most (99.57%).

3.2. Data Preprocessing

In the visualization process, additional measures for the network are calculated. The
calculated measures are mostly related to degree centrality and density, which were also
used in a previous study [7]. However, while this study examined a nationwide network,
the degree centrality of a hospital was not calculated for entire hospitals across the country.
Instead, we first derive clusters through community detection, and then we measure the
degree-centrality of a hospital within the belonging cluster. The community detection
technique used in this study is the Louvain algorithm [12,14,24]. We used a variation of
the algorithm [14] that reflects all characteristics of a directed (i.e., transfer direction) and
weighted (i.e., the number of transferred patients) network. As a result, the measures for
each node, each cluster, and the network are calculated (Table 2).

Table 2. Network measures for node, cluster, and entire network.

Category Metric Equation Application

Degree centrality nin
Node CD (ni ) = g−i 1 Find hub hospitals (1) *
(in-degree)
g
Group-degree ∑i=1 [CD (n∗ )−CD (ni )] Find highly
CGD = g
max ∑i=1 [CD (n∗ )−CD (ni )] (2) †
-centralization g centralized clusters
[C (n∗ )−C (n )]
= ∑i=1[( gD−1)( g−1D)] i
Cluster Intra-cluster | Eii | Compare internal
(
Kintra = 0.5× gi ×( gi −1) , where gi ≥2 (3) ‡
density connectivity of clusters
0, otherwise
Inter-cluster l | Eij | Compare external
Kinter = l −1 1 ∑ (4) ¶
density gi × g j dependency of clusters
j=1,j6=i
Transfer rate pintra Identify independent
Tintra = pintra + prcv + psend (5) §
(intra-cluster) clusters
Transfer rate
prcv Identify influential
(inter-cluster Trcv = pintra + prcv + psend (6) §
clusters
receive)
Transfer rate psend Identify highly
Tsend = pintra + prcv + psend (7) §
(inter-cluster send) dependent clusters

nin
i nj
out
  
1, i = j Probe the strength of the
(8) ||

Modularity 1
m ∑ij Aij − m δ cni , cn j , where δ(i, j) =
0, i 6= j division of a network
Network | E| Probe the overall density
Global density K= (9) #
0.5× N ( N −1) of a network
Mean intra- l
| Eii |
Compare the value with
K intra = 1
∑ (10)
cluster density l 0.5× gi ( gi −1) each intra-cluster density
i =1
Mean inter- l l | Eij | Compare the value with
K inter = 1
∑ ∑ (11)
cluster density 0.5×l (l −1) gi × g j each inter-cluster density
i =1 j = i +1

* ni = ith node in a cluster, nin

i = in-degree of node ni , ni
out = out-degree of node n ; † g = number of nodes in
i
g
the cluster, CD (n ) = largest value of CD (ni ) in the cluster, max ∑i=1 [CD (n∗ ) − CD (ni )] = maximum possible sum
∗

of differences in the cluster; ‡ Eij = number of undirected edges from cluster i to j, gi = number of nodes in
the ith cluster; ¶ l = number of clusters in the network, pintra = number of patients transferred within a cluster;
§ p
inter = number of patients transferred from other clusters, psend = number of patients transferred to other
clusters; || m = number of edges in the network, Aij = weight between the nodes ni and n j , Cni = cluster to which
node ni belongs; # | E| = number of undirected edges, N = number of nodes in the network.

The degree centrality of the ith node (ni ) in a cluster is the number of hospitals that sent
patients to the node (i.e., in-degree) out of the total number of hospitals within the cluster,
excluding the respective node ( g − 1). Among three options (i.e., in-degree, out-degree,
total degree) to calculate the degree centrality, the use of the in-degree in the calculation
was determined after a review of network measures and confirmed by the collaborating
Appl. Sci. 2023, 13, 5241 7 of 21

medical researchers who also participated in user task analysis (Section 3.4). They wanted
to focus on the role of the hub hospital in receiving and treating patients from nearby spoke
hospitals. We tested various thresholds of degree centrality to determine a hub hospital.
In this study, heuristically, a node with a value of 0.5 or higher is classified as a hub node.
The collaborating researchers used our tool to analyze 93 clusters from Louvain clustering
and found that 86 groups had a node located in the cluster’s center with a degree centrality
higher than 0.5. Three groups only had one node, and the remaining four had a central
node with a centrality between 0.3 and 0.5. As a result, the collaborators determined the
threshold as 0.5 in this research.
Measures calculated for each cluster include group degree centralization, intra-cluster
density, and inter-cluster density, which are used for community analysis of networks
in previous studies [25,26]. Regarding group-degree-centralization, the previous study
calculated the value for the undirected network. Thus, we adjusted the denominator from
[(g − 1)(g − 2)] to [(g − 1)(g − 1)] to accommodate the characteristics of the directed net-
work (i.e., minimum in-degree is 0). Furthermore, three types of patient transfer rates were
calculated for each cluster based on feedback from medical researchers. The proportion
of patients transferred within each cluster, the proportion of patients transferred out to
other clusters, and the proportion of patients transferred in from other clusters are calcu-
lated using the number of patients who visited the hospital in each cluster at least once
(i.e., excluding stay patients) as the denominator.
Indices calculated for the entire network included modularity [14], which is calculated
for community detection, and three densities (i.e., global, mean intra-cluster, and mean inter-
cluster), which are used to evaluate community detection results in a previous study [26].
In this study, the adequacy of community detection is examined by determining if three
density values satisfied K inter < K < K intra .

