Improving hospital evacuation planning using simulation.

Abstract

Hospital evacuation in the event of a hurricane is a complex and unpredictable process. Recent natural disasters have called attention to the importance of a timely evacuation plan. The success of an evacuation greatly depends on developing and evaluating alternative plans. However, there is no standard approach to address the issues of a hospital evacuation. This research describes the development of a simulation model and initial analysis to assess the effectiveness of an evacuation plan given different scenarios and resources.

Full text

Proceedings of the 2006 Winter Simulation Conference
L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds.
ABSTRACT
Hospital evacuation in the event of a hurricane is a com-
plex and unpredictable process. Recent natural disasters
have called attention to the importance of a timely
evacuation plan. The success of an evacuation greatly
depends on developing and evaluating alternative plans.
However, there is no standard approach to address the is-
sues of a hospital evacuation. This research describes the
development of a simulation model and initial analysis to
assess the effectiveness of an evacuation plan given dif-
ferent scenarios and resources.
1 INTRODUCTION
Hospitals are usually considered a safe haven and support
system for the people involved in an emergency situation.
As the foundation for many emergency response plans,
hospitals are rarely considered subjects for evacuation.
This research focuses on the event of a hurricane in which
a hospital must decide to evacuate and relocate its patients
and staff to nearby shelters.
The objective of our research is to propose simulation
modeling as a tool to understand, analyze, and improve
hospital evacuation plans. This research evaluates the ef-
fects of varying transportation, sheltering, and staffing
plans for hospitals, while observing the effects on evacua-
tion time and number of patients evacuated.
In surveying South Carolina and Florida hospitals,
data were collected on the present state of evacuation
plans. As of 2004, the Department of Health and Envi-
ronmental Control in South Carolina requires all hospitals
to develop and/or update their evacuation plans (DHEC
2004). However, risk managers have limited actual
evacuations to aid them in refining and updating their
hospital’s plans. Some hospitals actually are forced to
perform costly mock evacuations to evaluate many of the
responses our research is modeling. We feel this research
will benefit hospitals because simulation provides a
method of testing many different scenarios over many
replications to observe the outcomes that are critical to a
successful evacuation.
Hospital evacuation planning involves several com-
plex, interrelated steps. The evacuation process can be
thought of as a set of activities, some of which are con-
strained by resources, but all of which must be completed
for success. Taaffe and Tayfur (2006) propose optimiza-
tion-based models that measure the ability to effectively
evacuate patients from a hospital based on evacuation
cost, clearance time and patient risk. Yet, with cost, time
and risk as competing objectives, there is not one clear
recommendation. Moreover, their work assumes all
events and tasks during the evacuation are deterministic in
nature. In addition to the uncertainties surrounding a hur-
ricane event, the available resources for accomplishing an
evacuation plan will be unique at any given time. While a
mathematical programming approach may be applicable
at a tactical level, there is a need for conducting simula-
tion analysis to understand the interdependencies of op-
erational-level decisions (Law and Kelton 2000). In this
paper, we provide insight into these interdependencies
through the use of simulation.
2 RELATED LITERATURE
Researchers have typically focused on general population
evacuations as they pertain to the use of roadway infra-
structure to move people away from a hazard (see, e.g.,
Sheffi et al. 1982, Pidd et al. 1996, Hobeika and Kim
1998, Franzese and Joshi 2002, Chang 2003, and Cova
and Johnson 2003). Some researchers have addressed as-
pects of the evacuation problem such as decision making
procedures (see, e.g., Tufekci 1995, Gladwin 2001, and
Sorensen et al. 2004) and emergency preparedness train-
ing (Pollak et al. 2004), and Frantzich (1997) considers
risk evaluation where hospitals serve as support for first
responders. Vogt (1991), McGlown (1999), and
McGlown (2001) even consider the decision-making
IMPROVING HOSPITAL EVACUATION PLANNING USING SIMULATION
Kevin Taaffe
Matt Johnson
Desiree Steinmann
Department of Industrial Engineering
Clemson University, 110 Freeman Hall
Clemson, SC 2934-0920, U.S.A.
