257
Chapter 31
Simulation in Healthcare
Tarun Mohan Lal and Thomas Roh
Introduction
As healthcare costs continue to rise and providers move toward optimizing the care delivery pro-
cesses with the Institute for Healthcare Improvement (IHI)’s triple aim of improving patient expe-
rience, improving health of populations, and reducing costs as the eventual goal, the ability to
assess trade-os among resource utilization, service, and operating costs is becoming more and
more important. Simulation, successfully applied in other industries, is an industrial engineer-
ing methodology that provides the ability to assess such trade-os and is now gaining traction
in healthcare. is chapter discusses the use of simulation modeling in studying and improving
health systems. e chapter will cover the basics of discrete event simulation model development,
and its common applications in healthcare.
Contents
Introduction .............................................................................................................................257
Denition of Simulation and Its Importance and Growth ........................................................258
Example: Major Applications in Healthcare .............................................................................258
Steps in a Simulation Study ......................................................................................................259
Example Project ...................................................................................................................... 260
Situation ............................................................................................................................. 260
Objective ............................................................................................................................ 260
Analysis .............................................................................................................................. 260
Results .................................................................................................................................261
Advantages and Disadvantages of Simulation .......................................................................... 262
Software Decision ................................................................................................................... 262
Future of Simulation in Healthcare ......................................................................................... 263
Bibliography ............................................................................................................................ 263
258 ◾ Tarun Mohan Lal and Thomas Roh
Denition of Simulation and Its Importance and Growth
e term simulation refers to imitationorenactmentofa future event. Simulation in healthcare
sometimes refers to physical simulation in which education centers are set up for training care
providers to practice real patient care in an articial environment. In a medical context, the words
model and simulation can have several meanings and are beyond the scope of this chapter. e
focus of this chapter will be on an analytical computer simulation technique known as discrete
event simulation that is often used by management engineers or operations research experts to
evaluate, optimize, and improve care delivery processes. In this technique, historical data are used
to imitate or simulate the operations of various kinds of healthcare systems to provide an approxi-
mation of future outcomes. As an example of simulation, consider a hospital that is contemplating
a decision to add beds in the intensive care unit to reduce the time that a patient must wait to
be moved into an inpatient unit. It is not certain that adding more beds would truly reduce the
congestion in the system, and even if it does, the number of additional beds required to make this
a nancially viable option is unknown.
A healthcare system is often referred to as a system of systems due to multiple components that
are both operationally and managerially dependent. ere is also a high level of variability, mak-
ing them stochastic in nature. When the relationships that compose the system are simple, it is
possible to use simple mathematical methods to obtain exact information on questions of interest
and provide an analytical solution. However, for complex systems such as most healthcare pro-
cesses, with multiple moving parts, deterministic methodologies do not give the desired accuracy.
e use of simulation is crucial in order to estimate the desired characteristics.
Simulation is used when the proposed change cannot be implemented without a signicant
change in practice that might be too disruptive or too expensive. It can be used to justify improve-
ments or to nd the bottlenecks in a system without a huge investment. Going back to the exam-
ple of additional beds in the ICU, for example, it would certainly not be cost eective to add beds
and then remove them later if it does not work. However, discrete event simulation could throw
light on the question by simulating the operations of the hospital as they are currently and as they
would be if the number of beds were increased.
Example: Major Applications in Healthcare
Application areas for simulation in healthcare are numerous and diverse. e following is a list
of some of the problems in healthcare for which simulation has been found to be a useful and
powerful tool.
◾ Hospital operations
− Bed occupancy and utilization
− Stang analysis
− Operating room scheduling
− Patient ow analysis
◾ Emergency department
− Patient triage and its impact on resource needs
− Number of beds needed
− Patient ow from emergency department to hospital inpatient units
− Stang analysis
Simulation in Healthcare ◾ 259
◾ Outpatient clinics
− Patient scheduling policies
− Workload balancing
− Facility analysis of lobby size, number, and design of exam room space
− Equipment utilization
◾ Call center
− Patient appointment scheduling oce stang needs
◾ Healthcare supply chain
− Blood platelets usage and optimal inventory levels
− Pharmaceutical needs demand and inventory levels
e above list provides examples of some common application areas in dierent components of
healthcare delivery systems and is not meant to be exhaustive. Further information can be found
in numerous articles on applications of simulation methodology in healthcare.