3.3. Simulation Data Generation

The data handled in this study contain sensitive information even after thorough
anonymization, and it was impossible to export them outside the designated institution for
the study. Therefore, we prepared simulation data for development and verification. Based
on the statistical representative values of the actual data, simulation data are generated with
a similar number of hospitals per administrative district. For inter-hospital connections,
sending and receiving hospitals were randomly selected based on the regional hospital
distribution calculated from the number of hospitals located in each region. In addition, the
number of transferred patients was randomly drawn from the patient count distribution
of inter-hospital transfers (i.e., for randomly selected sending/receiving hospitals, we
assigned randomly selected patient counts following the distribution of the actual data).
These simulation data were used to validate feasibility and capability of the proposed
visualization in the development process.

3.4. User Task Analysis

Through monthly meetings with the two collaborating medical researchers and a
literature review, analyses performed in previous studies and additionally demanded
analyses were identified. Two information visualization researchers and a neurologist
searched for network analysis papers on stroke patients and cross-checked the results.
Then, we summarized the task from the resulting list of articles. At the research meeting,
we prepared statistics regarding network topology and discussed them along with clinical
outcomes prepared by the medical researchers. While examining the proposed prototype
of the analysis framework, we reviewed research tasks and collected feedback as they
performed the task using the prototype. The tasks that influenced the design of the
proposed framework are as follows:
Appl. Sci. 2023, 13, 5241 8 of 21

3.4.1. Multi-Perspective Analysis of Topology (T1)

The medical researchers wanted to analyze the topology of how networks were formed.
This can be divided into two categories: analyzing quantitative measures (e.g., centrality,
density) that represent topology, and inspecting a node-link-based visualization for visual
analysis of topology. The latter approach of analyzing through visualization was divided
into a method of placing nodes on a map based on GPS coordinates, and a method of
placing nodes using a force-directed layout, depending on whether geospatial information
was used when placing nodes in a two-dimensional space. For collaborating researchers, all
three analysis methods were used because the results that could be drawn varied depending
on the three methods.

3.4.2. Analysis at Multiple Scales (T2)

The medical researchers tried to proceed with the analysis by setting the scope of the
topology analysis in various ways. Some previous studies analyzed patient transfer data
on a national scale [8,9], and others limited the analysis scope to local regions [1,5]. In
addition, the analysis was based on administrative regions, other cases chose community
as a scope of analysis. Zachrison et al. showed the possibility of network analysis using
the community-detection algorithm [7], and Dong et al. analyzed communities in the
inter-hospital network [11]. The literature review indicated that insights derived based
on the scope of each analysis were expected to be different. Therefore, we decided to aid
analysis with all three scales (i.e., national, regional, and community). Section 3.5 describes
the relevance and characteristics of three network analysis levels.

3.4.3. Analysis of Hub-and-Spoke Structure (T3)

The medical researchers wanted to analyze the hub-and-spoke structure at the commu-
nity level. Prior work confirmed that the designation as an SCH was followed by forming a
network with nearby hospitals. Thus, one of the goals of the analysis was to identify how
such network formation occurred in the Republic of Korea. In a previous study [13], hub
hospitals were designated prior to an analysis based on the size of hospitals. However, the
researchers wanted to detect communities solely from patient transfers and analyze the
resulting hub-and-stroke structures. They wondered if there was a discrepancy between
the intended and actual network structures. Thus, we classified hub hospitals by network
measures regardless of the SCH designation status or hospital size. Moreover, we tried to
visualize the hub-and-spoke structure to support comparative analyses of communities.

3.4.4. Analysis of Interprovincial Transfers (T4)

The medical researchers have attempted to analyze patient transfer patterns at the
provincial level. Since the large catchment areas align with provincial areas in the Republic
of Korea, investigating interprovincial patient transfers could indirectly examine whether
the catchment area was well established. For example, it is commonly known that some
regions in the Republic of Korea are highly dependent on specific hospitals. There were even
cases where patients transferred to such hospitals from other provinces. The collaborating
researchers wanted to confirm such known patterns with our framework and find other
notable patterns from patient transfer data.

3.4.5. Exploratory Analysis of Patient Counts (T5)

The medical researchers wanted to analyze the proportion of patients by transfer types
(i.e., transfer in/out and stay) in each hospital. However, the initial statistics examination
with the researchers revealed that the proportion of patients who stayed without transfer
was high (83.71%). Thus, the number of patients transferred in or out could be relatively
small for visual comparison when we include the number of stayed patients. Still, exclud-
ing them from the analysis was not preferred by the researchers as the numbers could
provide a cue about how many patients a hospital could accommodate. As a result, we
could conclude that, while the researchers do not have a hypothesis, they still want to
Appl. Sci. 2023, 13, 5241 9 of 21

investigate patient distribution by transfer types with exploratory analysis. Moreover, they
also showed interest in investigating the distribution over the whole nation, provinces,
and communities.

3.5. Three Network Analysis Levels

The task analysis result indicated that the analyses of the three levels of the network
were conducted in prior work and also wanted by the target users. This section summarizes
the relevance and characteristics of the networks as follows. The national-level analysis
reveals overall patient transfer trends and the geographical distribution of hospitals. One
could analyze network measures over the country and explore the geographical relationship
of hospitals of interest. The community-level analysis could complement the holistic
analysis by depicting hub-and-spoke structures in each cluster. It can help users examine
the role of individual hospitals in the community. The provincial level shows network
characteristics regarding administrative divisions. It reveals a relationship between a
predefined group of hospitals.
While prior work barely looked into the design space, the users can also closely
coordinate the three levels of the network. For instance, exploring at the community level
could draw groups of interest (e.g., groups with low centrality), and one can locate them
in the national-level view to investigate the geographical relationship. One can also start
from the provincial level to find communities of interest and drill down to exceptional
hospitals. For example, the aggregated transfers between provinces could aid in finding an
excessive amount of inter-province transfers. Then, one can browse communities within
the sending/receiving regions to find communities where spoke hospitals are in remote
administrative divisions.