5091-4244-0501-7/06/$20.00 ©2006 IEEE

Taaffe, Johnson, Steinmann
process regarding evacuation of health care facilities and
special needs populations. However, the problem of de-
veloping robust hospital evacuation plans using quantita-
tive techniques is still largely unresearched. Taaffe et al.
(2005) discuss the many issues and complexities inherent
in not only hospital evacuation planning but also plan
execution. Recently, the United States Government Ac-
countability Office released a report that summarized pre-
liminary observations of the issues surrounding health
care facility evacuation due to hurricanes (GAO 2006).
However, the report does not address how to improve,
suggest, or implement more robust evacuation plans.
Consideration of hospital evacuation occurs when a
threat to the population grows to include the hospital it-
self. However, the hospital under consideration is usually
an integral part of a broader emergency response plan to
deal with those injured or exposed to the threat, which
could result in a decision not to evacuate despite the threat
to the hospital. While hurricanes and floods were the
primary concern for mass evacuation planning for many
years, a more concentrated effort is now being given to
hazardous material spills and terrorist incidents (see, e.g.,
Rogers 1994 and Lindell 2004). Broadening the number
of possible threats expands both the scope of the problem
and the number of hospitals which may be at risk.
One of the more difficult problems with transporta-
tion and sheltering plans is when the threat grows to in-
clude the “safe” facilities or hospitals. The U.S. hurricane
season of 2004, for example, severely taxed resources
both by direct impact and by the extent and frequency of
the storms. It was not unusual for evacuees to find them-
selves the target of a storm after evacuation (Hoffman
2005). Given that hurricane-force winds can occur over a
wide swath, there is a strong likelihood that multiple hos-
pitals will undergo evacuation procedures (and each of
these hospitals will be subject to potential critical systems
failures). To date, there has been no documented research
to address the logistics of such an evacuation.
3 MODELING HOSPITAL EVACUATIONS
As previously stated, Taaffe and Tayfur (2006) compare
various resource and vehicle transport allocations for a
hospital evacuation based on a deterministic optimization
model. Due to the level of detail included in the model
representing the evacuation decision-making process, it
may become prohibitively difficult to formulate each de-
cision mathematically. Even assuming that we can ade-
quately represent each task or operation, we must also
consider the variability in the duration of each of these
sequential events that the hospital must complete during
an evacuation. As a next step, this analysis could be ex-
tended to account for stochastic elements directly into the
optimization model. However, this will not be a straight-
forward extension, and additional research will be re-
quired in this area. Instead, we incorporate the uncer-
tainty of task duration and timing through the
development of a simulation model. The next section
presents the assumptions that were used in the analysis.
Based on data inputs and evacuation guidelines from
several coastal hospitals in South Carolina and Florida, a
model has been developed that determines the average
evacuation time and the overall clearance (or evacuation
completion) time, based on the parameters of a proposed
evacuation plan. In this analysis, we only test a subset of
possible representations, and alternative evacuation plan-
ning models are currently being investigated with input
from the operators and risk managers at the hospitals. We
selected our scenarios based on the operating conditions
at Cape Canaveral Hospital (Florida) and Beaufort Me-
morial Hospital (South Carolina), both mid-sized facilities
with 150-200 beds. It is assumed that there is one hospital
for which we are evaluating its evacuation plan options.
This hospital can send its patients, medical staff, and ba-
sic supporting equipment (e.g., IV hookups) to various
sheltering facilities in the region. The logistics of trans-
porting advanced medical equipment and supplies is omit-
ted. Moreover, the sheltering facilities are assumed to be
hospitals, and they will typically have most of the pa-
tients’ medical equipment needs.
In this model, those patients who will be part of the
evacuation plan are addressed. Also, all tasks related to
preparing the patient for evacuation staging have been ag-
gregated into a single stochastic delay. This could include
preparing the patient for moving from the patient room,
processing any/all paperwork regarding the move to a
sheltering facility, moving to a first-floor staging area,
etc. The evacuating hospital faces limited resources in
terms of the number and size of transporting vehicles,
staging area for transport, support staff to accompany
transferred patients, and bed capacities at the sheltering
facilities.
3.1 Model Structure
Using discrete-event simulation modeling, we can meas-
ure the effectiveness of evacuation policies by modeling
human behavior and other stochastic decisions that may
not be handled adequately in the optimization model. The
goal is to design a set of experiments that systematically
test alternate flows, staging, and scheduling of events dur-
ing an evacuation. While there may be a universal set of
experiments of interest regardless of the specific hospital
being evacuated, there will still exist a need to study
unique hospital characteristics to be able to accurately as-
sess a plan‘s performance.
There are several approaches for model development,
and we describe one potential methodology for incorpo-
rating the administrator/staff/patient decisions into the
simulation. In Figure 1, we assume that there are three
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Taaffe, Johnson, Steinmann
main areas of control within the evacuation process: storm
control, patient / medical staff control, and administrator /
risk manager control.
Figure 1: Simulation Model Structure
Using simulation, not only can we more accurately
account for the stochastic nature of human decisions, but
we can also characterize the uncertainty in the timing, se-
verity and duration of the hurricane event, and provide 3-
or 6-hour storm-track updates. It would likely have an
effect on the speed at which an evacuation can be imple-
mented, the time at which an evacuation is initiated, and
the number of patients that can be safely evacuated. This
functionality would be provided within the storm control
function area.
Within the patient / medical staff function area, we
will have the ability to monitor individual patient rooms
and obtain evacuation status on each room (e.g., room oc-
cupied or not, patient type, evacuation decision (release,
transfer, stay), and expected release or transfer time, if
applicable). If there is additional patient/medical staff in-
teraction that affects release or “ready for release” time,
this information can be included in the model.
The third function area is administrator / risk man-
ager control. This is a very important piece in the model-
ing process in that we can allow for policy changes in the
midst of a hurricane event. This will provide hospital
management with the ability to make decisions that
change the responsibilities of doctors, nurses, and staff in
how they are currently handling patients and the overall
evacuation.
Ultimately, we would like to include appropriate de-
tail in all of the control areas listed in Figure 1. In fact,
most of the body of code has been included in the base
simulation model. However, we have chosen to focus on
the patient / medical staff control function for this first re-
search paper on hospital evacuation.
We have developed the simulation model using the
commercially-available simulation software package
Arena, which is especially suited to representing process
flows. We recognize that hospital evacuations can be in-
fluenced by the interactions of different persons (patients,
nurses/staff, risk managers, and local emergency man-
agement personnel). As an area of future research, we
plan to test the use of agent-based modeling as a means of
more accurately representing these human behavioral de-
cisions (see, e.g., Deadman 1999, Bonabeau 2002a, and
Chen 2003). We may also be able to incorporate optimi-
zation within the simulation models that we develop.
Highly detailed, discrete-event simulation models can run
extremely slow, rendering near-term planning exercises
fruitless. Through our extensive testing, we will incorpo-
rate the results and learning experiences into a more ro-
bust evacuation planning procedure.
3.2 Model Assumptions
We assume that there are three acuity levels. An acuity
level 1 patient is denoted as any patient who is a candi-
date for early release, and these patients can expect to be
released 24 hours earlier than normal to reduce the num-
ber that need to be transported to another facility. How-
ever, not all acuity level 1 patients will be released, based
on any number of reasons where care cannot be provided
away from the hospital. An acuity level 3 patient repre-
sents a critical care patient, such as those either waiting
for or in recovery from a serious operation, or those that
have an extreme ailment. All other patients would fall
into the larger, middle group, which we denote as acuity
level 2. There are no priorities placed on the order in
which patients are evacuated (i.e., all three patient groups
will be evacuated simultaneously based on the availability
of the appropriate transport vehicles). In future research,
we will recognize that certain acuity level 3 patients may
require immediate evacuation to obtain the care that they
need, possibly preempting a planned transfer of acuity
level 1/2 patients.
Once a nurse is assigned to assist in transporting a
group of patients, he or she will remain with the patients
until the end of the evacuation to provide necessary care.
Based on feedback from hospitals participating in the data
collection effort, it is assumed that one nurse is required
for every 5/5/2 patients of acuity level 1/2/3 for transport-
ing patients.
In this model, there are three sheltering facilities,
each of which can accommodate any of the patient types.