Steps in a Simulation Study
Although simulation is becoming a very widely accepted methodology in healthcare, its usefulness
and implementation is very dependent on the process used to build the models. It is important
to keep in mind that, like any scientic method, simulation modeling is most successful when
attention is paid to the process of building a model that includes statistical experimental design
to budget and personnel management. is section outlines the recommended framework to be
adopted in building simulation models as described in Figure31.1.
e very rst step in a simulation study is to dene the specic problem and associated goals
and metrics. e project can then move forward into data collection and early statistical analysis
of the data. At this point, one starts conceiving how the model is going to be built, what further
information might be needed, and maps out the ow of the system. Building the model is a
phase that is entwined with the analysis. e analysis drives some of the things that the modeler
can and cannot do in building the model and sets the quantitative guidelines. e other part
of model building is the art of reproducing actual processes, rules, behaviors, and policies in an
articial model.
Define Problem Build Model
Model Conceptualization
Verification
Implementation
Validation
Validated
Verified
No
Yes
Failed
Failed
Set Objectives
of Project
Additional
Testing
Develop New
Hypothesis
Scenario Testing
and Output
Analysis
Results and
Presentation
Feedback
Data Collection
and Analysis
Figure 31.1 Phases in a simulation study.
260 ◾ Tarun Mohan Lal and Thomas Roh
After the model is built, the simulation needs to be veried. Verication is checking the coding
of the simulation and ensuring that the inputs are producing the intended outputs. Validation is
ensuring that the simulation model closely resembles the real-life system. If either test fails, you
have to go back and change the model. Once an agreed upon model is developed, the simulation
is used to answer what-if questions and output comparable metrics. e results are then presented
and feedback is gathered. Usually, new questions arise, so new scenarios are tested.
Once an acceptable solution is found, implementation begins. After successful implementa-
tion, a new problem can be identied and we return to the model-building phase. e rst step
in data analysis is to make sure that the data you have is correct. After that, the data needs to be
“cleaned” into a usable format, during which one learns the data limitations and the complexity of
the problem. Performance measures are then developed so that the metrics will reect the direct
eect that changes have on the problem.
Creating the simulation to mirror real life is not an exact science. e model should be built as
simply as possible because complexity decreases accuracy. e data limitations need to be assessed
along with the limitations of articially recreating the real-life system. More likely than not, the
modeler will have to go back several times to data collection and analysis during the building of
the model.
Stakeholder involvement is also a key element in any successful project. Stakeholders are more
accepting of the model when the analyst has visibly been learning their work. Simulation is not an
easy concept to understand. Most people label it as a “black box.” Providers will not trust some-
thing that they do not understand. Simple graphs create a foundation for understanding and a
gradual path for change. Leadership will strongly challenge simulation studies if the results do not
coincide with their hypotheses, and do not shape results to the department’s expectations.
Example Project
Situation
An outpatient clinic at an academic medical center oers comprehensive evaluations and con-
sultations to patients undergoing an anesthesia-related, low- to medium-risk, planned surgery
or procedure. Implementation of surgical process improvement initiatives across various surgical
specialties led to an increase in demand for the clinic services. e administration believed that
insucient consultation rooms would hinder their ability to expand services.
Objective
Determine the capacity requirements, both facility and personnel, needed to support the expected
growth in patient volume.
Analysis
Preliminary investigational process review was conducted through sta interviews and patient
shadowing in order to understand the processes and patient ow at the clinic. Discrete event simu-
lation was used to model the current state of the system. Inputs into the model included scheduled
patient appointments, duration of each process step, and sta work schedules.
Simulation in Healthcare ◾ 261
e simulation model of the current system, Figure31.2, indicated the existence of under-
utilized capacity. is initial current state model was used as a framework to investigate possible
improvements in the system.
Multiple future state scenarios were developed to evaluate the impact of potential changes on
scheduling patterns and capacity reallocation on patient wait time and resource utilization. e
most optimal future state was determined, Figure31.3, based on the utilization of consultation
rooms and clinical sta.
Results
e simulation model results indicated that there were a sucient number of rooms at the outpa-
tient clinic to meet the increase in demand. However, imbalance in patient scheduling across the
Exam Rooms
7:30
7:50
8:10
8:30
8:50
9:10
9:30
9:50
10:10
10:30
10:50
11:10
11:30
11:50
12:10
12:30
12:50
13:10
13:30
13:50
14:10
14:30
14:50
15:10
15:30
15:50
16:10
16:30
16:50
17:10
RT Providers
Current State of the System
Optimum Utilization
Figure 31.2 Current state of outpatient clinic.
Exam Room RT Physician
Future State after Scheduling Change
7:15
7:40
8:05
8:30
8:55
9:20
9:45
10:10
10:35
11:00
11:25
11:50
12:15
12:40
13:05
13:30
13:55
14:20
14:45
15:10
15:35
16:00
16:25
16:50
17:15
Figure 31.3 Future state after simulation of outpatient clinic.
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset