COMBINING DISCRETE EVENT SIMULATION MODELING AND
“TRANSFORMING CARE AT THE BEDSIDE” METHODOLOGY
TO IMPROVE THE SURGICAL PATIENT EXPERIENCE
by
Jennifer Christine Jozefiak
B.S. History, University of Michigan, 2011
Submitted to the Graduate Faculty of
Health Policy & Management
Graduate School of Public Health in partial fulfillment
of the requirements for the degree of
Master of Health Administration
University of Pittsburgh
2013
UNIVERSITY OF PITTSBURGH
GRADUATE SCHOOL OF PUBLIC HEALTH
This essay is submitted
by
Jennifer Christine Jozefiak
on
April 16, 2013
and approved by
Essay Advisor:
Jagpreet Chhatwal, PhD
_________________________________
Assistant Professor
Department of Health Policy & Management
Graduate School of Public Health
University of Pittsburgh
Essay Reader:
Natasa Vidic, PhD
________________________________
Assistant Professor
Department of Industrial Engineering
Swanson School of Engineering
University of Pittsburgh
Essay Reader:
Michael A. Grace, MBA, FACHE
Vice President, Operations
UPMC Presbyterian Shadyside
________________________________
ii
Copyright © by Jennifer Christine Jozefiak
2013
iii
Jagpreet Chhatwal, PhD
COMBINING DISCRETE EVENT SIMULATION MODELING AND
“TRANSFORMING CARE AT THE BEDSIDE” METHODOLGY TO IMPROVE
THE SURGICAL PATIENT EXPERIENCE
Jennifer Christine Jozefiak, MHA
University of Pittsburgh, 2013
A case study of a process redesign at UPMC Shadyside Hospital that combines
engineering concepts with an emphasis on discrete event simulation modeling, with the
existing hospital process improvement methodology, Transforming Care at the Bedside.
The process in question is the surgical patient arrival and transport process, which begins
in the surgical family lounge and ends in the pre-operative preparation unit. The current
process design is perceived as the most efficient, but tends to be focused on the operational
needs of the hospital, rather than the needs of the patient. The objective of the project is to
design a new process to position the organization in a favorable position in terms of patient
satisfaction and patient experience measures, without a significant increase in resource
costs. Process alternatives are examined considering both efficiency and the degree of
patient centeredness. A set of recommendations with a proposed implementation plan is the
objective of the project. The case study has the potential to have an impact on public health
by influencing the quality improvement work at other hospitals to shift the United States
healthcare system from a volume-based to a value-based system.
iv
TABLE OF CONTENTS
PREFACE ........................................................................................................................ IX
1.0
INTRODUCTION .............................................................................................1
1.1
CURRENT HEALTHCARE ENVIRONMENT ...................................1
1.2
HOSPITAL ENVIRONMENT ................................................................3
2.0
1.2.1
UPMC Shadyside Background .........................................................3
1.2.2
Problem Description ..........................................................................4
LITERATURE REVIEW.................................................................................8
2.1
3.0
IMPROVEMENT METHODOLOGIES ...............................................8
2.1.1
Discrete Event Simulation Modeling ................................................8
2.1.2
Transforming Care at the Bedside Methodology ..........................10
2.1.3
Other Industrial Engineering Concepts.........................................11
DATA AND DESIGN .....................................................................................13
3.1
DATA .......................................................................................................13
3.2
MODEL ASSUMPTIONS .....................................................................15
3.3
MODELS .................................................................................................15
3.3.1
Current State ....................................................................................16
3.3.2
Alternative One ................................................................................19
3.3.3
Alternative Two ................................................................................21
v
3.3.4
Alternative Three .............................................................................23
3.3.5
Alternative Four ...............................................................................24
3.4
4.0
MODEL VALIDATION ........................................................................26
FINDINGS .......................................................................................................29
4.1
EFFICIENCY ANALYSIS ....................................................................29
4.2
IMPACT ON PATIENT SATISFACTION .........................................31
4.3
EASE OF IMPLEMENTATION ANALYSIS .....................................32
5.0
DISCUSSION ..................................................................................................34
5.1
MODEL LIMITATIONS .......................................................................34
5.2
BARRIERS TO SUCCESS ....................................................................34
5.3
DRIVERS OF SUCCESS .......................................................................36
6.0
RECOMMENDATIONS AND CONCLUSION ..........................................38
6.1
6.2
RECOMMENDATIONS .......................................................................38
6.1.1
Recommended Alternatives ............................................................38
6.1.2
Recommended Implementation Plan .............................................39
6.1.3
Projected Impact ..............................................................................40
CONCLUSION/ PUBLIC HEALTH IMPLICATIONS .....................41
APPENDIX A: RAW DATA FOR MODEL DEVELOPMENT .................................42
APPENDIX B: SIMULATION MODEL INFORMATION ........................................51
APPENDIX C: FLOOR PLANS ....................................................................................56
APPENDIX D: IMPLEMENTATION PLAN GANTT CHARTS ..............................59
APPENDIX E: SIMULATION MODEL RESULT REPORTS..................................61
BIBLIOGRAPHY ............................................................................................................91
vi
LIST OF TABLES
Table 1. Time Study Data Table. .................................................................................................. 14
Table 2. Current State Process Steps. ........................................................................................... 17
Table 3. Simulation Module Details- Current State...................................................................... 17
Table 4. Alternative 1 Process Steps............................................................................................. 20
Table 5. Simulation Module Details- Alternative 1. ..................................................................... 20
Table 6. Alternative 2 Process Steps............................................................................................. 22
Table 7. Simulation Module Details- Alternative 2. ..................................................................... 22
Table 8. Alternative 3 Process Steps............................................................................................. 24
Table 9. Alternative 4 Process Steps............................................................................................. 25
Table 10. Simulation Module Details- Alternative 4. ................................................................... 26
Table 11. Statistical Test Details. ................................................................................................. 27
Table 12. Efficiency Analysis. ...................................................................................................... 30
Table 13. Patient Centeredness Analysis. ..................................................................................... 32
Table 14. Stakeholder Analysis. ................................................................................................... 35
vii
LIST OF FIGURES
Figure 1. Surgical Patient and Family Experience Flow Diagram ................................................. 6
Figure 2. Time Study Histograms. ................................................................................................ 14
Figure 3. Ease of Implementation Analysis. ................................................................................. 33
viii
PREFACE
It is only fitting that the final essay of my graduate career begins with a thank you to
those in my life who have made the experience possible.
First, I must thank my family. My parents, Louis and Theresa, have never expected
anything more from me than an affirmative answer to the question: “Did you do your best?” In
training me to answer that question honestly, they have trained me to continually work harder
and reach farther; and their example of service to one another, and to everyone in their lives,
inspires me to be a better person. My siblings, Robert and Marie, are never-ending sources of
laughter and support; their trust in my judgment is a constant reminder to trust myself, for which
I am grateful. And, because blood does not define the limit of family, I will be forever thankful
to my best friend, Jillian Cherniawski. She has been a voice of reason and a shoulder to cry on,
without whom I almost certainly would have lost my mind.
I also thank my friends and classmates. I arrived in Pittsburgh alone, but I will depart
knowing that I have a network of support no matter where life might take me. The camaraderie
we share will enhance our successes, because each “win” for one is a “win” for all.
I cannot properly express my gratitude to my mentors at UPMC Shadyside Hospital,
because I know that I have only scratched the surface of my appreciation. As I progress in my
career, I am certain that my understanding of the depth of the wisdom they have shared will only
ix
grow. I am blessed to have such a talented group of people dedicated to my development on a
daily basis.
Finally, thank you to my professors and teachers who have challenged me to think
differently and have taught me about the opportunity that I have to impact the future of
healthcare. A special thank you to my essay committee members who have been patient with me,
as I have worked to juggle my commitments and responsibilities. Additionally, I am honored to
thank Mitchell Usher for his patience and his willingness to share his knowledge of simulation
modeling; this project would not have been possible without him.
x
1.0
INTRODUCTION
The following work is designed to be a case study of a hospital operations process. The
objective of the case study is to understand the current process, identify alternatives, analyze the
alternatives, and provide recommendations with a proposed implementation plan. The case study
will incorporate engineering principles, while maintaining a strong focus on the reality of the
implications of process changes in terms of unique cultural and political traits of the hospital in
question. The demonstration of understanding of internal and external environmental factors and
how to manage change in a hospital is a testament to the effectiveness of a healthcare
administrator. Furthermore, a quality process has the potential to impact an entire population of
patients and has the potential to be adopted by other institutions, thus having a profound impact
of the health of a community, whether the community is local, national, or even global.