4. PatientFlow: The Visual Analytics Framework

We designed PatientFlow for medical researchers who intend to analyze the network
structure based on patient transfer data. Multiple-coordinated views enable an exploratory
analysis based on the data and task analysis results. This section describes the overview,
the views for the cluster-level and province-level analyses, and the dynamic queries for
selecting or excluding data to be analyzed. PatientFlow is a web-based application that
uses D3.js [27] for visualization and the JavaScript library of bubble sets [28] for expressing
cluster coverage on a map. We used Graphology [29] for the Louvain clustering and
calculating network metrics. Regarding the task analysis result, we denoted the resolved
task (i.e., T1, T2, T3, and T4) for each framework component.

4.1. 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.
 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 be-
Appl. Sci. 2023, 13, 5241 10 of 21
tween 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.

Figure 2. PatientFlow interface. (a) A list of interactive charts presents network characteristics. One
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
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
selected, and one
view presents can confirm
hospitals the applied
and patient transfersfilter with chips
between them on on top
a map. of the
(c) screen.
The bar (b) The
chart topology
depicts the
view
number presents hospitals
of patients andhospital
for each patient in transfers between (i.e.,
three categories themtransfer-in,
on a map.transfer-out,
(c) The bar and
chartstay).
depicts
(d)
the number
A list of patients
of clusters yieldedfor each
from hospital in three
a community categories
detection (i.e., is
algorithm transfer-in,
presentedtransfer-out, and stay).
in three visualizations
(node-link
(d) A list of diagram, chord diagram,
clusters yielded and bar chart).
from a community Eachalgorithm
detection is designed to supportinanalyzing
is presented topology,
three visualizations
patient transfer
(node-link patterns,
diagram, chordand the number
diagram, and bar of chart).
patientsEach
for each hospital.
is designed to The totalanalyzing
support number oftopology,
clusters
and selected
patient clusters
transfer are and
patterns, shownthe on top ofofthe
number view (e.g.,
patients 5 out
for each of 95 clusters
hospital. The total are selected)
number (e) The
of clusters
and selected clusters are shown on top of the view (e.g., 5 out of 95 clusters are selected) (e) Thea
comparison view supports quantitative comparison between the selected clusters. One can select
network measure from the list on the left to make a comparison. (f) The province view presents
comparison view supports quantitative comparison between the selected clusters. One can select
patient transfers between provincial-level regions with a directed node-link diagram and a diverg-
a network measure from the list on the left to make a comparison. (f) The province view presents
ing bar chart. Depending on the transfer direction, the number of patients is colored in blue or or-
patient
ange. Atransfers
table andbetween provincial-level
a bar chart on the bottom regions
show with a directed
the hub hospitals node-link
within thediagram
region.and a diverging
One can click
bar
on achart. Depending
hospital on the
in any chart transfer
to locate direction,
it in the number
the topology view. of patients is colored in blue or orange.
A table and a bar chart on the bottom show the hub hospitals within the region. One can click on a
hospital
Forineach
any chart
clusterto locate
derived it infrom
the topology view.
the community-detection results, we used bubble sets
[28] to show the assumed coverage. (T4) It could show the geographical size of the cluster
For each cluster derived from the community-detection results, we used bubble
more clearly than simply coloring the node markers and could also aid in estimating the
sets [28] to show the assumed coverage. (T4) It could show the geographical size of
catchment area. However, the patient’s address or point of departure when visiting the
the cluster more clearly than simply coloring the node markers and could also aid in
hospital was missing from our data. Thus, it does not represent accurate catchment areas
estimating the catchment area. However, the patient’s address or point of departure when
as in [2], where Adeoye et al. assessed the accessibility of hospitals by region and visual-
visiting the hospital was missing from our data. Thus, it does not represent accurate
ized it. Still, it is possible to indirectly identify regions not participating in the treatment
catchment areas as in [2], where Adeoye et al. assessed the accessibility of hospitals by
network for stroke patients. Moreover, any patient transfer between distant hospitals can
region and visualized it. Still, it is possible to indirectly identify regions not participat-
be confirmed immediately through more giant bubbles spanning several areas.
ing 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.
 Appl. Sci. 2023, 13, 5241 11 of 21

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 analy-
sis 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, visu-
alizing 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
Appl. Sci. 2023, 13, x FOR PEER REVIEW chart (Figure 2a) to visualize the number of hospitals by size and SCH designation status 12 of 21
(e.g., A-SCH, B-SCH, B, C, and D).

Figure 3. Box
Figure plot
3. Box of of
plot the patient
the patienttransfer
transfer distance aggregatedbyby
distance aggregated province.
province. OneOne
can can
clickclick the outlier
the outlier
dotsdots
in the box plot to highlight the transfer link in the map.
in the box plot to highlight the transfer link in the map.
 Figure 3. Box plot of the patient transfer distance aggregated by province. One can click the outlier
Appl. Sci. 2023, 13, 5241 12 of 21
dots in the box plot to highlight the transfer link in the map.

