Supporting Strategic Planning with Interactive Visualization

A Case Study of Patient Flow through a Large Hospital

Dominique Brodbeck

, Markus Degen

and Andreas Walter

University of Applied Sciences and Arts Northwestern Switzerland, Brugg, Switzerland

Inselspital, Bern University Hospital, Bern, Switzerland

Keywords:

Clinical Informatics, Visual Analytics, Patient Flow.

Abstract:

Hospitals collect large amounts of data during their daily operation. Next to its immediate primary purpose,

this data also contains implicit information that can be used to improve clinical and administrative processes.

We present a case study of how strategic infrastructure planning can be supported by the analysis of enriched

patient ﬂow through a hospital. Data from various hospital information systems was collected, enriched with

topographical and organizational data, and integrated into a coherent data store. Common analysis tools and

methods do not support exploration and sense-making well for such large and complex problems. We therefore

developed a highly interactive visual analytics application that offers various views onto the data, and provides

fast access to details in order to show them in context. The analysts were able to validate their experiences,

conﬁrm hypotheses and generate new insights. As a result, several sub-systems of clinics were identiﬁed that

will play a central role on the future hospital campus. This approach was successful enough that we envision

to extend it towards other process optimization tasks in hospitals.

1 INTRODUCTION

Adoption of information technology in health care

has been slow (Jha et al., 2009), (American Hospital

Association, 2007), but increasingly health care in-

stitutions such as hospitals collect and manage large

amounts of data. Next to its eventual use in sup-

porting clinical care, the data is also largely used

for administrative purposes such as billing, schedul-

ing, or resource planning (Chaudhry et al., 2006).

While there are commercial hospital information sys-

tems that perform the core of the data management,

there are typically many additional independent sys-

tems that are designed to support a speciﬁc medical

procedure, with a unique patient id often being the

only link between them.

For typical strategic planning tasks, this setup

allows to answer questions such as how many pa-

tients were treated in the cardiology department last

year. More complex questions require queries across

several of the systems and along different dimen-

sions: Which departments have many transfers be-

tween each other? Are there unusual transfers into

other departments that deviate from this trend, and

how are the involved patients characterized? Is this

effect seasonal? If the data is enriched even more, for

example with external data like geographic location

of departments, weather conditions, etc. then further

investigations become feasible (Alapont et al., 2005).

Even if all the data is available for answering

such questions, it is typically large, distributed and

only weakly related. Standard tools for analysis of

such data sets are limited, and new approaches are

needed (Cuzzocrea et al., 2011). These characteris-

tics and the related technology and issues have be-

come known as big data. Deﬁnitions of big data vary

(Laney, 2001), (Zikopoulos and Eaton, 2011) but data

from hospital information systems often share many

of these aspects. (Van der Aalst, 2012) argues that X-

raying business processes in this huge amount of data,

through the application of clever mining algorithms,

can be used to gain valuable insights for future strate-

gic planning of organizations.

In this paper, we present a case study where we

collected, combined, and enriched data from a large

university hospital, and used interactive visualization

to access, analyze, and interpret the data to support

strategic infrastructure planning.

Hospital sites are developed in an evolutionary

manner over a long time span. This leads to phys-

ical and organizational layouts of the facilities that

are usually not optimal anymore after a certain while.

Brodbeck D., Degen M. and Walter A..

Supporting Strategic Planning with Interactive Visualization - A Case Study of Patient Flow through a Large Hospital.

DOI: 10.5220/0004244000850093

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2013), pages 85-93

ISBN: 978-989-8565-37-2

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

Strategic planning with time horizons of 25 years and

more provides the opportunity to correct this degen-

eration, and optimize the layout when the campus is

enlarged, new facilities are built, or old ones replaced.

The optimal conﬁguration of departments, their

organizational units and technical facilities is not al-

ways evident. Questions such as “where should the

emergency department be placed, and if we locate it in

a new building, do we need an additional radiology fa-

cility?” should be answered based on evidence and in-

sights rather than intuition, subjective opinions, or ob-

solete experience. The idea therefore was to use past

real data to identify existing clusters of organizational

units that are related based on what they actually do,

and not on where they are placed in the organization

chart. With these insights, it should be possible to

deﬁne future sub-systems of organizational units and

medical functions, optimized for efﬁciency. These

new sub-systems can then be characterized again with

the past data for further analysis and communication

to stakeholders.