1.1
CURRENT HEALTHCARE ENVIRONMENT
Hospitals across the United States are experiencing a new wave of process changes and
quality improvement initiatives as a result of the Affordable Care Act, a well-referenced piece of
legislation that has mandated that hospitals meet certain standards of care. Value-based
purchasing (VBP) has been implemented for government payors (Medicare and Medicaid) and
1
even for some private payors (e.g. Highmark Blue Cross Blue Shield) (Highmark Blue Shield,
n.d.). Value-based purchasing is defined as the system in which reimbursement is tied to
performance in certain quality metrics. The federal VBP program includes both clinical and
patient experience quality measures ("Hospital value-based purchasing," n.d.). Clinical quality
measures include metrics such as the number of patients on the appropriate medications prior to
surgery. Clinical metrics can be driven through electronic health record reminders and solutions,
and provider accountability. Patient experience measures include metrics such as hospital
cleanliness and quietness; therein lays the difference between clinical and patient experience
measures. Patient experience measures are much harder to drive with one solution and cannot be
achieved by holding one hospital staff member accountable; rather, there must be culture of
collective effort to keep the patient at the center of all processes in order for a hospital to be
successful in reaching a high level of patient satisfaction.
In addition to value-based purchasing, the Affordable Care Act is placing an emphasis on
preventative care and driving the American healthcare system to be proactive ("Affordable care
act medicaid.gov," n.d.). Hospitals have historically been the center of the care process and the
focus has been on treating illness. As the healthcare system evolves the primary care physician’s
office is becoming the center of the care process with a focus on maintaining wellness.
Preventative care has the ability to reduce healthcare costs and increase the health status of all
citizens, but if this approach is successful the impact on hospitals will be devastating. Hospitals
obtain revenue only when there are patients who require acute care services and they are able to
pay for such services. Hospitals must work to transition their focus to improving the health of the
community while also maintaining financial health; this is undoubtedly a challenge that will
shape the fate of every hospital in the country as inpatient volumes fall (Shoemaker, 2011).
2
1.2
HOSPITAL ENVIRONMENT
1.2.1 UPMC Shadyside Background
The hospitals of UPMC Health System, headquartered in Pittsburgh, Pennsylvania, are
experiencing the same challenges of an increased demand for value and declining service
volumes. The population of the Western Pennsylvania region is declining overall, and there is an
increased focus on managing patient conditions in a lower-cost outpatient setting. The case study
that will be described in this essay is from the Shadyside Campus of UPMC Presbyterian
Shadyside Hospital, more commonly known as Shadyside Hospital. Shadyside is a tertiary care
facility that is well known in the Pittsburgh community. It is part of an academic medical center,
but it maintains a large population of private practice providers, which creates a unique
environment. Shadyside is also a recognized Magnet hospital, a distinct accomplishment
indicating that the culture at the hospital creates an excellent environment for nurses and others
to work in and it is an excellent place to receive medical care. The hospital has a sizable
population with 520 licensed hospital beds. The larger patient population can be broken down
into smaller subpopulations. One such subpopulation is surgical patients; furthermore, the
population of surgical patients can be categorized into inpatients, outpatients, and ambulatory
outpatients. The inpatient surgical population refers to patients who have been previously
admitted to the hospital and have now opted to undergo surgery. The outpatient surgical
population refers to patients who have opted to undergo surgery after consulting with their
physicians in the outpatient office setting. Outpatients arrive to the hospital on the day of surgery
and they expect to be admitted to the hospital following their surgery. The ambulatory outpatient
population refers to patients who have also chosen to undergo surgery after outpatient office
3
visits, much like the outpatient population. Ambulatory outpatients also arrive at the hospital on
the day of surgery, however these patients do not expect to be admitted to the hospital, rather
they expect to return home the same day. The outpatient surgical patient population is the focus
of this case study, and hereafter, all patients and families referenced in belong to this category.
The Shadyside Hospital facility was built in 1972, but has undergone various renovations
and additions since that time ("History of UPMC Shadyside," n.d.). As a result of building
additions, and the need to work with available space, the physical hospital locations that a patient
and family experience are not in close proximity. Please reference Appendix C to view floor
plans and the physical layout of the hospital space with primary surgical locations. The Zorub
Surgical Family Lounge and the patient arrival and transport process have evolved over time
with changing leadership and changing communication technologies. The current process will be
the subject of discussion and redesign throughout this essay, so it must be explained in detail.
1.2.2 Problem Description
On the morning of surgery, patients arrive at UPMC Shadyside to be prepared for, and
undergo their surgical procedure(s). The patient arrival process begins at varying times,
depending on when the patient’s surgery is scheduled. All patients are asked to arrive at a
designated time, about two and a half hours prior to the scheduled surgery time, on their surgery
day and they are asked to park in the South Aiken Parking Garage. More often than not, patients
bring family members with them, so, after parking, the patient and family travel to the Zorub
Surgical Family Lounge (SFL). Upon arrival in the lounge, the group reports to the desk. At the
desk, one of two greeters welcome the patient and family, share important information, and
provide the patient with a hospital wristband. After the patient and family are checked in they are
4
offered the option of storing belongings in a locker and they are asked to wait for the greeter to
call the patient for transport to the pre-operative nursing unit. One of the greeters then calls the
pre-operative nursing unit (more commonly referred to as Day of Admission Surgery unit, or
DAS) to announce the patient’s arrival and to receive an update about which bed the patient
should be directed to once he or she is transported to the unit. Upon confirmation of the bed
number and vacancy, the patient is called to the front of the lounge by a greeter. Only the patient
is transported at this time; the patient’s family members are asked to wait until the patient is
ready. The greeter verifies the patient’s name and birthdate via his or her wristband and then the
patient is led to DAS. Upon arrival in DAS, the greeter guides the patient to the appropriate bed
and provides instructions for the patient to change into a hospital gown and wait to be greeted by
the attending pre-operative nurse. After the patient is transported to DAS, the greeter returns to
SFL to continue serving patients and families. Once each patient in DAS has changed into a
gown and has been assessed by the nurse, the nurse calls the desk in SFL to ask the greeter to
share this information with the patient’s family, and invite the family to come up and visit with
their loved one. The greeter then pages the family, shares the information and provides the
family with printed instructions on how to get to DAS for their visit. Refer to Figure 1, below,
for a flow diagram of the elements of the patient and family experience. Please note the steps
outlined in red; this specific sub-process is the object of the study.
5
Figure 1. Surgical Patient and Family Experience Flow Diagram
During busy periods of the day, it is common for the greeters to check-in multiple
patients at the desk within a narrow timeframe (e.g. two greeters may check in 11 patients over
the course of ten minutes); this results in a number of patients waiting to be transported. In this
case, the group of patients are called to the front of the lounge, their identities are confirmed as
they congregate, and the group is led to DAS as a unit. The vernacular term used for the
assembly and transport of a group of patients from SFL to DAS is “herding.” The number of
patients in such a group varies from as many as 15 patients to just 2 patients over the course of
the day. Larger groups are usually assembled for the first trips of the day, due to the fact that the
highest number of surgeries scheduled to start at the same time, occur between 7 and 8 am. After
these “herds”, patients are more commonly grouped into pairs, or transported one-on-one.
The objective of this project is to redesign the transport process so each patient feels that
he or she is treated as an individual, rather than one in a “herd” of patients. The new process will
position the organization in a favorable position in terms of patient satisfaction and patient
6
experience measures, without a significant increase in resource costs. Process alternatives are
analyzed using Arena simulation modeling software. The criteria for considering each alternative
include: efficiency, patient centeredness, and ease of implementation.
The impetus for current patient arrival process to be analyzed and redesigned is a
combination of things. First, healthcare payment reform initiatives are placing a greater emphasis
than ever on patient satisfaction. It is in the best interest of the hospital to modify the process to
be a patient-centered as possible, because when the patient and family arrive in the Surgical
Family Lounge it is often their very first interaction with the staff at UPMC Shadyside. The first
impression is critical in setting the tone and expectations for the remainder of the patient’s
experience. Second, new team members have allowed for a fresh-look at the processes. As
previously mentioned, the current process is the result of much experimentation, and it is
understood to be the best option available, by some of the current leaders. However, as
“outsiders” observe the process, the opportunity for improvement is evident.
7
2.0
LITERATURE REVIEW
A clear understanding of the problem and the case study objective allows for research of
methods that may prove useful in solving the problem.
2.1
IMPROVEMENT METHODOLOGIES
2.1.1 Discrete Event Simulation Modeling
Simulation modeling is a powerful methodology that is used by various types of
industries including military, industrial and healthcare. Simulation modeling enables engineers to
understand a system, create a model, and run tests in an environment that simulates reality, but
does not present any risk. There are multiple types of simulation modeling, including Monte
Carlo modeling which is used to analyze situations involving a probability distribution such as
calculating the payment of patients in a clinic (McLaughlin & Hays, 2008). Discrete event
simulation is another type of simulation model that is best used when simulating processes that
involve queuing theory, or the concept of a “waiting line” (McLaughlin & Hays, 2008).
Simulation models allow quick and inexpensive evaluation of “what-if” situations using
computer software. There are seven main steps in developing a simulation model: identify the
problem and objectives, data collection, model development, model validation, production runs
8
and analysis, reporting and implementation (Banks, 2005). The simulation model is developed
using knowledge of the real-world situation. Then, the model is compared to reality to ensure
that it is an accurate representation of the system. And finally many replications of the simulation
process are run and data is collected and analyzed. Arena simulation software allows models to
be built much in the same manner that one would assemble a flow diagram and then, parameters
are entered for each step in the process. When each simulation is complete the software produces
an output report, for the user to analyze. Discrete event simulation modeling is an engineering
tool that is applicable to the surgical patient arrival and transport process because it will enable
the analysis of multiple process alternatives with limited resources.
Simulation modeling has great potential to impact the future of healthcare. In a survey of
simulation modeling experts completed in 2007, they agreed that patient satisfaction, costeffectiveness, and downstream effects of process changes could all be sufficiently addressed
using this methodology (Eldabi, Paul, & Young, 2007).
Simulation modeling has been used to design process flow in other healthcare settings. In
one example simulation modeling was used to design an outpatient clinic and staffing model
(Kaye Parks, Engblom, Hamrock, Satjapot, & Levin, 2011). The clinic flow was assessed, and
diagrammed and the diagram was then input into a simulation software program. Staffing and
scheduling patterns were added to the model, and appropriate simplifications were executed. The
model for the clinic resulted in congestion, especially with the patient check out process.
Appropriate modifications were made to the model in order to reach a more efficient process.
The use of simulation modeling in this case allowed for process changes to be evaluated without
disrupting patient flow or negatively impacting patient satisfaction.
9
Simulation modeling has also been used in the inpatient setting to understand capacity
and bed availability (Ashby, Ferrin, Miller, & Shahi, 2008). In this case, the simulation model
was intended to represent a future hospital in order to guide construction. Since historical or
observational data was not available for the future system, the development team used strategic
planning techniques to translate the existing hospital data into projections for the new hospital.
The team found that the simulation modeling efforts were informative and inpatient flow
processes were designed using this information. Such large-scale simulation models are useful
for understanding the benefits of simulation modeling, but, given the limited scope the Shadyside
Hospital project, the simulation models developed are more concentrated and require the use of
additional methodologies.
2.1.2 Transforming Care at the Bedside Methodology
Transforming Care at the Bedside (TCAB) is a healthcare improvement methodology that
was developed and introduced by the Robert Wood Johnson Foundation and the Institute for
Healthcare Improvement. UPMC Shadyside was selected as a pilot organization to participate in
the development of the quality improvement methodology in 2003 (Institute for Healthcare
Improvement, n.d.). The principles of TCAB are: safe and reliable care, vitality and teamwork,
patient-centered care, and value-added care processes. These principles provide a framework for
quality improvement by guiding frontline staff members, who are the key innovators. TCAB
improvement initiatives do not require a special team of engineers or quality improvement
specialists, rather the idea behind the methodology is that one nurse, or other care provider, can
change the way something is done, on one day, with one patient. This simple change is called a
“test of change”; each test is evaluated in a rapid plan-do-study-act cycle. Another test can then
10
be developed or the process change can move toward implementation on a pilot unit. TCAB
values small tests and frequent process modifications. The TCAB methodology can be combined
with other improvement methodologies to provide a focused, healthcare-appropriate approach.
TCAB is used for improvement work across the hospital. Some examples of TCAB
projects include nurse face-to-face handoffs during shift changes, re-designing the workflow for
skin care nursing, and partnering with families to prevent patient falls. All of these current
projects involve pilot units and the four principles of TCAB. In the case of the surgical family
lounge, this is the first dedicated effort designed to improve the arrival and transport process and
it is the first time that the principles of TCAB are being explicitly applied to this problem. In the
past, efforts have been made to improve the process with small changes and the result has been
the creation of the efficient current process; however the use of TCAB has enhanced the analysis
and development of alternatives.
2.1.3 Other Industrial Engineering Concepts
There are a few other frequently used industrial engineering concepts that help to
enhance the re-design of processes. Process flow mapping is a critical step in visualizing a
process. In this case study, a process flow map is also a useful tool in reaching a consensus about
which parts of the process are problematic, and which parts of the process will be affected by the
implementation of change. A second tool that is useful in this case study is stakeholder analysis.
Stakeholders include all people and groups who have a vested interest in a project. It is valuable
to understand the viewpoint of each of stakeholder to understand where goals are aligned and
where work must be done in order to reach a consensus about project objectives. The last
industrial engineering concept used in this case study is a time study. A time study is a set of
11
timed observations that allows for the understanding of how long each step in a process takes, on
average. A simple time study can be a powerful tool for reconciling perceptions and reality.
12
3.0
DATA AND DESIGN
Discussions with leadership and staff produced four alternatives for redesigning the
patient arrival and transport process. Data collection and study related to the process alternatives
combine simulation modeling and the other industrial engineering concepts previously discussed.
3.1
DATA
The information used to create the following models has been obtained from multiple
sources; there are two datasets that were translated in model parameters. The first dataset used to
build the simulation models is comprised of historical surgical schedule information. The
surgical schedule for the complete 2012 calendar year was analyzed to find an average number
of patient arrivals between the hours of 5:00am and 10:00am. The schedule data is included in
Appendix A.2 for reference purposes.
The second dataset was obtained via a time study. The study collected information about
the sequence and duration of each step in the current state arrival and transport process and the
other activities that the SFL greeters are engaged in. Qualitative information about the current
process was also obtained from interviews with leadership and staff and from observations of
interactions over the course of the time study. Raw time study data is included in Appendix A.1,
however the data used to develop the simulation models is summarized in Figure 2 and Table 1
13
below; please note the specific references to the link between the raw data and the simulation
model module parameters. Figure 2 displays histograms that represent the distribution of the
duration of individual process steps; the two most critical steps in the current state patient arrival
and transport process are included. Table 1 displays the average duration information for some of
the other steps in the process.
Figure 2. Time Study Histograms.
Table 1. Time Study Data Table.
14
3.2
MODEL ASSUMPTIONS
The construction of accurate discrete event simulation model for the current state process,
as well as the alternatives, in this case was dependent on the appropriate simplification of the
process. In an effort to focus on the predictable and critical components of the patient arrival and
transport process, the days and hours that the models represent were restricted. The days were
limited to Monday, Tuesday, Thursday and Friday, to avoid the interference of morning meetings
that delay the OR start time on Wednesdays. The weekends and holidays were also excluded in
order to achieve truly representative arrival averages. Furthermore, the models assume that all
processes run smoothly without any major errors or delays. For example, two key assumptions
are that there is always a bed available for an arriving patient in the morning hours, and that
patients do not become lost during self-transport. The model assumptions are further summarized
for quick reference in Appendix B.1. The assumptions enable the creation of a computer model
that is as similar to reality as possible.
3.3
MODELS
A thorough understanding of the current state and proposed alternatives is critical to
making an educated decision about how to proceed with improvement and redesign efforts. The
following section describes each of the processes in depth. Please reference Appendix B.2 for
images of the actual Arena simulation models.
15
3.3.1 Current State
The current patient arrival and transport process has several strengths. First, the current
process minimizes the amount of time a greeter spends walking back and forth between the
Surgical Family Lounge and the Day of Admission Surgery unit, this minimizes waste in time
and movement, and increases efficiency. Second, the process provides for each patient to be
transported with the guidance of a greeter. The trip between the two locations can be confusing
and it is easy for patients to become lost; by providing a guide, the process minimizes delays due
to lost or confused patients. Third, the process requires minimal resources. The status quo in SFL
requires only two employees to manage the phones, attend to patient and family needs, and
transport patients to DAS.
The current arrival and transport process also has some weaknesses. First, the transport
process does not provide individualized attention to each patient. In the case of a group transport,
or “herd”, a patient that is handicapped may feel rushed by others, and conversely, a patient who
is able-bodied may feel as though their surgical preparation is delayed on account of others.
Additionally, once the group reaches DAS, patients are led to their beds one-by-one by the
greeter, while the remainder of the group must wait in the doorway. Nurses, other caregivers, and
other team members, continue their daily tasks without notice and the patients may feel as
though they are being ignored. The lack of individual attention has the potential to reduce patient
satisfaction and increase patient anxiety. Second, the transport process is delayed until the
greeter can confirm via telephone that a nurse is ready to care for the patient, and that there is a
bed available in DAS. The steps in the current state process are displayed in Table 1 below; note
that the steps outlined in red are the steps that are included in the simulation model.
16
Table 2. Current State Process Steps.
Current State: Greeter guided group transport
Description
1 Patient/family arrives
2
3
Patient/family reports to SFL
Greeter welcomes patient/family, provides instructions
4
Patient/family waits
5
6
7
8
9
10
Greeter calls DAS to announce patient and/or confirm bed number
Greeter calls patient(s) to the front of SFL
Greeter confirms patient(s) name/d.o.b. via wristband
Greeter leads patient(s) to DAS
Greeter leads patient(s) to bed and provides instructions
Greeter returns to SFL
11
12
13
14
15
DAS nurse prepares patient
DAS nurse calls greeter to invite family to DAS
Greeter pages family
Family reports to desk to receive printed instructions on how to walk to DAS
Family members transport themselves to DAS
As previously discussed, the Arena simulation model uses time study data and an average patient
arrival schedule to simulate patient arrival and transport steps from 5:00am to 10:00am. The
current state simulation model modules are further described in Table 3 below.
Table 3. Simulation Module Details- Current State.