(a) (b)
Figure 4. Brushable bar charts
Figure for (a) the
4. Brushable longest
bar link
charts for (a)within eachlink
the longest cluster
withinand
each(b) the degree
cluster and (b) centrality
the degree centrality
of each hospital. Darker gray background indicates the brushed area (i.e., selected range).
of each hospital. Darker gray background indicates the brushed area (i.e., selected The colorThe color
range).
strip on the right sidestrip
of the degree
on the centrality
right side chart centrality
of the degree shows the value
chart range
shows for arange
the value primary
for a hub (red),
primary hub (red), a
a secondary hub (orange and yellow),
secondary andand
hub (orange a spoke hospital
yellow), (blue).
and a spoke hospital (blue).

4.2. Comparative Cluster Topology Analysis

4.2. Comparative Cluster Topology Analysis
Based on the task analysis results, we designed the cluster view with three types of
Based on the task analysis for
visualizations results, we designed
analyzing the cluster
hub-and-spoke view
structures with
at the three types
community levelof(T3). The
visualizations for analyzing hub-and-spoke structures at the community level (T3).
node-link diagram at the top left uses a force-directed layout to depict the cluster’s Thetopology.
node-link diagramWe at the top left
colored uses by
the node a force-directed
degree centrality:layout to depict
red, orange, the cluster’s
yellow, and bluetopol-
correspond to
values greater than 0.5, 0.4, 0.3, and 0, respectively (Figure
ogy. We colored the node by degree centrality: red, orange, yellow, and blue correspond 5). These values are determined
heuristically during the meeting with the researchers. We classified a node with a degree
to values greater than 0.5, 0.4, 0.3, and 0, respectively (Figure 5). These values are deter-
centrality greater than 0.5 as a hub node and a node with a degree centrality less than 0.3 as
mined heuristicallya during the meeting
spoke node. Nodes with with the researchers.
a centrality between 0.3Weand classified a node to
0.5 are assumed with a
be secondary
degree centrality greater
hubs. We than 0.5 divided
further as a hub thenode and hub
secondary a node
range with
into atwo
degree centrality
parts for less
detailed classification,
than 0.3 as a spokeusing
node.0.4Nodes with a centrality between 0.3 and 0.5 are assumed
as a threshold. As a result, one can distinguish a node receiving many to be patients
secondary hubs. We from other hospitals
further divided within the clusterhub
the secondary fromrange
a nodeinto
receiving a relatively
two parts small number of
for detailed
patients while sending many patients to other hospitals.
classification, using 0.4 as a threshold. As a result, one can distinguish a node receiving
The chord diagram on the upper right (T5) enables a more detailed examination
many patients from other hospitals within the cluster from a node receiving a relatively
of patient transfer counts than the node-link diagram (Figure 5). While the node-link
small number of patients
diagramwhile sending
provides many
a better patients
topology to other
overview, onehospitals.
can hardly compare the number of
patients transferred between the hospitals. On the contrary, the thickness of the arcs in
the chord diagram corresponds to the patient count, and one can visually compare them.
Each circumference segment represents the hospital with a distinct background color. The
arcs in the diagram have identical colors to the target hospital, and the sharp arrowhead
represents the transfer direction. Thus, one could quickly identify the hospital with a
considerable number of visited patients and the hospital receiving the most significant
number of patients. The chord diagram can display stay patients with self-links, but since
most patients stayed at the same hospital, it hindered analyzing transfer patterns. As a
remedy to a relatively small number of transferred patients, we put a button at the title bar
to toggle whether to include stay patients or not. The bar chart at the bottom shows the
exact number of patients and follows the toggled state.
Appl.
Appl. Sci. 2023, 13,
Sci. 2023, 13, 5241
x FOR PEER REVIEW 13 of
13 of 21
21

(a) (b) (c)