In this case study we built such a system based on the

following hypotheses:

• The core questions that arise in strategic planning

of hospital infrastructure can be answered by us-

ing the ﬂow of patients between organizational

units as core data, and enriching it with additional

data about cost and performance of medical pro-

cedures

• The nature of the problem requires an exploratory

approach, since the novel combination and repre-

sentation of the data will likely lead to the emer-

gence of new insights and hypotheses.

In the following, we present our approach that con-

sists of two parts:

• Aggregate as much data from the various hospi-

tal information systems as possible, and make it

available in a ﬂexible format

• Provide highly interactive access to and visualiza-

tion of this information to support exploration and

interpretation

2 METHODS

2.1 Data Aggregation

Large hospitals, and in particular university hospitals,

typically have a heterogeneous IT-infrastructure due

to the fact, that the different clinics are rather au-

tonomous and have different needs for the type of data

to store. Often data of one clinic (i.e., orthopedics) is

stored in a speciﬁc IT system only used by that spe-

ciﬁc clinic. To cover speciﬁc needs for research, clin-

ics often develop their own applications that are used

to store additional research related information. Usu-

ally this data can be linked to the patient’s electronic

health record by using the patient id or the case id as

key, but otherwise data integration is complicated fur-

ther and indirect clues must be found.

The design of our data store was heavily inﬂu-

enced by the two central dimensions of future queries:

Multi-scale: Case data has to be aggregated into sev-

eral layers to allow drill-down from the hospital

level (e.g., number of patient-days per year) to

speciﬁc organizational units (e.g., is there a sea-

sonal pattern in patients visits to the pneumol-

ogy clinic?), and to individual patient cases (e.g.,

chronology of visits to the radiology department

for one speciﬁc case).

Multi-aspect: The system shall be able to view the

data from different aspects, ranging from the lin-

ear temporal view (e.g., chronological view of all

events in a patient case), to a two-dimensional

geographic map (e.g., where should the radiol-

ogy department be placed on a campus to mini-

mize travel distances for patients?), to the network

topology of relationships between organizational

units (e.g., which units transfer the most patient

between each other).

In our project, information from several sources was

used and linked:

• Inter-organizational transfer histories of station-

ary patients.

• Case attributes (e.g., diagnosis, treatment, diagno-

sis related group (DRG)) of stationary cases.

• Times of surgeries (timestamps at the cut and at

the end of suturing).

• Transfers to ambulatory facilities (i.e., radiology

department).

• Hierarchical organization of the hospital.

• Physical layout of the organizational units of the

hospital.

While cleaning and integrating the data, we faced

the typical problems that arise during data wrangling

(Kandel et al., 2011), confounded by the privacy is-

sues inherent in medical data.

The input data was gathered from numerous CSV

formatted database table exports. No input ﬁles con-

tained personal information about the patients, and

the exports were performed by the hospitals IT staff

to ensure patient privacy. No live connection was es-

tablished to the hospital IT infrastructure. In the data

HEALTHINF2013-InternationalConferenceonHealthInformatics

aggregation step, the data from the different systems

and queries were interwoven, mostly by taking the pa-

tient id or the case id as a key. Many precautions,

ﬁltering and post-processing steps were taken to en-

rich the data and at the same time to ensure, that the

resulting data was consistent (i.e., post-surgical trans-

fers from one clinic to another had to be suppressed in

a few speciﬁc cases where, although the transfer was

present in the input data, in the real world none took

place). The output of the data aggregation step con-

sists of a few ﬁles in human readable CSV format. As

a side effect, we were able to use simple text manipu-

lation tools (e.g., sed, sort, grep, and python scripts) to

ﬁlter and generate subsets of the enriched data, which

was in turn used by the hospital to perform plausibil-

ity checks on their data.

Overall, we collected one full year of data from

40 clinics comprising 300 organizational units that

treated 40000 cases from 30000 stationery patients,

with 320000 transfers between the organizational

units.

Standard transactional databases do not typically

offer the high data access performance and the ver-

satile data types that highly interactive multi-view vi-

sualization applications require (Keim et al., 2010).

We therefore build up a dedicated optimized and in-

terlinked in-memory data structure that is created on

start-up of the analytics application. This approach

leads to a slightly prolonged start-up time, but enables

the implementation of the fast and highly responsive

user interface described in the next section.

The data aggregation process is shown in Figure 1.

Figure 1: In the data aggregation step information from sev-

eral sources is enriched and integrated.

2.2 Visual Analytics

With all the data integrated and available, the next

challenge was to render it usable for the planning ex-

perts. The amount and complexity of the data made

it impractical to use common analysis tools such

as spreadsheets and standard methods from statistics

with static graphs. For the type of problems found

in our case study, analysts often only have vague no-

tions of what they are looking for (“I know it when I

see it”). It is therefore crucial to make the data visi-

ble from various angles, and to provide highly inter-

active tools to identify interesting patterns and access

details in context. Visual Analytics is a set of methods

and technologies from a ﬁeld deﬁned as the formation

of abstract visual metaphors in combination with hu-

man interaction that enables detection of the expected

and discovery of the unexpected within massive, dy-

namically changing information spaces (Wong and

Thomas, 2004).

Based on this approach, we developed a visual an-

alytics application to support analysts in making sense

of the collected data. The application offers four prin-

cipal views (Figure 2):

• Organizational: shows the organizational struc-

ture and how the actual medical activities shape

the administrative space.

• Systemic: reveals the operational structure as it

emerges from patients ﬂowing through the hospi-

tal.

• Topographical: shows the actual physical situa-

tion as a structure that evolved through many in-

dividual decisions.

• Chronological: adds the dynamic view on how

events and quantities change over time.

2.2.1 Organizational View

The organizational view ((Figure 2), top left) uses

a circular layout to arrange all the major clinics of

the hospital. Circular layouts have proven effective

to show genetic sequences and relationships between

genomic positions (Krzywinski et al., 2009). We

adapted this technique to show the ﬂow of patients in

relation to the organizational structure of the hospital.

The outer circle shows how many patients enter

(blue bars) or exit (red bars) a clinic from outside

of the hospital. The inner circle represents the size

of the clinic, as measured by the number of individ-

ual cases that passes through that clinic, mapped to

the thickness of the black bar. The combined bi-

directional ﬂow of patients between two clinics is

shown as a curved line, whose thickness is propor-

tional to the number of transfers. The lines are drawn

semi-transparent to mitigate occlusion problems. The

order of the clinics around the circle can be adapted

(by department, size, alphabet, etc.) to the current

question.

To reveal further information we follow the prin-

ciple of detail on demand (Shneiderman, 1996). If a

clinic is probed (hover with the mouse cursor) then

SupportingStrategicPlanningwithInteractiveVisualization-ACaseStudyofPatientFlowthroughaLargeHospital

Figure 2: The principal views of the visual analytics application: organizational, systemic, topographic (top row), and chrono-

logical (bottom right). All views are coordinated through brushing and linking to support exploration. Filters (bottom left)

can be used to limit the display to certain organizational units. The yellow lines represent the trajectory of a single case treated

for abdominal metastasis during a four week stay at the hospital.

all the incoming or outgoing transfers from that clinic

are overlaid (Figure 5). If a clinic is selected then only

the transfers to and from that clinic are shown and all

the others suppressed (Figure 6). Probing now shows

quantitative information about the number of transfers

from the selected clinic to the probed one.

2.2.2 Systemic View

The movement of patients between clinics effectively

creates a network of relationships, where clinics that

move more patients between them are closer, or more

similar, than clinics with fewer or no transfers.

To make this network visible we employ a mul-

tidimensional scaling algorithm. Multidimensional

scaling is a family of methods that turns informa-

tion about the similarity of objects into geomet-

ric positions in such a way that, as best as possi-

ble, similar objects are close together and dissimi-

lar ones far apart. It is particularly well suited for

our data because it is able to reproduce non-linear

high-dimensional structures in a lower-dimensional

(i.e., two-dimensional) geometric representation (i.e.,

points on a plane).

Our algorithm is implemented as a spring-mass

model where the clinics are modeled as masses that

are connected pairwise with springs whose resting

length is proportional to the strength of the relation-

ship between the clinics.