Simulation
Module Title
Module
Type
Objective
Parameters
Data Used
Schedule (shown
in Appendix
A.1)
Expression (0.5
+ GAMM(0.563,
3.96) minutes)
Resource: 1
greeter
Historical
surgery schedule
data
Main Model
Patient Arrival
Create
Introduce patients into
the system
Wristbands and
Checkin
Process
Simulate initial
processing
Increment Batch
Size
Assign
Hold for
Batching
Hold
Assign a number to each
patient to aid in model
batching
Hold patients until batch
is formed
17
--Wait for signal
(from submodel
Time study data
via input
analyzer
-----
1)
Signal to third
submodel to
hold greeter
Batch for
elevator
Reset batch size
Signal
Batch
Assign
Signal logical model to
keep the greeter busy for
transport
Simulate “herd”
formation
Unassign numbers and
begin counting again
Using elevator
Process
Simulate transport to
DAS
Unbatch for
bedding
Separate
Unbatch patients
Put patients in
bed
Process
Simulate each patient
being led to a bed
Decide
Prevents model from
allowing the greeter to
leave until all patients are
in a bed
Signal
Signals the model when
the greeter walk from
DAS back to SFL is
complete
Last person in
queue?
Signal to second
submodel to
make greeter
busy for walk
back
Dispose 1
Create Logical
for batching
Decide if
available greeter
Signal to regular
model to send
batch of patients
up
Delay logical for
5 seconds
Create Logical
for transporter
walkback delay
Hold Logical for
walkback delay
Dispose
Create
Decide
Signal
Delay
Create
Hold
Simulates patient exiting
the system
Submodel 1
Creates logical entity
Scans to see if greeter is
busy
If greeter is not busy, and
there are patients the
greeter is able to
transport
Delays until the logical
model checks for busy
greeters again
Submodel 2
Creates logical entity
Holds logical entity until
the signal from the main
18
Send signal (to
submodel 3)
---
Temporary,
patient batch
---
---
---
Expression (0.5
+ 3 * BETA(3,
1.88) minutes)
Resource: 1
transporter
Split existing,
retain original
Expression (-0.5
+4*
BETA(1.48,
2.08) minutes)
Resource: 1
transporter
Time study data
via input
analyzer
---
Time study data
via input
analyzer
---
---
Send signal (to
submodel 2)
---
---
---
Random
---
---
---
Send signal (to
main model)
---
5 seconds
---
Random
---
Wait for signal
(from main
---
Walk back to
desk
Process
Release greeter
in submodel 3
Signal
Create Logical
to take care of
greeter while
transporter is
busy
Create
Hold entity until
batch leaves
Hold
Take control of
greeter
Seize
Wait for signal
for greeter to be
done
Hold
Release greeter
for next batch
Release
model calls for the walk
Simulates a delay while
the greeter is walking
back to DAS from SFL
Signal is sent to main
model to allow the
dispose module to
proceed
Submodel 3
Creates logical entity
Holds logical entity until
the signal from the main
model calls for transport
Greeter is held for the
entirety of transport
Holds logical entity until
the signal from the main
model says transport is
complete
Allows greeter to be free
again
model)
Expression
(TRIA(0.5, 2.17,
2.5) minutes)
Time study data
via input
analyzer
Send signal (to
main model)
---
---
---
Wait to signal
(from main
model)
---
---
---
Wait for signal
(from main
model)
---
---
---
3.3.2 Alternative One
Alternative 1 is titled “patient self-transport without a guide.” Alternative 1 would
eliminate the greeter facilitated transport process, and instead allow patients to guide themselves
up to DAS with the assistance of printed instructions. This alternative would also require a
workflow adjustment for at least one DAS team member, because someone would need to greet
the patient upon his or her arrival to the unit; in my subjective assessment of this alternative, I
believe that it would reduce waiting time, and maximize patient autonomy, since patients would
not need to wait for an escort. It would also increase the likelihood a patient would get lost, and
19
prove difficult for handicapped patients (such patients would need to have a family member or
greeter assist them for safety purposes). The steps in the alternative 1 process are displayed in
Table 2 below; note that the steps outlined in red are the steps that are included in the simulation
model.
Table 4. Alternative 1 Process Steps.
Alternative 1: Patient self-transport without a guide
Description
1 Patient/family arrives
2
3
4
5
6
7
8
9
Patient/family reports to SFL
Greeter welcomes patient/family, provides instructions
Patient/family waits
Greeter calls DAS to announce patient and/or confirm bed number
Greeter provides patient with printed instructions on how to walk to DAS
DAS team member greets patient
DAS team member confirms patient(s) name/d.o.b. via wristband
DAS team member leads patient(s) to bed and provides instructions
10
11
12
13
14
DAS nurse prepares patient
DAS nurse calls greeter to invite family to DAS
Greeter pages family
Family reports to desk to receive printed instructions on how to walk to DAS
Family members transport themselves to DAS
As in the current state model, the Arena simulation model for Alternative 1 uses time study
information to simulate the arrival and transport process; the simulation model modules are
further described in Table 5 below.
Table 5. Simulation Module Details- Alternative 1.
Simulation
Module Title
Module
Type
Objective
Patient Arrival
Create
Introduce patients into
the system
Wristbands and
Checkin
Process
Simulate initial
processing
20
Parameters
Data Used
Schedule (shown
in Appendix
A.1)
Expression (0.5
+ GAMM(0.563,
3.96) minutes)
Historical
surgery schedule
data
Time study data
via input
analyzer
Saying goodbye
to family 1
Delay
Allow time for each
patient to leave loved
ones
Walk to DAS 1
Delay
Allow time for selftransport
Greeted and
bedded in DAS
Process
Simulate each patient
being led to a bed
Dispose 1
Dispose
Simulates patient exiting
the system
Resource: 1
greeter
Expression
(NORM( 1 , 0.5 )
minute
s)
Expression
(TRIA(0.5, 2.17,
2.5) minutes)
Expression (-0.5
+4*
BETA(1.48,
2.08) minutes)
Resource: 1
transporter
Time study data
via input
analyzer
---
---
Educated
estimate based
on observations
Time study data
via input
analyzer
3.3.3 Alternative Two
Alternative 2 is titled “check-in at information desk.” Alternative 2 would change the
location in which the patient received a wristband and instructions. Rather than walking the
entire path to SFL, the patient and family would be greeted just inside the hospital building when
entering from the parking garage. Then, the patient and family would walk to the south wing
elevators. The patient would take the elevator to the second floor, while the family would
continue walking to SFL. The patient would be greeted by a DAS team member and guided to
his or her bed, just as in alternative 1. The rest of the family’s experience would be the same as
the current process. The strengths of this alternative are that it is efficient, and the patient does
not need to wait. The weaknesses of this alternative include: the process reduces the
patient/greeter interaction, and it may increase patient and family anxiety by forcing them to say
goodbye in the hallway rather than the family lounge. Additionally, in the event that a bed was
21
not available in DAS upon the patient’s arrival, this alternative may increase waiting time on the
unit. The steps in the alternative 2 process are displayed in Table 3 below; note that the steps
outlined in red are the steps that are included in the simulation model.
Table 6. Alternative 2 Process Steps.
Alternative 2: Check-in at information desk
Description
1
2
3
4
5
6
7
Patient/family arrives
Patient/family is greeted at East wing entrance desk
Greeter welcomes patient/family, provides instructions
Patient reports to DAS
DAS team member greets patient
DAS team member confirms patient(s) name/d.o.b. via wristband
DAS team member leads patient(s) to bed and provides instructions
8
9
10
11
12
13
14
15
Family reports to SFL
Greeter welcomes family, provides instructions
Family waits
DAS nurse prepares patient
DAS nurse calls greeter to invite family to DAS
Greeter pages family
Family reports to desk to receive printed instructions on how to walk to DAS
Family members transport themselves to DAS
The Arena model for Alternative 2 uses time information inferred from the original time study,
since the path of transport is different from the current state; the alternative 2 simulation model
modules are further described in Table 7 below.
Table 7. Simulation Module Details- Alternative 2.
Simulation
Module Title
Module
Type
Objective
Parameters
Patient Arrival
Create
Introduce patients into
the system
Schedule (shown in
Appendix A.1)
Wristbands and
Checkin
Process
Simulate initial
processing
Expression (0.5 +
GAMM(0.563, 3.96)
22
Data Used
Historical
surgery schedule
data
Time study data
via input
minutes)
Resource: 1 greeter
Walk to Elevator
Delay
Simulate patient and
family walk to elevator
Expression UNIF( 1
, 3) minutes)
Saying goodbye
to family
Delay
Allow time for each
patient to leave loved
ones
Expression (NORM(
1 , 0.5 ) minute
s)
Walk to DAS
Delay
Allow time for selftransport
Expression
(TRIA(1.5, 3.17,
3.5) minutes)
Greeted and
bedded in DAS
Process
Simulate each patient
being led to a bed
Dispose 1
Dispose
Simulates patient exiting
the system
analyzer
Educated
estimate based
on observations
Educated
estimate based
on observations
Time study data
via input
analyzer plus 1
minute for
greater distance
Expression (-0.5 + 4
* BETA(1.48, 2.08)
minutes)
Resource: 1
transporter
Time study data
via input
analyzer
---
---
3.3.4 Alternative Three
Alternative 3 is titled “additional greeter, no grouping.” Alternative 3 would keep all
process steps the same, except there would be an additional greeter to help with transporting
patients. The additional staff member may be a cross-trained employee or a greeter assigned to a
five-hour shift from 5:00am to 10:00am. The strengths of this alternative include increased
patient/greeter interaction and personal attention. Weaknesses include an increased use of
resources, and an increased cost. The steps in the alternative 3 process are displayed in Table 4
below; note that the steps outlined in red are the steps that are included in the simulation model.
23
Table 8. Alternative 3 Process Steps.
Alternative 3: Additional greeter, no grouping
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Description
(Note: Steps are the same as current state except that there would be a 3rd
greeter and there would be no grouping of patients)
Patient/family arrives
Patient/family reports to SFL
Greeter welcomes patient/family, provides instructions
Patient/family waits
Greeter calls DAS to announce patient and/or confirm bed number
Greeter calls patient to the front of SFL
Greeter confirms patient name/d.o.b. via wristband
Greeter leads patient to DAS
Greeter leads patient to bed and provides instructions
Greeter returns to SFL
DAS nurse prepares patient
DAS nurse calls greeter to invite family to DAS
Greeter pages family
Family reports to desk to receive printed instructions on how to walk to DAS
Family members transport themselves to DAS
The Alternative 3 Arena simulation model is nearly identical to the current state model with the
exception of increased resources in terms of the additional staff member. Thus, the module
details are identical to those in Table 3.
3.3.5 Alternative Four
Alternative 4 is titled “nurse guided transport.” Alternative 4 removes the responsibility
of transporting patients from the greeters. Instead, the DAS nurse who will be caring for the
patient will travel to SFL and introduce him/herself to the patient and family. The nurse will
verify the wristband of the patient and then lead the patient to DAS. The strengths of this
alternative include increased individual patient attention and the nurse would have an
24
opportunity to begin conversation with the patient earlier in the patient’s experience; this will
reduce the nurse’s work later in the pre-operative process. Weaknesses of this alternative include
an increased demand for flexibility from the nurse. It would also mean that the nurse would
spend less time on the floor, so in the event the nurse was caring for multiple patients, this may
cause the nurse anxiety. The steps in the alternative 4 process are displayed in Table 5 below;
note that the steps outlined in red are the steps that are included in the simulation model.
Table 9. Alternative 4 Process Steps.
Alternative 4: Nurse guided transport
Description
1 Patient/family arrives
2
3
4
5
6
7
8
9
10
Patient/family reports to SFL
Greeter welcomes patient/family, provides instructions
Patient/family waits
Greeter calls DAS to announce patient and/or confirm bed number
DAS nurse travels to SFL
DAS Nurse finds patient/family and introduces him/herself
DAS Nurse confirms patient(s) name/d.o.b. via wristband
DAS Nurse leads patient(s) to DAS
DAS Nurse leads patient(s) to bed and provides instructions
11
12
13
14
15
DAS nurse prepares patient
DAS nurse calls greeter to invite family to DAS
Greeter pages family
Family reports to desk to receive printed instructions on how to walk to DAS
Family members transport themselves to DAS
The Arena simulation model for Alternative 4 is built on the assumption that nurses are able to
pick up their patients within five minutes of arrival in addition to time study data for the duration
of registration and transport. The alternative 4 simulation model modules are further described in
Table 10 below.
25
Table 10. Simulation Module Details- Alternative 4.
Simulation
Module Title
Patient Arrives
Wristbands and
Checkin
Nurse finishes
tasks at hand and
travels to SFL
Module
Type
Create
Process
Objective
Introduce patients into
the system
Simulate initial
processing
Delay
Allow time for each
nurse to interrupt work
and travel to SFL
Nurse Arrives to
meet patient and
family
Delay
Simulate nurse meeting
patient and family
Nurse and
Patient walk to
DAS
Delay
Allow time for transport
Patient is led to
bed
Delay
Simulate each patient
being led to a bed
End
Dispose
Parameters
Data Used
Schedule (shown
in Appendix A.1)
Historical
surgery schedule
data
Expression (0.5 +
GAMM(0.563,
3.96) minutes)
Resource: 1
greeter
Expression (UNIF(
2 , 5) minutes)
Estimate based
on observations
Expression
(NORM( 1 , 0.5 )
minute
s)
Expression (0.5 +
3 * BETA(3, 1.88)
minutes)
Expression (-0.5 +
4 * BETA(1.48,
2.08) minutes)
Time study data
via input
analyzer
Time study data
via input
analyzer
---
---
Simulates patient exiting
the system
3.4
Time study data
via input
analyzer
Educated
estimate based
on observations
MODEL VALIDATION
The current state model is the model that is intended to replicate the real-world scenario
that exists now. The current state model must be compared to the reality in order to be deemed
valid. The time study data allows the comparison to be completed relatively easily. According to
the time study, a patient spends an average of nine minutes total in the system; this average was
calculated using patient-identified information for the entire set of process steps. According to
the simulation model, the patient spends an average of 13.5 minutes in the system. Validation of
26
the model can be completed using a statistical t-test to test the hypothesis that the model mean
and the data mean are equal. The t-test rejected the hypothesis that the means are equal, which
means that the model is not an adequate representation of the real system. Table 11 below
summarizes the statistical test. However, the real system was simplified for the purposes of
creating the model; the assumptions that allowed for the construction of the model have modified
the original system in such a way that the simulated system is not equivalent to the real-life
system. The most important point to understand about the model validation is that the lack of
statistical significance is not an issue that should prevent the model from being used as part of a
decision making process, especially when combined with the “Transforming Care at the
Bedside” methodology.
Table 11. Statistical Test Details.
Parameter Information
Sample Size
25
Sample Mean
13.5
Population Mean
9
Variance
10.7
Std. Deviation
3.3
One Sample, Two-Sided T-Test
Degrees of Freedom
24
T-Score
6.81
Critical T-Value
2.064
Null hypothesis rejected
Result
(p<0.0005)
Translation
Model is not adequate
The alternative state models cannot be validated time study data, however many modules
from the current state model have been used in the alternative state models. For example, all four
of the alternative models use the “Checkin and Wristbanding” module with the exact same
parameters as in the current state model. So, even though process steps may have been added or
changed, some of the basic current state model construction was included in the alternatives. The
27
alternative models are theoretical representations of proposed process redesign options. Given
the fact that any alternative model will be tested and implemented using the TCAB methodology,
I am comfortable using the models to provide basic, objective information about the relative
efficiency of each alternative.
28
4.0
FINDINGS
The discrete event simulation models allow for both objective analysis of the current state
process and the alternatives in question. Objective, quantitative analysis is important for
understanding how process changes will affect the downstream aspects of the system. It is
imperative that patients arrive in DAS in a timely manner, because the failure to complete preoperative preparation on time can result in OR delays. Subjective, quantitative analysis is also
important because the process must be agreeable to patients and staff. A process that does meet
all of these criteria will not succeed, so the qualitative analyses have been completed in addition
to the simulation models. As a result, the current state and each of the alternatives have been
analyzed in terms of efficiency, patient-centeredness, and ease of implementation.
4.1
EFFICIENCY ANALYSIS
As previously discussed, it is vital that the process in the surgical family lounge be
efficient, so as to avoid creating any operating room delays. Efficiency is defined as the ratio of
the effective or useful output to the total input in any system ("Efficiency," n.d.). In the case of
the surgical family lounge arrival and transport process, inputs include staff resources and time,
and effective output is a timely check-in and transport of the patient. Efficiency can be impacted
by increased waiting times, or delays. The current state and each alternative have both strengths
29
and weaknesses in terms of efficiency. Efficiency metrics also include: the total number of
patients that are processed in the given time period (throughput), the average time each patient
spends in the process, and the financial impact of resource utilization. Table 6 below displays the
elements of efficiency for comparison purposes; these elements have been identified through
general analysis as well as the simulation models. The Arena simulation result reports have been
included in Appendix E for reference purposes
Table 12. Efficiency Analysis.
The cost differential is rooted in staffing cost increases; the details of the cost differential
calculation can be found in Appendix B.3. Based on the analysis, the alternative that is most
efficient in terms of average total time per patient is Alternative 1.
30
4.2
IMPACT ON PATIENT SATISFACTION
The importance of patient satisfaction has been a key factor in the development of the
improvement project in the Surgical Family Lounge. A key part of the mission is to provide
patient-centered care, so it is not enough to have an efficient process. The process must also
provide patients with the sense that they are valued, and minimize any anxiety they may be
feeling. Table 7 shows the strengths and weaknesses of the current state and each alternative in
terms of patient satisfaction. The analysis is not purely objective, however, it is clear that
Alternative 4 maximizes caregiver to patient interaction.
31
Table 13. Patient Centeredness Analysis.
4.3
EASE OF IMPLEMENTATION ANALYSIS
Efficiency and patient satisfaction will be key to the success of any alternative; however,
it is important to consider the ease of implementation for each alternative. In some cases, process
redesign is not always possible within the existing culture, so culture change will be required for
sustainable improvement to occur. The analysis in Figure 2 provides a ranking for the ease of
implementation for each alternative.