Figure 5.
Figure 5. Analysis
Analysis of
of cluster-level
cluster-level patient
patient transfers.
transfers. One
One cancan click
click on
on circle
circle markers
markers in
in the
the node-link
node-link
diagram, circumference segments in the chord diagram, or bars in the bar chart. The clicked element
diagram, circumference segments in the chord diagram, or bars in the bar chart. The clicked element
becomes highlighted with a red outline in all three charts: (a) hospital ID 83 and (b) hospital ID 434.
becomes highlighted with a red outline in all three charts: (a) hospital ID 83 and (b) hospital ID 434.
(c) The background color of circle markers corresponds to degree-centrality: red, orange, yellow,
(c)
andThe background
blue correspond color of circle
to values markers
greater thancorresponds
0.5, 0.4, 0.3,toand
degree-centrality: red, orange,
zero, respectively. One canyellow,
locate and
sec-
blue correspond to values greater
ondary hubs colored orange and yellow.than 0.5, 0.4, 0.3, and zero, respectively. One can locate secondary
hubs colored orange and yellow.
The chord diagram on the upper right (T5) enables a more detailed examination of
Then, we arranged additional space for the comparative analysis of clusters (T1).
patient transfer counts than the node-link diagram (Figure 5). While the node-link dia-
One can browse a list of clusters by scrolling but examining 90 clusters derived from
gram provides a better topology overview, one can hardly compare the number of pa-
1009 hospitals could be challenging. After several reviews on network measures with
tients transferred between the hospitals. On the contrary, the thickness of the arcs in the
medical researchers, we selected group degree centralization as a sorting criterion: one
chord diagram corresponds to the patient count, and one can visually compare them. Each
could start the analysis from the most/least centralized cluster. In addition, one can click
circumference segment represents the hospital with a distinct background color. The arcs
the title bar on top of each cluster to select it, and the selected clusters move to the top
in the diagram have identical colors to the target hospital, and the sharp arrowhead rep-
of the list for future reference. The selected clusters also appear in the comparison view
resents the transfer direction. Thus, one could quickly identify the hospital with a consid-
located at the bottom for comparison of network measures, enabling quantitative analysis
erable number of visited patients and the hospital receiving the most significant number
of network topology. A user can select a measure from the list on the left (Figure 2e), and
of patients. The chord diagram can display stay patients with self-links, but since most
a bar chart shows the corresponding value of selected clusters. Moreover, one can select
patients stayed
multiple measures at the
to same
combine hospital,
them itintohindered analyzing
a normalized transfer
stacked barpatterns. As a remedy
chart (Section 5.1). It
could aid the comparison of multiple proportions where their sum equals one title
to a relatively small number of transferred patients, we put a button at the (e.g.,bar
threeto
toggle whether to include
types of transfer rates in Table 2).stay patients or not. The bar chart at the bottom shows the exact
number of patients
We aimed and follows
to encourage the toggled state.
multi-perspective analysis by locating and analyzing a hospital
Then, we arranged additional
of interest from another view (T1 and space forThus,
T2). the comparative
we linked the analysis
chartsofwithin
clustersthe(T1). One
cluster
can browse a list of clusters by scrolling but examining 90 clusters
analysis area and the other charts from different perspectives. One can click on circle derived from 1009 hos-
pitals could
markers be node-link
in the challenging. After several
diagram, reviewssegments
circumference on network measures
in the with medical
chord diagram, or barsre-
searchers, we selected group degree centralization as a sorting
in the bar chart. Then, the clicked hospital becomes highlighted in all three charts andcriterion: one could start
the analysis
other from the most/least
linked visualizations. centralized
For instance, when cluster. In addition,
one clicks the circleone can click
marker in thethe title bar
node-link
on top of the
diagram, eachmarkers
cluster to select it, and
representing thethe selected
hospital areclusters movewith
highlighted to the top outline
a red of the list
in for
all
future reference. The selected clusters also appear in the comparison
three charts in the cluster view and the map in the topology view. Further, it automatically view located at the
bottomtofor
scrolls thecomparison
province it of network
belongs to inmeasures,
the province enabling quantitative
view (Section analysis
4.3) on of network
the right side.
topology. A user can select a measure from the list on the left (Figure 2e), and a bar chart
4.3.
shows Provincial-Level
the correspondingAnalysisvalue of selected clusters. Moreover, one can select multiple
measures to combine
The task analysisthem into aa demand
revealed normalized for stacked
analysisbar chart
at the (Section level
provincial 5.1). It
incould
additionaid
thethe
to comparison
community of level.
multiple proportions
Nekkab et al. [9]where theirtransfer
analyzed sum equals one (e.g.,
patterns at thethree types
regional and of
transfer rates in Table 2).
county level, and we took a step forward by adopting multiple interactive coordinated
views. WeWe aimed to encourage
aggregated multi-perspective
transfer analysis by locating
patterns by administrative district and
unitsanalyzing
and listeda hos-
hub
pital of interest
hospitals fromtoanother
in the area supportview (T1 andrequirement
this analysis T2). Thus, we (T4).linked thetwo
The top charts within the
visualizations
cluster analysis
display area and between
patient transfers the otherprovinces
charts from different
(Figure 6a). perspectives.
In the node-link One diagram
can click on on
circle
the markers
left, in the node-link
the province of interestdiagram,
lies in the circumference segments
center, and other in thethat
provinces chord diagram, or
sent/received
patients
bars in theto/from the province
bar chart. Then, theencircle it. Unlikebecomes
clicked hospital the node-link diagram
highlighted in three
in all the cluster
chartsview,
and
two color-coded curved arrows (i.e., blue: transfer-in, orange: transfer-out)
other linked visualizations. For instance, when one clicks the circle marker in the node- indicate the
link diagram, the markers representing the hospital are highlighted with a red outline in
 ber of provincial-level divisions in the Republic of Korea is less than 20, and linked areas
by patient transfers could be even fewer. Thus, we introduced bidirectional links and
mapped the number of transferred patients to the thickness of the link. Although the bi-
directional node-link diagram provides an overview while browsing multiple provinces,
drawing the number of transferred patients could be challenging. An additional diverging
Appl. Sci. 2023, 13, 5241 bar chart on the right mitigates the problem. Moreover, one could compare the number 14 of 21
of
patients across multiple provinces with the chart. We used a consistent color scheme to
display the transfer direction of patients (i.e., blue and orange).
There
transfer are twoSince
directions. visualizations
the number in the bottomcould
of entities row to display
lead information
to visual clutter in on
theSCHs
cluster in
the province
view, we tried(Figure 6c). The
to minimize table
visual on the left
encodings shows
such a completeHowever,
as directions. list of SCHs. The columns
the total number
contain
of a district, hospital
provincial-level divisionstype,
in the and the number
Republic of patients
of Korea who20,
is less than transferred
and linkedin/out
areasand
by
stayed.transfers
patient Since tabular
could visualization
be even fewer.isThus,
challenging to observe
we introduced the overall
bidirectional trend,
links and we put a
mapped
stacked
the barof
number chart on the right
transferred that to
patients shows the threeof
the thickness types of patient
the link. counts.
Although theWe linked the
bidirectional
node-link diagram provides
two visualizations with the an overview
others while
(T2) and browsing
clicking multiple
a specific provinces,
hospital in thedrawing
table orthethe
number
stacked of
bartransferred patients
chart highlights it incould
everybelinked
challenging. An additional
visualization diverging
(e.g., topology viewbar
andchart
clus-
on
terthe rightClicking
view). mitigatesthethe problem.
column titleMoreover, one could
sorts the items in thecompare
table and the number
the stackedofbar
patients
chart.
across
As in multiple provinces
the cluster view, we with the chart.
added We used
a button a consistent
on top to togglecolor scheme
whether to display
to include the
stayed
transfer
patientsdirection
or not inof patients
the (i.e., blue and orange).
visualizations.