Starting with a random layout, the positions adapt

with each iteration and settle into a (hopefully global)

minimum. The algorithm uses several optimizations

to avoid local minima and improve subjective layout

quality, and was inspired by (Chalmers, 1996).

Once the positions of the clinics on what we now

call the systemic view are determined, the transfers

are represented analogous to the organizational view

in order to emphasize their complementary aspect

(Figure 2, top center).

2.2.3 Topographical View

The topographical view (Figure 2, top right) shows

the patient transfers on a geographical representation

of the current hospital campus. The clinics can not

be represented as single units like in the other views,

since the various organizational sub-units of a clinic

are not typically located in a single physical location.

Distinct locations are therefore symbolized as circles

and the organizational sub-units at this location are

represented as ﬁlled dots within this circle. The lay-

out within a circle is randomized and the dots drawn

transparently. This visualization scales well with the

greatly varying number of units at a single location.

2.2.4 Chronological View

The time-dependent behavior of the system is shown

in the chronological view (Figure 2, bottom right).

The in- and out-transfers for each day are shown

as a mirrored stacked bar chart. The dark hues in

the center close to the time axis show the transfers

HEALTHINF2013-InternationalConferenceonHealthInformatics

from (blue, pointing up) and to (red, pointing down)

other clinics, whereas the external entries and exits

are stacked on top and shown in light hues. Internal

and external transfers can also be shown separately

depending on the question, and the mirroring makes

it easy to spot imbalances between in- and out-ﬂows.

The net ﬂow for each day is cumulated and over-

plotted as a black line. This essentially shows the

number of patients that are present in a clinic on a par-

ticular day, which can be integrated to compute care

days.

2.2.5 Interaction

All the views are coordinated through brushing and

linking (Buja et al., 1991), meaning that an action

in one view (e.g., probing, selection) is immediately

reﬂected in all the other views. It has been shown

that interfaces designed around multiple coordinated

views are effective when users need access to details

in addition to getting the overview (North and Shnei-

derman, 2000). They bring beneﬁts of improved user

performance, discovery of unforeseen relationships,

and integration by interaction in addition to integra-

tion by visual design (Shneiderman, 1996).

In order to rationalize and interpret the insights

and hypotheses generated with the four principal

views, it is necessary to drill-down to the level of

individual cases. Cases can be ﬁltered either by or-

ganizational unit that they have visited on their jour-

ney through the hospital, or by various categorical or

numerical case attributes (e.g., destination after dis-

charge, diagnosis, length of stay). When a case is se-

lected from the list of ﬁltered results, its details are

shown both as a table of transfers, as well as a visual-

ization of the whole case history (Figure 3), showing

admission, surgeries, radiology procedures, ambula-

tory visits, and transfers between organizational units

(vertical axis) on a time-line (horizontal axis). At the

same time, the transfers of the selected case are high-

lighted (yellow) in the main views (Figure 2).

In a separate view it is also possible to show all

the ﬁltered cases at the same time. In order to display

several hundred case histories in parallel, their rep-

resentation is condensed to a single line that is only

one pixel high, but still preserves the essential infor-

mation about the case history. Figure 4 shows all the

cases that were classiﬁed under the DRG “craniotomy

with complex procedure”. Lengths of stays vary from

2 to 30 days, with the catalog average deﬁned at 10.1

days. A large number of cases follow the same pat-

tern (surgery on the second day and a transfer on the

third), but deviations from this pattern can easily be

spotted and further investigated.

Figure 3: Cases can be ﬁltered by various attributes (top).

Individual cases in the ﬁlter result can be examined in de-

tail in a table that lists each single transfer (center), or in a

visualization of the whole case history (bottom).

Figure 4: All the ﬁltered cases can be displayed in parallel

to ﬁnd pattens of similarity, or outliers. Case histories are

condensed to a single line representation. This image shows

the variation in procedures and duration of all cases for a

particular DRG, in this example “craniotomy”.

3 RESULTS

The organizational view (Figure 2, top left) shows

the overview of how patients ﬂow through the hos-

pital, and serves as the starting point for analysis. The

outer ring shows that by far the most patients enter

(blue bars) the hospital through the emergency de-

partment (NOTF), followed by gynecology (FRKE),

pediatric surgery (KCHK), and cardiology (KARD).