32
Figure 3. Ease of Implementation Analysis.
The analysis accounts for cultural aspects at UPMC Shadyside and the extent of process change
that would be required. A detailed explanation for each ranking is included in Appendix B.3.
33
5.0
5.1
DISCUSSION
MODEL LIMITATIONS
As previously discussed, the development of the simulation models requires the use of
key assumptions. The patient arrival and transport process depends on the processes immediately
before and after, and there is some variability by time of day, day of the week, etc. While the
assumptions allow for the models to simulate the usual scenario, unusual circumstances are not
accounted for. For example, in a situation where the patient is late, a bed is not available in DAS,
a patient’s surgery is delayed, etc. the model will not be representative of reality.
5.2
BARRIERS TO SUCCESS
The UPMC Shadyside case study is more complex than a simple process redesign of the
patient arrival and transport process. The simulation models and the quantitative analyses do not
fully account for cultural factors at work in the current environment. The qualitative analyses
touch on some of the circumstances that act as barriers to change.
One of the key factors that impacts success in any process improvement project is the
level of investment of each person or group associated with the project. A stakeholder analysis is
a useful tool for understanding the position of each of thee people or groups in question. Table 8
34
displays the stakeholders for the project along with a ranking for commitment and ability to
change. The stakeholders are ranked on a scale from -5 to 5, with -5 being extremely low and 5
being the greatest level of commitment/ability.
Table 14. Stakeholder Analysis.
Commitment
to Objective
Ability to
Accept
Change
Ability to
Overcome
Barriers
Ability to
Drive
Change
Total
VP, Operations
5
5
5
5
20
Clinical Director
1
2
3
2
8
Unit Director
-3
-1
-2
2
-4
Unit Supervisor
-5
-4
-5
0
-14
Nurses
-2
1
1
3
3
Greeters
1
-3
-3
2
-3
QI Team
5
5
3
3
16
There is a high degree of variability in the total scores as displayed in the right-most
column of Table 8; this indicates that stakeholder goals are not aligned. Past process
improvement efforts at UPMC Shadyside have been successful largely due to the engagement of
the unit director, the frontline staff and the QI team (D'Antonio, 2012). Thus, the lack of
investment in the project of a few key stakeholders is a barrier to the success of the process
improvement project.
35
Another barrier to success is the physical space in which the patient arrival and transport
process exists. The physical layout of the building creates difficulties for patient flow and
efficiency. The lack of a direct route from the Surgical Family Lounge to DAS limits the number
of process alternatives.
While these two barriers can be discouraging, by no means do they create a firm
roadblock to success. In the case of poor stakeholder alignment, there are strategies that can be
used to gain stakeholder buy-in. Most importantly, the stakeholders must have a say in the
process changes, and they must recognize exactly what they have at stake. The development and
implementation plans must take these into consideration. In the case of difficult physical space,
this barrier simply requires those involved in the re-design of the process to think more broadly.
It is important to recognize that it is possible that no solution will be “perfect”, and focus on
evaluating each alternative in a balanced way.
5.3
DRIVERS OF SUCCESS
Despite the existence of some barriers, there are situational factors that will support
success of the process change. The first driver of success is strong leadership support. As noted
in Table 7, executive level leadership is invested in the development and implementation of a
new process for patient arrival and transport. A second driver for success is the idea that
reimbursement is indirectly tied to the success of the process change. The current healthcare
reform environment, in conjunction with the cost-saving initiatives at UPMC Shadyside,
reinforces the need to provide increased patient satisfaction and positive patient outcomes. The
36
final driver of success in this case study is the Magnet culture at UPMC Shadyside, and staff
familiarity with process change. Nurses in surgical areas generally feel as though they are
removed from inpatient nursing and function as a separate entity in some respects, but they are
still members of a Magnet workforce. The visibility of other nursing-related quality
improvement projects has a positive influence on the nurses in all units of the hospital. The three
success factors must be leveraged in order to achieve a sustainable process change. If the
implementation plan, combines leadership support and cost-saving motivations, and is built on a
foundation of Magnet culture and patient-centeredness the likelihood of success is high.
37
6.0
RECOMMENDATIONS AND CONCLUSION
The UPMC Shadyside case study is a complex problem that must be treated and
evaluated as such. The use of simulation modeling is an appropriate use of an engineering tool.
But, in light of the existence of barriers to success, the recommendations and implementation
plan must be tailored to the situation.
6.1
RECOMMENDATIONS
6.1.1 Recommended Alternatives
The efficiency, patient centeredness, and ease of implementation analyses of each
alternative provide valuable information, and when this information is combined with situational
knowledge appropriate recommendations can be made. Based on the efficiency analysis,
Alternative 1 is the best option in terms of total time per patient and cost differential. Based on
the patient centeredness analysis, Alternative 4 maximizes caregiver to patient interaction. And
based on the ease of implementation analysis Alternative 1 ranks as easiest and Alternative 4
ranks as most difficult. Alternatives 2 and 3 are eliminated from consideration because
Alternative 2 has a high potential for a decline in patient satisfaction, and Alternative 3 would
add an unlikely additional resource cost in a time of cost-savings initiatives. Thus, the best
38
recommendation for the unique environment is to complete a TCAB “test of change” for both
Alternatives 1 and 4.
A “test of change” or a pilot will address all of the situational needs. The barriers to
success require stakeholder alignment and broad, objective thinking; by running tests,
stakeholders will have the opportunity to work together toward a common goal which is an
objective evaluation of the alternatives in reality as opposed to just in simulation models.
Additionally, the “test of change” approach will leverage the success factors appropriately. Staff
members will be actively modifying and evaluating the process alternatives as a group, which
supports collaboration, the essence of Magnet culture. Staff will see the impact on patient
satisfaction in real time which will allow them to recognize the importance of keeping patients at
the center of the process, and inspire them to understand their role in driving hospital
reimbursement. And finally, leadership will have the opportunity to hear the report following the
“tests of change”, reaffirm the importance of the process changes, and provide positive
encouragement.
6.1.2 Recommended Implementation Plan
The recommendation implementation plan begins with meetings to plan the “tests of
change”; a unit leadership meeting will be the forum for discussing the high level overview of
the process background and process alternatives. The next step is a frontline staff member
committee meeting where the alternatives are discussed, further planning is completed, and all
frontline stakeholder groups are able to express their opinions freely. The tests should then be
conducted, and a de-briefing meeting should be scheduled to evaluate the alternatives.
39
Following the “test of change” phase, the selected alternative should be implemented.
The implementation phases will differ depending on which alternative is chosen, but both plans
will include additional leadership meetings and committee meetings. De-briefing meetings will
also be required to monitor the process change through the critical time when staff members are
forming new habits that will determine the sustainability of the new process. Appendix D
includes Gantt charts that provide a visual representation of the proposed implementation plan.
6.1.3 Projected Impact
The projected impact of the new process will include stronger patient satisfaction scores,
increased awareness of patient feelings and needs during the arrival and transport processes, and
increased staff vitality. Patient satisfaction is measured using the Press Ganey survey tool. Press
Ganey is a recognized leader in the industry, “working with more than 10,000 health care
organizations nationwide, including 50% of all U.S. hospitals, to improve clinical and business
outcomes” ("About the Press Ganey Survey," n.d.). It is not possible to explicitly identify the
patient satisfaction surveys of those patients who experienced the surgical family lounge,
because the Press Ganey survey does not separate surgical patients who are admitted to the
hospital from the general inpatient population. It is not possible to identify a specific satisfaction
metric that would be an appropriate indicator for patient satisfaction with the lounge; however, it
is true that the surgical family lounge is the first hospital location that is experienced by many
patients, so we look to see an overall increase in patient satisfaction scores due to an improved
first impression after the process changes. Additionally, as staff members take the time to care
for the whole patient and recognize that the patient is the center of the arrival and transport
40
process, rather than being focused on efficiency, staff members will feel more comfortable
interacting with patients and their vitality will be increased.
6.2
CONCLUSION/ PUBLIC HEALTH IMPLICATIONS
The UPMC Shadyside surgical patient and family experience case study has the potential
to have an impact on public health by influencing the quality improvement work at other
hospitals. Simulation modeling is a powerful tool that can be used in the effort to shift the United
States healthcare system from a volume-based to a value-based system. Transforming Care at the
Bedside is a methodology that has the potential to increase health literacy and improve patient
compliance, because healthcare providers and patients that feel as though patient care is truly the
center focus are more engaged. The combination of simulation modeling and TCAB is
translatable to other hospitals, and other care settings to simultaneously improve efficiency and
patient outcomes.
41
APPENDIX A: RAW DATA FOR MODEL DEVELOPMENT
A.1
TIME STUDY
The data obtained in the time study were collected by over the course of several days. All
data was collected via observation and manual documentation. The information displayed in the
table below is an excerpt of the complete set of information (only one day of observations is
included) for reference purposes.