Figure 6. Analysis of provincial-level patient transfers. (a) The province of interest (i.e., KWJ) lies in
Figure 6. Analysis of provincial-level patient transfers. (a) The province of interest (i.e., KWJ) lies in
the center, and other linked provinces encircle it. One can notice that KWJ receives many patients
the center, and other linked provinces encircle it. One can notice that KWJ receives many patients
from CLN province, judging from a thick link on the left and the longest bar on the right. (b) As
for CLN province, it mostly sends patients to others except for three provinces. (c) The table on the
left shows the complete list of hubs in the area. One can identify the hub with the most significant
number of patients in the stacked bar chart on the right (i.e., hospital ID 425). When one clicks the
row or bar, the cluster with the hub becomes highlighted, and one can locate them in the cluster view.
(d) The chord diagram shows that hospital ID 425 mostly receives patients from hospitals within the
cluster but sends patients to hospitals outside the provinces judging from the bar chart in (c). (e) One
can confirm it in the topology view by selecting the cluster and hovering a mouse on the hub. There
are connected dark dray nodes outside the province.

There are two visualizations in the bottom row to display information on SCHs in the
province (Figure 6c). The table on the left shows a complete list of SCHs. The columns
contain a district, hospital type, and the number of patients who transferred in/out and
stayed. Since tabular visualization is challenging to observe the overall trend, we put a
stacked bar chart on the right that shows the three types of patient counts. We linked the
two visualizations with the others (T2) and clicking a specific hospital in the table or the
stacked bar chart highlights it in every linked visualization (e.g., topology view and cluster
view). Clicking the column title sorts the items in the table and the stacked bar chart. As in
the cluster view, we added a button on top to toggle whether to include stayed patients or
not in the visualizations.

4.4. Dynamic Query and Community Detection

In PatientFlow, a user can filter hospitals and links in the interactive charts on the
leftmost side of the screen (T1, T2, and T4). The task analysis result indicated that the
Appl. Sci. 2023, 13, 5241 15 of 21

collaborating researchers demanded exploratory analysis. Thus, we introduced several

filters for links, hospitals, and clusters and placed chips at the top of the screen to check the
currently applied filtering conditions. It could also aid in fine-tuning the conditions.
As a starter, one can filter links within a specific range by brushing a bar chart
(Figure 2a). Zachrison et al. [1] assumed that patient transfers over an excessively long
distance are uncommon because stroke is a time-sensitive disease. Therefore, prior work
excluded the outliers after inspecting the distribution of travel distances. However, such a
long-distance transfer could be meaningful for identifying defective communities. There
are also no clear standard threshold values for filtering: users might have to apply various
thresholds. With our tool, one can interactively adjust the range of transfer distances
depending on the purpose of analysis. We added a brushable chart at the bottom of the
histogram to provide an enlarged view of the selected range on top. Upon selection, the
Louvain algorithm derives new communities with filtered links.
There are also filters for hospitals. Regarding the bar chart for hospital type, one
can click on each bar to toggle selection. The bar for selected types becomess highlighted
with red outlines. As the filtering affects every visualization in our tool, only the selected
types of hospitals appear in other views. Thus, it could aid in analyzing the distribution
of specific types of hospitals. For instance, if only the A-SCHs are selected, the A-SCHs
and only the patient transfers between them become visible in the topology view. Users
can click the apply button to update the community detection result with filtered hospitals
(Figure 2a). Since the interaction includes or excludes hospitals in the network, it took
relatively longer to come up with clustering results. Thus, we added a button on top to
execute the clustering algorithm instead of updating the community in real time. It is
also possible to filter hospitals by centrality. One can select only hub or spoke hospitals
by brushing on the bar chart (Figure 4b). However, it does not derive new communi-
ties since it aims to find a hospital satisfying a specific condition without updating the
clustered result.
Lastly, one can interactively filter clusters. One can find clusters with solid or weak
hub-and-spoke structures by brushing a bar chart for group degree centralization. In
the bar chart of inter-cluster density, users can identify highly interconnected clusters.
Moreover, brushing the bar chart for the longest transfer distance within the cluster could
reveal abnormally large clusters. We also considered deriving a convex hull or a concave
hull based on the GPS coordinates of hospitals as an alternative. However, we took a
conservative approach using the longest link distance and avoiding estimation, which
might lead to a false perception of cluster coverage.

5. Evaluation
5.1. 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 ex-
amine 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
Appl. Sci. 2023, 13, 5241 16 of 21