The clinic for pediatric surgery has a negative bal-

ance for entries/exits (red bars) which means that like

in the emergency department, patients enter the hos-

SupportingStrategicPlanningwithInteractiveVisualization-ACaseStudyofPatientFlowthroughaLargeHospital

pital through this unit but then get transferred into

other units. The opposite is the case for the chil-

dren’s clinic, suggesting a typical path for pediatric

patients. This is indeed the case, since the clinic for

pediatric surgery also operates an emergency room for

children, which accounts for most of these transfers.

Another feature that pops out is that while the clinic

for gynecology has many external entries and exits, it

has very few internal transfers. This is due to most

of the women giving birth without further complica-

tions.

Looking at the transfers on the inside of the circle,

it becomes obvious that the institute for diagnostic,

interventional and pediatric radiology (DIPR) plays

an important role. Highlighting all the transfers that

go out of DIPR (Figure 5) shows that it is a service

center for many of the hospital’s clinics.

Figure 5: Incoming or outgoing (red) transfers can be high-

lighted to reveal relationships between clinics. The radiol-

ogy department (DIPR) plays the role of a service center

and is highly connected to the other clinics.

Further examination shows a less pronounced but

similar pattern for the cardiology clinic (Figure 6).

About 17% of its transfers are from and to the clinic

for internal medicine (KAIM) and about the same

for the clinics of cardiovascular surgery (HGBE), and

neurology (NRLK).

The topographical view in Figure 7 with the car-

diology clinic highlighted shows that its sub-units are

spread across the whole campus in three different lo-

cations, which gives rise to further questions (e.g.,

level-of detail for analysis).

The systemic view reveals a number of interesting

features of the way that the clinics are related based

on the actual ﬂow of patients. In Figure 8 we can

Figure 6: Limiting the view to only transfers that go in or

out of one speciﬁc organizational unit show that the car-

diology clinic (KARD) is also exhibiting a service center

characteristic.

Figure 7: The organizational sub-units of the cardiology

clinic are spread across the hospital campus in three dif-

ferent locations.

see a dense core of highly related clinics in the cen-

ter, surrounded by a ring of clinics with a less central

role, and ﬁnally followed by a number of clinics that

are very peripheral (e.g., clinics of hematology, osteo-

porosis, oncology, infectiology).

Analysis of the logarithmic histogram of transfer

counts showed distinct transitions at thresholds of 300

and 1000 transfers. Figure 9 shows how these thresh-

olds partition the map into three areas. The institute

for diagnostic, interventional and pediatric radiology

DIPR (highlighted in blue) again plays a central role

and sits at the center of the hospital system.

If we limit the view to clinics that have at least

1000 transfers with any of the other clinics, four

groups emerge (Figure 10). At the core we have the

emergency department (intersection between groups

A and B). Group A encompasses the radiology at

the center, and clinics such as cardiology, abdomi-

HEALTHINF2013-InternationalConferenceonHealthInformatics

Figure 8: The systemic view shows a core of highly con-

nected clinics surrounded by six clinics that only play a pe-

ripheral role.

Figure 9: We found two distinct thresholds at 300 and 1000

transfers per clinic that partition the map into three areas.

The radiology department (blue) sits at the very center of

the system.

nal medicine, orthopedic surgery, or internal medicine

surrounding it. Group B also connects to the emer-

gency department, but groups around clinics such as

neuroradiology, intensive medicine, neurology, neu-

rosurgery, or immunology and allergology. Groups

C (radio-oncology and oncology wards) and D (chil-

dren’s clinic and pediatric surgery) are somewhat sep-

arate and less central.

The chronological view provides a view of the pa-

tient ﬂow across time. Looking at the whole hospital

(Figure 11, top), it can be seen that the number of pa-

Figure 10: These clinics make up the core of the hospital

system, as measured by the number of patients that they ex-

change (<1000 per clinic per year). Four groups can be dis-

tinguished and serve as the basis for deﬁning sub-systems

that are central to the future hospital infrastructure.

tients who stay at the hospital is quite constant (black

line), with only minimal seasonal effects. The oscil-

lation pattern is due to the fact, that surgeries tend to

take place at the beginning of the week (peaks) and

patient discharge takes place preferentially before the

weekend (lows).