Line
Number
Patient/
Family
Identifie
r
13-Dec
1
13001
13-Dec
2
13001
13-Dec
13-Dec
Date
Actor
Activity Type
Time
Start
Time
Stop
Duratio
n
5:00 AM
5:00 AM
greeter 1
Arrival
Initial
Processing
5:00 AM
5:03 AM
03:00.0
3
greeter 1
Incoming call
5:03 AM
5:03 AM
00:00.0
4
greeter 2
Herding
5:04 AM
5:04 AM
00:00.0
5
greeter 2
Total Transport
5:04 AM
5:10 AM
06:00.0
5:06 AM
5:06 AM
00:00.0
greeter 1
Arrival
Initial
Processing
5:06 AM
5:08 AM
02:00.0
5:07 AM
5:07 AM
00:00.0
greeter 1
Arrival
Initial
Processing
13-Dec
6
13002
13-Dec
7
13002
13-Dec
8
13003
13-Dec
9
13003
5:08 AM
5:09 AM
01:00.0
13-Dec
10
13004
Arrival
5:09 AM
5:09 AM
00:00.0
13-Dec
11
13005
Arrival
5:10 AM
5:10 AM
00:00.0
13-Dec
12
13005
Intial Processing
5:11 AM
5:15 AM
04:00.0
13-Dec
13
13006
Arrival
5:11 AM
5:11 AM
00:00.0
13-Dec
14
13004
Intial Processing
5:09 AM
5:12 AM
03:00.0
greeter 2
greeter 1
42
No. in
"Herd"
2
Comments
13-Dec
15
greeter 1
Intial Processing
5:12 AM
5:15 AM
03:00.0
13-Dec
16
13006
greeter 1
Incoming call
5:14 AM
5:14 AM
00:00.0
13-Dec
17
greeter 1
Herding
5:16 AM
5:17 AM
01:00.0
13-Dec
18
greeter 1
Total Transport
5:17 AM
5:24 AM
07:00.0
13-Dec
19
greeter 2
Assisting family
5:18 AM
5:19 AM
01:00.0
locker
13-Dec
20
greeter 2
Other
5:19 AM
5:20 AM
01:00.0
clerical
13-Dec
21
greeter 2
Assisting family
5:20 AM
5:21 AM
01:00.0
locker
13-Dec
22
greeter 2
Other
5:21 AM
5:21 AM
00:00.0
clerical
13-Dec
23
13007
Arrival
5:21 AM
5:21 AM
00:00.0
13-Dec
24
13007
Intial Processing
5:21 AM
5:24 AM
03:00.0
13-Dec
25
13008
Arrival
5:21 AM
5:21 AM
00:00.0
13-Dec
26
13009
Arrival
5:22 AM
5:22 AM
00:00.0
13-Dec
27
13010
Arrival
5:22 AM
5:22 AM
00:00.0
13-Dec
28
13008
greeter 1
Intial Processing
5:24 AM
5:26 AM
02:00.0
13-Dec
29
13009
greeter 2
Intial Processing
5:25 AM
5:27 AM
02:00.0
13-Dec
30
13011
Arrival
5:23 AM
5:23 AM
00:00.0
13-Dec
31
13010
greeter 1
Intial Processing
5:26 AM
5:28 AM
02:00.0
13-Dec
32
13011
greeter 2
Intial Processing
5:27 AM
5:30 AM
03:00.0
13-Dec
33
13012
Arrival
5:23 AM
5:23 AM
00:00.0
13-Dec
34
13012
greeter 1
Intial Processing
5:28 AM
5:29 AM
01:00.0
13-Dec
35
greeter 2
Incoming call
5:28 AM
5:28 AM
00:00.0
13-Dec
36
13013
13-Dec
37
13013
13-Dec
greeter 2
put on hold
?
Arrival
5:29 AM
5:29 AM
00:00.0
greeter 1
Intial Processing
5:29 AM
5:31 AM
02:00.0
38
greeter 2
Assisting family
5:31 AM
5:32 AM
01:00.0
13-Dec
39
greeter 1
Outgoing call
5:31 AM
5:31 AM
00:00.0
13-Dec
40
greeter 2
Herding
5:32 AM
5:34 AM
02:00.0
13-Dec
41
greeter 2
Total Transport
5:34 AM
5:41 AM
07:00.0
13-Dec
42
greeter 1
Assisting family
5:33 AM
5:33 AM
00:00.0
13-Dec
43
greeter 1
Incoming call
5:33 AM
5:34 AM
01:00.0
13-Dec
44
greeter 1
Assisting family
5:35 AM
5:36 AM
01:00.0
13-Dec
45
greeter 1
Other
5:36 AM
5:43 AM
07:00.0
clerical
13-Dec
46
greeter 2
Other
5:41 AM
5:46 AM
05:00.0
coffee
13-Dec
47
13014
13-Dec
48
13014
13-Dec
Arrival
5:43 AM
5:43 AM
00:00.0
greeter 1
Intial Processing
5:43 AM
5:45 AM
02:00.0
49
greeter 1
Outgoing call
5:45 AM
5:46 AM
01:00.0
13-Dec
50
greeter 1
Total Transport
5:46 AM
5:50 AM
04:00.0
13-Dec
51
greeter 2
Other
5:46 AM
5:49 AM
03:00.0
13-Dec
52
greeter 2
Incoming call
5:49 AM
5:49 AM
00:00.0
13-Dec
53
greeter 2
Outgoing call
5:49 AM
5:50 AM
01:00.0
13-Dec
54
greeter 2
Assisting family
5:50 AM
5:50 AM
00:00.0
13-Dec
55
greeter 2
Other
5:50 AM
5:54 AM
04:00.0
13-Dec
56
13015
Arrival
5:52 AM
5:52 AM
00:00.0
13-Dec
57
13015
greeter 1
Intial Processing
5:52 AM
5:54 AM
02:00.0
13-Dec
58
greeter 2
Total Transport
5:54 AM
6:01 AM
07:00.0
43
? ( a lot)
locker
1
clerical
getting wheelchairs/ clerical
1
13-Dec
59
13016
13-Dec
60
13016
Arrival
5:57 AM
5:57 AM
00:00.0
Intial Processing
5:57 AM
5:59 AM
02:00.0
13-Dec
61
13017
Arrival
5:57 AM
5:57 AM
00:00.0
13-Dec
62
13017
13-Dec
63
13018
Intial Processing
6:00 AM
6:01 AM
01:00.0
Arrival
5:58 AM
5:58 AM
00:00.0
13-Dec
64
13018
13-Dec
65
greeter 1
Intial Processing
6:01 AM
6:03 AM
02:00.0
greeter 1
Incoming call
5:58 AM
5:58 AM
00:00.0
13-Dec
66
greeter 1
Incoming call
5:59 AM
5:59 AM
00:00.0
13-Dec
67
greeter 2
Incoming call
6:01 AM
6:01 AM
00:00.0
13-Dec
68
greeter 2
Herding
6:04 AM
6:07 AM
03:00.0
13-Dec
69
greeter 2
Total Transport
6:07 AM
6:14 AM
07:00.0
13-Dec
70
greeter 1
Outgoing call
6:04 AM
6:04 AM
00:00.0
13-Dec
71
greeter 1
Incoming call
6:05 AM
6:05 AM
00:00.0
13-Dec
72
greeter 1
Assisting family
6:05 AM
6:06 AM
01:00.0
13-Dec
73
greeter 1
Incoming call
6:09 AM
6:09 AM
00:00.0
13-Dec
74
greeter 1
Other
6:06 AM
6:09 AM
03:00.0
13-Dec
75
greeter 1
Outgoing call
6:09 AM
6:10 AM
01:00.0
13-Dec
76
Arrival
6:12 AM
6:12 AM
00:00.0
13-Dec
77
greeter 1
Incoming call
6:13 AM
6:13 AM
00:00.0
13-Dec
78
greeter 1
Incoming call
6:14 AM
6:14 AM
00:00.0
13-Dec
79
greeter 1
Intial Processing
6:12 AM
6:14 AM
02:00.0
13-Dec
80
greeter 1
Incoming call
6:15 AM
6:15 AM
00:00.0
13-Dec
81
greeter 1
Incoming call
6:16 AM
6:16 AM
00:00.0
13-Dec
82
greeter 1
Outgoing call
6:16 AM
6:16 AM
00:00.0
13-Dec
83
greeter 2
Incoming call
6:18 AM
6:18 AM
00:00.0
13-Dec
84
greeter 2
Incoming call
6:19 AM
6:19 AM
00:00.0
13-Dec
85
greeter 2
Outgoing call
6:20 AM
6:20 AM
00:00.0
13-Dec
86
greeter 1
Incoming call
6:20 AM
6:20 AM
00:00.0
13-Dec
87
greeter 2
Assisting family
6:21 AM
6:21 AM
00:00.0
13-Dec
88
greeter 1
Other
6:22 AM
6:22 AM
00:00.0
13-Dec
89
13020
Arrival
6:25 AM
6:25 AM
00:00.0
13-Dec
90
13020
greeter 1
Intial Processing
6:25 AM
6:27 AM
02:00.0
13-Dec
91
greeter 1
Outgoing call
6:27 AM
6:27 AM
00:00.0
13-Dec
92
greeter 1
Total Transport
6:28 AM
6:31 AM
03:00.0
13-Dec
93