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. Then, the user brushed the lower area in the degree centrality bar chart to
examine hospitals with a degree centrality less than 0.5. Four hospitals in KAW, CCB, CCN,
and CLN provinces matched the conditions. The user clicked the node in the geographical
topology view, and he/she could notice that two of them were secondary hubs, but the
rest were spokes in the cluster (highlighted with red outlines in Figure 5a,b). In the case of
hospital ID 83, it showed a tendency to receive patients from the others within the cluster.
However, the hub hospital in the cluster (i.e., hospital ID 88) received significantly more
patients even though it was not an SCH (Figure 5a). Regarding hospital ID 434, it did not
receive any patients from other hospitals. Instead, it sent most of the visited patients to
one of the hub hospitals in the cluster, which is also A-SCH. Considering that the other
30 A-SCHs acted as a primary or a secondary hub, it might require further examination
with additional clinical data.
During the case study, the participants used group degree centralization and the
number of hubs in the cluster as criteria for classifying clusters. While such classification
sufficed the demand, a user took a step forward by analyzing additional metrics for network
topology. When a hospital receives patients from all the others in the cluster, group degree
centralization becomes 1.0. Thus, the user selected five clusters (Figure 7a) with identical
group degree centralization (i.e., 1.0) and star topology (i.e., cluster without inter-spoke
links). Within the cluster, all five of them seemed similar to the user. Then, he/she selected
inter-cluster density to investigate connectivity between clusters to find differences between
the five clusters (Figure 7b). He/she wanted to investigate the cause and selected three
transfer rates to analyze them in a stacked bar chart (Figure 7c). As a result, the user could
identify two distinctive patterns: sending more patients to other clusters (e.g., cluster ID 1
and 3), and vice versa (e.g., cluster ID 5). When analyzing topology within a cluster, all
 identical group degree centralization (i.e., 1.0) and star topology (i.e., cluster without in-
ter-spoke links). Within the cluster, all five of them seemed similar to the user. Then,
he/she selected inter-cluster density to investigate connectivity between clusters to find
differences between the five clusters (Figure 7b). He/she wanted to investigate the cause
Appl. Sci. 2023, 13, 5241 and selected three transfer rates to analyze them in a stacked bar chart (Figure 7c).17As a
of 21
result, the user could identify two distinctive patterns: sending more patients to other
clusters (e.g., cluster ID 1 and 3), and vice versa (e.g., cluster ID 5). When analyzing topol-
ogy within a cluster, all five clusters would be similar. However, if the goal of the analysis
five clusters would be similar. However, if the goal of the analysis is related to inter-cluster
is related to inter-cluster transfers, the user might need another perspective to investigate
transfers, the user might need another perspective to investigate them properly.
them properly.

(a) (b) (c)

Figure 7.
Figure 7. Comparative
Comparativecluster
clusteranalysis
analysis view
view with
with multiple
multiple network
network measures.
measures. In example,
In this this example,
a usera
user selected five clusters (i.e., cluster ID 1, 3, 5, 11, and 14). (a) The centralization degree for five
selected five clusters (i.e., cluster ID 1, 3, 5, 11, and 14). (a) The centralization degree for five clusters
clusters shows identical values. (b) However, inter-cluster densities vary among the five clusters.
shows identical values. (b) However, inter-cluster densities vary among the five clusters. (c) Users
(c) Users can select multiple measures to analyze them with a stacked bar chart.
can select multiple measures to analyze them with a stacked bar chart.
5.2. Comparative
5.2. Comparative Analysis
Analysis with
with Prior
Prior Work
Work
AA network
network analysis
analysisapproach
approachisisininitsits early
early stage
stagein clinical
in clinicalresearch
researchafterafter
the appear-
the ap-
ance of EVT, and visualization emerged as an essential part
pearance of EVT, and visualization emerged as an essential part of studying hospital of studying hospital coordi-
nation. Still, the
coordination. prior
Still, thework
priorleft
worka possibility to reachtoa reach
left a possibility higheralevel of maturity
higher in contrast
level of maturity in
to the information
contrast visualization
to the information domain.
visualization To the To
domain. besttheofbest
ourofknowledge,
our knowledge, this isthis
theisfirst
the
paper
first to propose
paper an interactive
to propose framework
an interactive frameworkfor thefor analysis of stroke
the analysis care hospitals.
of stroke More-
care hospitals.
over, some research used commercial tools in the analysis process,
Moreover, some research used commercial tools in the analysis process, which made quan- which made quantita-
tive comparison
titative comparison (e.g., task
(e.g., completion
task completion time,
time,error
errorrate)
rate)with
withourourmethod
method difficult.
difficult. Thus,
we compared
we compared our our work
work withwith previous
previous research
research regarding
regarding data, data, analysis
analysis tasks,
tasks, analyzed
analyzed
network levels,
network levels, research
research focus,
focus, and
and visual
visual representations
representations(Table (Table3). 3).
Prior work
Prior work focused
focused on on analyzing patient transfer patterns (e.g., transfer transfer volumes,
volumes, the
number of connections
connections between
betweenhospitals,
hospitals,and andthe theexistence
existence of of
community)
community) andand
some re-
some
lated them
related them to to
clinical variables
clinical variables (e.g., thethe
(e.g., number
number of performed
of performed treatments
treatmentsand and
clinical out-
clinical
outcomes). Regarding
comes). Regarding thethe data,
data, it ranged
it ranged from
from a asingle
singlestate
state(or
(orprovince)
province)to toaawhole
whole nation.
nation.
The
The number
number of of patients
patients varied
varied according
according to to the
the target
target disease
disease andand collection
collection period.
period. TheThe
number
number of of hospitals
hospitals was
was primarily
primarily affected
affected by by the
the collection
collection coverage.
coverage. The research focus
determined the analysis
analysis task.
task. As a result, the previous previous studies
studies partially
partially conducted
conducted the the
analysis compared
compared to toours.
ours.However,
However,the thecase
case study participants generally
study participants generally agreed that the agreed that
the capability
capability to perform
to perform additional
additional taskstasks
would would
benefit benefit the prior
the prior work.work. Some studies
Some studies inves-
investigated
tigated network changes over time, which this study did not cover; we only we
network changes over time, which this study did not cover; hadonly
accesshadto
access
patienttotransfer
patient transfer
data withindata awithin
singlea interval.
single interval.
We coveredWe covered this limitation
this limitation as a as a future
future re-
research directionininthe
search direction thenext
nextsection.
section.Regarding
Regardingthe theanalyzed
analyzed network
network levels,
levels, two
two previous
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 relation-
ships 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 visual-
izations 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.
Appl. Sci. 2023, 13, 5241 18 of 21

Table 3. Comparison with prior work on hospital networks.