If we look at the emergency department shown in

Figure 11 (bottom left), we can see that only a small

number of patients stay at this department for a long

time (black line) but there are a lot of patients enter-

ing directly from outside the hospital (light blue bars)

and are transferred to other clinics within the hospital

(dark red bars).

Figure 11 (bottom right) shows the radiology de-

partment. The black line stays at zero because no pa-

tients stay overnight in this clinic. This department

adds to the weekly pattern found in the overall hos-

pital view, since planned interventions are not per-

formed during the weekend.

4 DISCUSSION

The application was developed in an incremental way.

Starting with tables and simple views, diagrams were

reﬁned, new views added, and interactive functional-

ity increased. With each iteration, the understanding

of the relationships in the hospital system was deep-

ened, and the potential for optimizations identiﬁed.

In their daily work, people develop an intuition for

the relations in a system. The view of the observers

SupportingStrategicPlanningwithInteractiveVisualization-ACaseStudyofPatientFlowthroughaLargeHospital

Figure 11: Extracts from the chronological view. The pa-

tient ﬂow for the overall hospital (top) shows a weekly pat-

tern but no seasonal variations. The ﬂow in (blue) and out

(red) of the emergency department (bottom left) shows no

chronological pattern. About two thirds of the patients enter

from outside (light hues) but most of them get transferred to

other clinics (dark hues). Few patients stay overnight (black

line). No patients stay at the radiology department (bottom

right) and nothing happens there on weekends. Gridlines

denote weeks, with months shown as alternating shaded

backgrounds.

however is often limited to their sphere of action. The

dependencies on the next or next-to-next source of in-

ﬂuence are not taken into account sufﬁciently. Our

analysis with this application however allowed us to

gain an overview of the big picture of the hospital

system. The details were validated by experiences of

speciﬁc experts, but we also gained new insights that

went beyond particular knowledge.

By making the ﬂow of patients visible, we were

able to contrast the hierarchical organizational struc-

ture with the actual implemented working relation-

ships. This showed the difference between the op-

erational structures that developed through medical

consequences, and the theoretically deﬁned organiza-

tional structure. Based on this difference, we were

able to describe new sub-systems and identify an or-

ganizational form that corresponds to the current ac-

tual needs.

It was not really a surprise for instance, that the

core functions of a hospital such as emergency de-

partment, operating rooms, and diagnostic functions

appeared in the center of the system, but it was not

expected to be so pronounced. A new insight was the

role of the cardiology clinic as an important service

center for diagnostics. This lead to the decision to

also assign it a central role on the campus. Also new

was the interpretation of the role of the clinic for in-

ternal medicine as being primarily a receiving station

for the emergency room, with the further distribution

into the specialized clinics taking place only one or

two days later.

In summary, we conﬁrmed our two hypotheses,

namely that enriched patient ﬂow data reveals inter-

esting insights into various aspects of a hospital, and

that an exploratory approach to analytics, enabled by

interactive visualization, leads to insights that can not

be gained with standard or automated statistical meth-

ods.

5 FUTURE WORK

The current application was primarily built to gain in-

sights into the strategic planning of a new and evolv-

ing hospital campus (e.g., which clinics should be

placed where to minimize the travel distances for pa-

tients and optimize logistics).

Since the application provides access to various

aspects of a hospital, from high-level overview to in-

dividual treatments within a single case with just a

few clicks, it also has large potential for controlling

tasks and other purposes. We plan to enrich the data

with further treatment cost and performance data, to

support process analysis in the DRG-based manage-

ment context that is currently introduced in our coun-

try. If successful, then one will need to investigate the

possibility of a direct connection to the various hos-

pital information systems in order to obtain real-time

data without compromising the privacy issues.

Another direction of future work is to scale-up the

system to support the planning and controlling of re-

gional, jointly administered hospital clusters, by inte-

grating data from several hospitals. Some of the algo-

rithms and views can be directly scaled-up, but others

will have to be adapted or developed speciﬁcally to

address additional issues.

During the analysis and interpretation of the data,

the need came up to ﬁnd clusters of similar patient

paths through the hospital. This opens interesting

research questions about measures of similarity, and

how to ﬁnd the balance between automated mining

and human-guided visual analysis.

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