We also include an overflow shelter to accommodate ad-
ditional evacuated patients, when originally-anticipated
shelter capacity is not provided for any number of rea-
sons. The evacuating hospital can have up to 50 patients
of each patient type, and the sheltering facilities are also
assumed to have up to 50 beds available. Vans and ambu-
lances are available for transport, and we will vary the
number available across different experiments. Each ve-
hicle can travel at speeds between 30 and 45 miles per
hour, and all facilities are assumed to be 100 miles away
from the evacuating hospital. We do not consider any
costs in this model.
Administrator / Risk
Manager Control
Patient / Medical
Staff Control
Storm Control
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Taaffe, Johnson, Steinmann
3.3 Model Detail
In this research, we are proposing simulation modeling as
a tool to understand, analyze, and improve hospital
evacuation plans. The model allows for adjusting any of
the assumptions listed in Section 3.2 that provide input
data or define a particular test scenario, including initial
patient count prior to storm creation, nursing staff levels
and allocation, evacuation transportation capabilities, and
number of available shelters. The analysis provided in this
paper will concentrate on evacuation times, both for indi-
vidual patients and the overall evacuation completion
time.
In modeling the hospital evacuation, we created five
submodels that capture data input, patient control, evacua-
tion, storm updates, and risk manager control. Within the
data input submodel, the system creates patients and at-
taches each an array of assignments. Two important pa-
tient attributes are acuity level and expected release time.
Based on either of these attributes, a patient may leave the
hospital before evacuation begins. Also, according to
acuity level, patients require a certain amount of time and
staff members in order to evacuate the hospital.
Currently the number of staff members is pre-defined
within the resource configuration. Each group represents
a floor of the hospital and the corresponding patients by
acuity level. For example, nurses assigned to floor 1 are
responsible for acuity level 1 patients.
Patients continue to the patient control submodel
which monitors room occupancy and patient evacuation
status. After being assigned a bed, the patients wait for
storm updates. If the patient’s expected release time oc-
curs before evacuation begins or within six hours of
evacuation, then that patient prepares for release and exits
the hospital system. Otherwise, patients will take part in
the hospital evacuation. Additional patients can also ar-
rive to the hospital prior to the evacuation, creating the
ability to have an unpredictable number of patients at the
beginning of an evacuation.
Our efforts have been focused on the evacuation
process itself. First, each patient requires a nurse to move
from a hospital room to the staging area, and the time to
relocate the patients is a function of the patient’s acuity
level. Then, patients are divided into groups ready for
transporting to shelters. Each group requires a nurse/staff
member before proceeding to the loading area.
Depending on the patient’s or group’s condition, the
amount of time and resources required to move the patient
to the staging area and then load each patient onto a trans-
port vehicle may range from a few minutes to nearly an
hour.
As patients arrive to the staging area, they are as-
signed to travel to specific sheltering facilities. The cur-
rent model utilizes two types of transport vehicles: vans
for acuity levels 1 and 2 and ambulances for acuity level
3. Both types of vehicle types have the capacity to hold
one batch or group of patients. If capacity is no longer
available at the assigned shelter when the vehicle is ready
to depart, the remaining patients travel to an overflow
shelter with unlimited capacity. Once a van or ambulance
arrives and unloads at a sheltering facility, it returns to the
evacuation facility with similar transport delay times.
Once all patients are evacuated, the evacuation is de-
clared complete, and an overall evacuation completion
time is recorded. While this section does not present an
exhaustive list of issues in this area, these are certainly
among the most important. It is doubtful that any plan-
ning process will truly address all issues. However, the
robustness of the plan will depend on solid coverage of
the most essential issues.
4 EVACUATION MODEL RESULTS
In this section, we present the findings from the base
model described in Section 3. We consider the following
resource assignments in the base model:
10/10/25 nurses for acuity level 1/2/3 patients
3 shelters each with a capacity of 50 patients
1 overflow shelter with unlimited capacity
3 vans and 3 ambulances
For every test conducted, 20 simulation replications were
run, all producing fairly low variation (reported half-
width values in Arena were consistently less than one
hour). This is due in large part to some simplifying as-
sumptions that were made in the base model. Further
model development will remove such assumptions, result-
ing in an even more stochastic environment to consider.
First, we tested several initial patient counts when us-
ing the resources defined for the base model, and the av-
erage evacuation times per patient and average comple-
tion times are reported in Table 4.1.