Data Analysis Task a Network Level b Visualizations for Network

(Country, Coverage and Period, Analysis
Size, Target Disease) T1 T2 T3 T4 T5 N P C
Republic of Korea Node-link diagram
Patient Nationwide for 6-months (force-directed, geographic),
O O O O O O O O
Flow 19,113 patients, 1009 hospitals Histogram, Chord diagram,
Stroke Bar chart (diverging, stacked)
United States
Nationwide for 2-years Node-link diagram
[8] X X X X X O X X
12.5 M patients, 5667 hospitals (geographic)
Clostridium difficile
France
Parallel coordinates,
Nationwide for 1-year
[9] O O P O X O O O Node-link diagram
21,279 patients c , 1266 hospitals c
(geographic)
Hospital-acquired infections
China
Fujian province for 3-years Node-link diagram
[11] O P P N X N O P
32,759 patients 1043 hospitals (force-directed)
Hypertension
United States
Eight states d for 5-years Node-link diagram
[5] X P P X X N O O
154,631 patients, 394 hospitals (geographic)
Stroke
United States
California for 8-years Node-link diagram
[1] X P X N X N O P
336,247 patients, 351 hospitals (geographic)
Stroke
United States
Three states e for 15-years
[13] O N O X X N X O Map, Line chart
12,803 patients, 43 hospitals
Stroke
aRefer to Section 3.4 for each task description. O = performed, X = not performed, P = partially performed,
N = not applicable. b N = national, P = provincial, C = community. c HAI (Hospital-Acquired Infections)-specific
network. d Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, and
Vermont. e Massachusetts, New Hampshire, and Maine.

6. Conclusions
This study proposed a framework for deriving and analyzing a community from
patient transfer data, geographical location, and hospital type (i.e., size and whether
designated as SCH). The framework adopted the methodologies and suggested network
measures in prior work [1,7]. Unlike cases with a clear hypothesis to be tested or the
aim of making a predictive model for fixed variables, medical researchers performed
an exploratory analysis to understand the topology at the time data were collected. We
designed PatientFlow for a national-level overview and interactive analysis of inter-hospital
coordination. Users can also examine patients’ transfer patterns at the cluster and provincial-
level administrative district units. To this end, PatientFlow supports visual and quantitative
analyses with multiple-coordinated views.
Our approach fulfilled the requirement of the collaborating medical researchers. It
led to clinical implications with additional medical data and inspired follow-up research.
However, at the time of research, we could not access the clinical records of the medical
research institution. Since the patient outcome at the hospital played an essential role in
prior work [7], the researchers had to rely on a separate tool to analyze them with the
findings from our tool. Regarding quantitative analyses, we are working on integrating
the clinical records into our framework and adopting additional measures. For instance,
Appl. Sci. 2023, 13, 5241 19 of 21

additional network measures, such as eigen centrality or page rank, could be introduced to
analyze the topology from another perspective. Moreover, additional data could introduce
a subgroup analysis as in the prior work [9] on HAIs. We could divide patients into
subgroups by gender, age, diabetes, or other meaningful factors for stroke treatment.
We are also working on adopting another community detection algorithm. The
Louvain algorithm identified a total of 93 communities in this work. Even though we
introduced interactivity (i.e., brushing-and-linking, zooming, and filtering) in our tool,
communities located near the metropolitan area yielded visual clutter from occlusions.
Jittering nodes resolved the problem to some extent, but overlapping bubble sets made it
challenging to analyze the clusters in the topology view. Additional efforts on grouping
adjacent clusters using district information and the current patient transfer information
might help mitigate the occlusion problem. A visual comparison of multiple community
detection results would also help determine the appropriate algorithm heuristically. Such
an approach would lead to examining the changes in network structure over time, which
has been a subject of interest in prior work [1,5]. It could also aid in comparing the derived
communities to planned catchment areas for policy making.
One can use our research to analyze hospital networks for cases where interhospital
transfers are inevitable. For instance, patient transfers are prevalent in the emergency
department [31], but some studies reported challenges in arranging interhospital trans-
fers [32,33]. The COVID-19 outbreak has also introduced the need for network analysis.
Negative pressure rooms were proven effective for treatment [34], but the limited number
of rooms in each hospital might cause patient transfers to other hospitals. In such cases,
PatientFlow could aid clinical researchers in analyzing hospital networks and finding
communities with a low level of cooperation.

Author Contributions: Conceptualization, K.K., J.P. and H.S.; methodology, K.K. and H.S.; software,
K.K., J.P. and H.S.; validation, K.K., J.P. and H.S.; formal analysis, K.K. and H.S.; investigation, K.K.
and H.S.; resources, H.S.; data curation, K.K., J.P. and H.S.; writing—original draft preparation,
K.K. and H.S.; writing—review and editing, H.S.; visualization, K.K., J.P. and H.S.; supervision,
H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the
published version of the manuscript.
Funding: This work was support by the Soongsil University Research Fund (New Professor Support
Research) of 2020.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author with the permission of HIRA (Health Insurance Review and Assessment
service). The data are not publicly available due to privacy.
Acknowledgments: We thank Hee-Joon Bae, Jihoon Kang, and Seong Eun Kim (Department of
Neurology at Seoul National University Bundang Hospital) for their help and valuable comments on
this research.
Conflicts of Interest: The authors declare no conflict of interest.

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