Table 4.1: Base Model
Patient
Count
Avg.
Evacuation
Time (hrs)
Avg Com-
pletion
Time (hrs)
50/50/50 40.4 65.1
40/40/40 31.1 47.6
30/30/30 24.0 36.5
50/40/30 31.5 46.5
30/40/50 36.2 65.3
The correlation between patient counts and evacua-
tion times is not a major finding. Instead, the purpose of
running each test was to observe the magnitude of the
change in evacuation time across each test. Note that
these patient counts represent hospitals similar in size to
Cape Canaveral Hospital and Beaufort Memorial Hospital
(input data sources from Section 2). Also note that these
patient counts do not include the patients that could be re-
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Taaffe, Johnson, Steinmann
leased early (and, thus, did not require evacuation). For a
hospital with 90 patients to evacuate, evenly split across
all three acuity levels, the average evacuation time per pa-
tient is 24 hours, with an overall completion time of 36.5
hours. However, when increased to 150 patients
(50/50/50 for acuity levels 1/2/3), the completion time
approached three full days. The travel time assumptions
are still quite liberal, which means that any additional de-
lays on the roadways would only further exacerbate the
delay in finishing the evacuation.
From the input data gathered, the critical task is
transportation and not building evacuation. In other
words, even if the hospital is evacuated more quickly, it
would not change the patient evacuation times or evacua-
tion completion time since transportation is the bottle-
neck. If, however, the transportation element can be re-
duced to requiring only a few hours, then the ability to
efficiently prepare patients for evacuation would become
increasingly important.
Note that the base model assumes only three ambu-
lances and three vans. Based on the results, the bottle-
neck operation is the evacuation of acuity level 3 (critical
care) patients. In order to gauge the effect of adding
transport vehicles, we perform additional tests on each pa-
tient count. The new tests, shown in Tables A-1 – A-5 in
the Appendix, report 12 combinations of initial vehicle
requirements (including the base model). Increasing the
number of ambulances has a greater positive impact than
increasing the number of vans. Also, with fewer acuity 3
patients, the system approaches its optimal (minimum)
evacuation time with a fewer number of vehicles. These
results support the idea that acuity 3 patients create the
bottleneck in the system and have the greatest effect on
the results.
This simulation model has the potential to test other
changes in resources. For instance, vehicle capacities may
be varied to allow more or less patients per vehicle. Dis-
tance and travel times between the hospital and shelters
can also be changed to demonstrate the impact of travel
obstacles and shelter location relative to the hospital. An-
other variable to consider is the number of nurses for each
type of patient. Holding the patient count constant, we
can test the effect of the number of nurses assigned to
each acuity level. In addition, we may vary the distribu-
tion of time required to prepare the patient for evacuation.
The purpose of these additional tests would be to deter-
mine the resources with the greatest impact on the success
of the evacuation, and these resources will be the focus
for improving the system.
5 CONCLUSIONS AND FUTURE RESEARCH
In this paper, we proposed a simulation model as a
method of understanding, analyzing, and improving hos-
pital evacuation plans. The model utilizes resource re-
quirement information to provide evacuation time data
that can aid risk managers in making decisions regarding
their hospital’s plans for evacuation.
The base model has great potential to simulate the
evacuation processes in an increasing amount of detail in
future research. For example, new patients may not arrive
to the hospital after the simulation run begins. However,
the model has the potential to check in new patients, as
long as the evacuation has not begun. New patients are
assigned information regarding acuity and expected re-
lease time. If a bed is available, the patient enters the hos-
pital; otherwise, the patient leaves the system.
In addition, a more advanced and accurate representa-
tion of staffing levels is under development at this time.
This alternative process allows staff members to enter the
system according to a hospital’s current staffing schedule.
The staff members are separated into different shifts and
specific floor assignments.
While this research focuses on hospital evacuation
due to hurricanes (where the evacuation can be planned),
no-notice evacuation of hospitals would be an extension
with great importance. Research on building evacuations
in non-hospital settings would likely be included in such
an extension.
APPENDIX: ADDITIONAL MODEL RESULTS
Table A-1: Alternative Test Set 1
TEST SET 1: Patient Count 50/50/50
Evacuation Time (hrs) Num. of
Vans
Num. of
Ambulances Average
per Patient
Average
Completion
Time (hrs)
3 3 40.4 65.1
3 6 37.6 56.4
3 9 36.8 56.2
3 12 36.4 56.0
Average 37.8 58.4
5 3 38.8 65.4
5 6 35.8 49.7
5 9 35.0 49.6
5 12 34.7 49.6
Average 36.1 53.6
8 3 38.1 65.3
8 6 35.2 48.3
8 9 34.3 48.0
8 12 34.0 48.0
Average 35.4 52.4
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Taaffe, Johnson, Steinmann
Table A-2: Alternative Test Set 2
TEST SET 2: Patient Count 40/40/40
Evacuation Time (hrs) Num. of
Vans
Num. of
Ambulances Average
per Patient
Average
Completion
Time (hrs)
3 3 31.1 47.6
3 6 29.4 45.3
3 9 29.2 45.3
3 12 29.1 45.2
Average 29.7 45.9
5 3 29.7 47.1
5 6 28.1 41.6
5 9 27.9 41.1
5 12 27.8 41.1
Average 28.4 42.6
8 3 29.2 47.1
8 6 27.5 39.8
8 9 27.3 39.9
8 12 27.2 39.6
Average 27.8 41.6
Table A-3: Alternative Test Set 3
TEST SET 3: Patient Count 30/30/30
Evacuation Time (hrs) Num. of
Vans
Num. of
Ambulances Average
per Patient
Average
Completion
Time (hrs)
3 3 24 36.5
3 6 22.6 35.3
3 9 22.5 35.3
3 12 22.3 35.3
Average 22.9 35.6
5 3 23.1 36.4
5 6 21.8 35.3
5 9 21.6 35.4
5 12 21.4 35.4
Average 22.0 35.6
8 3 22.7 36.3
8 6 21.4 35.3
8 9 21.2 35.4
8 12 21.0 35.4
Average 21.6 35.6
Table A-4: Alternative Test Set 4
TEST SET 4: Patient Count 50/40/30
Evacuation Time (hrs) Num. of
Vans
Num. of
Ambulances Average
per Patient
Average
Completion
Time (hrs)
3 3 31.5 46.5
3 6 30.5 46.1
3 9 30.3 46.1
3 12 30.2 46.3
Average 30.6 46.3
5 3 29.2 41.1
5 6 28.1 40.9
5 9 28.0 41.1
5 12 27.9 41.1
Average 28.3 41.1
8 3 28.5 39.5
8 6 27.5 39.5
8 9 27.3 39.6
8 12 27.3 39.5
Average 27.7 39.5
Table A-5: Alternative Test Set 5
TEST SET 5: Patient Count 30/40/50
Evacuation Time (hrs) Num. of
Vans
Num. of
Ambulances Average
per Patient
Average
Completion
Time (hrs)
3 3 36.2 65.3
3 6 32.5 47.1
3 9 31.6 45.1
3 12 31.3 45.0
Average 32.9 50.6
5 3 35.3 65.1
5 6 31.6 46.9
5 9 30.7 41.7
5 12 30.3 41.4
Average 32.0 48.8
8 3 34.8 65.1
8 6 31.1 47.0
8 9 30.2 41.5
8 12 29.9 41.3
Average 31.5 48.7
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Taaffe, Johnson, Steinmann
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AUTHOR BIOGRAPHIES
MATTHEW JOHNSON is an undergraduate student in
the Department of Industrial Engineering at Clemson Uni-
versity. He graduated in May of 2006. His email address
is <mbjohns@clemson.edu>.
DESIREE STEINMANN is an undergraduate student in
the Department of Industrial Engineering at Clemson Uni-
versity. She graduated in May of 2006. Her email ad-
dress is <dsteinm@clemson.edu>.
KEVIN M. TAAFFE is an assistant professor in the De-
partment of Industrial Engineering at Clemson University.
His research interests include transportation and logistics
systems analysis as well as production, inventory, and
demand management. He is a Senior Member of IIE, as
well as a member of INFORMS, Transportation Research
Board, and Decision Sciences Institute. His email address
is taaffe@clemson.edu and his Web address is
http://people.clemson.edu/~taaffe/.
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