greeter 2
Incoming call
6:28 AM
6:28 AM
00:00.0
13-Dec
94
greeter 2
Outgoing call
6:29 AM
6:29 AM
00:00.0
13-Dec
95
greeter 2
Incoming call
6:30 AM
6:30 AM
00:00.0
13-Dec
96
greeter 2
Outgoing call
6:31 AM
6:31 AM
00:00.0
13-Dec
97
greeter 2
Incoming call
6:31 AM
6:32 AM
01:00.0
13-Dec
98
greeter 2
Assisting family
6:32 AM
6:32 AM
00:00.0
Arrival
6:33 AM
6:33 AM
00:00.0
Intial Processing
6:33 AM
6:36 AM
03:00.0
Arrival
6:39 AM
6:39 AM
00:00.0
Intial Processing
6:39 AM
6:42 AM
03:00.0
greeter 1
greeter 1
13019
13019
13020
13-Dec
99
13021
13-Dec
100
13021
13-Dec
101
13022
13-Dec
102
13022
greeter 2
greeter 1
44
directions/ locker
clerical
clerical
13-Dec
103
13-Dec
104
13023
greeter 1
13-Dec
105
13023
13-Dec
106
13024
13-Dec
107
13024
13-Dec
13-Dec
greeter 2
Incoming call
6:40 AM
6:40 AM
00:00.0
Arrival
6:40 AM
6:40 AM
00:00.0
Intial Processing
6:41 AM
6:45 AM
04:00.0
Arrival
6:42 AM
6:42 AM
00:00.0
greeter 1
Intial Processing
6:43 AM
6:45 AM
02:00.0
108
greeter 1
Assisting family
6:43 AM
6:43 AM
00:00.0
109
greeter 2
Incoming call
6:45 AM
6:45 AM
00:00.0
13-Dec
110
greeter 1
Incoming call
6:45 AM
6:45 AM
00:00.0
13-Dec
111
13023
greeter 2
Total Transport
6:46 AM
6:55 AM
09:00.0
13-Dec
112
13022
greeter 1
Total Transport
6:56 AM
7:02 AM
06:00.0
13-Dec
113
greeter 1
Outgoing call
6:49 AM
6:49 AM
00:00.0
13-Dec
114
greeter 1
Incoming call
6:50 AM
6:50 AM
00:00.0
13-Dec
115
greeter 1
Assisting family
6:51 AM
6:51 AM
00:00.0
13-Dec
116
13025
13-Dec
117
13025
13-Dec
118
13-Dec
119
13026
13-Dec
120
13026
13-Dec
Arrival
6:51 AM
6:51 AM
00:00.0
greeter 1
Intial Processing
6:51 AM
6:53 AM
02:00.0
greeter 1
Outgoing call
6:54 AM
6:54 AM
00:00.0
Arrival
6:56 AM
6:56 AM
00:00.0
greeter 2
Intial Processing
6:56 AM
6:59 AM
03:00.0
121
greeter 2
Outgoing call
7:00 AM
7:00 AM
00:00.0
13-Dec
122
greeter 2
Incoming call
7:00 AM
7:00 AM
00:00.0
13-Dec
123
greeter 1
Incoming call
7:04 AM
7:04 AM
00:00.0
13-Dec
124
greeter 1
Incoming call
7:10 AM
7:10 AM
00:00.0
13-Dec
125
greeter 1
Outgoing call
7:10 AM
7:10 AM
00:00.0
13-Dec
126
greeter 1
Assisting family
7:10 AM
7:10 AM
00:00.0
13-Dec
127
greeter 1
Outgoing call
7:11 AM
7:11 AM
00:00.0
13-Dec
128
greeter 1
Assisting family
7:13 AM
7:13 AM
00:00.0
13-Dec
129
greeter 2
Assisting family
7:16 AM
7:16 AM
00:00.0
locker
13-Dec
130
greeter 2
Other
7:26 AM
7:31 AM
05:00.0
coffee
13-Dec
131
greeter 1
Incoming call
7:29 AM
7:29 AM
00:00.0
13-Dec
132
13027
Arrival
7:00 AM
7:00 AM
00:00.0
13-Dec
133
13027
greeter 2
Intial Processing
7:01 AM
7:05 AM
04:00.0
13-Dec
134
13027
greeter 2
Total Transport
7:05 AM
7:11 AM
06:00.0
13-Dec
135
13028
Arrival
7:04 AM
7:04 AM
00:00.0
13-Dec
136
13028
Intial Processing
7:06 AM
7:09 AM
03:00.0
13-Dec
137
13029
Arrival
7:17 AM
7:17 AM
00:00.0
13-Dec
138
13029
Intial Processing
7:17 AM
7:22 AM
05:00.0
13-Dec
139
13024
Other
7:29 AM
7:29 AM
00:00.0
13-Dec
140
13030
13-Dec
141
13030
13-Dec
142
13-Dec
143
13-Dec
144
13031
13-Dec
145
13-Dec
146
greeter 1
greeter 1
Arrival
7:22 AM
7:22 AM
00:00.0
greeter 1
Intial Processing
7:22 AM
7:24 AM
02:00.0
greeter 1
Herding
7:32 AM
7:32 AM
00:00.0
greeter 1
Total Transport
7:34 AM
7:39 AM
05:00.0
7:45 AM
7:45 AM
00:00.0
13031
greeter 1
Arrival
Initial
Processing
7:45 AM
7:48 AM
03:00.0
13031
greeter 2
Total Transport
7:49 AM
7:58 AM
09:00.0
45
locker
Transport arrived to pick up
patient
2
13029, 13028??
13-Dec
147
13-Dec
148
13032
greeter 1
Outgoing call
7:48 AM
7:48 AM
00:00.0
Arrival
7:50 AM
7:50 AM
00:00.0
13-Dec
149
13032
greeter 1
Intial Processing
7:51 AM
7:53 AM
02:00.0
13-Dec
150
13032
13-Dec
151
13033
greeter 2
Total Transport
8:00 AM
8:08 AM
08:00.0
Arrival
7:54 AM
7:54 AM
00:00.0
13-Dec
152
13033
13-Dec
153
13037
Intial Processing
7:54 AM
7:58 AM
04:00.0
Arrival
8:10 AM
8:10 AM
00:00.0
13-Dec
154
13037
greeter 1
Intial Processing
8:10 AM
8:14 AM
04:00.0
13-Dec
155
13037
13-Dec
156
13034
greeter 1
Total Transport
8:23 AM
8:30 AM
07:00.0
Arrival
8:10 AM
8:10 AM
00:00.0
13-Dec
157
13034
13-Dec
158
13034
greeter 2
Intial Processing
8:11 AM
8:15 AM
04:00.0
greeter 2
Total Transport
8:15 AM
8:21 AM
06:00.0
13-Dec
159
13035
Arrival
8:45 AM
8:45 AM
00:00.0
13-Dec
160
13035
13-Dec
161
greeter 1
Intial Processing
8:46 AM
8:49 AM
03:00.0
greeter 1
Herding
8:54 AM
8:54 AM
00:00.0
13-Dec
162
greeter 1
Total Transport
8:55 AM
9:01 AM
06:00.0
13-Dec
163
13036
13-Dec
164
13036
Arrival
8:55 AM
8:55 AM
00:00.0
Intial Processing
8:55 AM
9:01 AM
06:00.0
13-Dec
165
Other
8:15 AM
8:15 AM
00:00.0
13-Dec
166
13-Dec
167
greeter 1
Outgoing call
8:01 AM
8:01 AM
00:00.0
greeter 1
Assisting family
8:04 AM
8:06 AM
02:00.0
13-Dec
13-Dec
168
greeter 1
Assisting family
8:06 AM
8:08 AM
02:00.0
169
greeter 2
Incoming call
7:36 AM
7:36 AM
00:00.0
13-Dec
170
greeter 2
Assisting family
7:38 AM
7:38 AM
00:00.0
13-Dec
171
greeter 2
Assisting family
7:39 AM
7:39 AM
00:00.0
13-Dec
172
greeter 2
Incoming call
7:43 AM
7:43 AM
00:00.0
13-Dec
173
greeter 2
Incoming call
7:47 AM
7:47 AM
00:00.0
13-Dec
174
greeter 1
Incoming call
7:50 AM
7:50 AM
00:00.0
13-Dec
175
greeter 1
Outgoing call
7:50 AM
7:50 AM
00:00.0
13-Dec
176
greeter 1
Incoming call
7:52 AM
7:52 AM
00:00.0
13-Dec
177
greeter 1
Outgoing call
7:54 AM
7:54 AM
00:00.0
13-Dec
178
greeter 1
Assisting family
7:54 AM
7:54 AM
00:00.0
13-Dec
179
greeter 1
Outgoing call
7:58 AM
7:58 AM
00:00.0
13-Dec
180
greeter 1
Outgoing call
8:14 AM
8:14 AM
00:00.0
13-Dec
181
greeter 1
Incoming call
8:20 AM
8:20 AM
00:00.0
13-Dec
182
greeter 1
Incoming call
8:20 AM
8:20 AM
00:00.0
13-Dec
183
greeter 1
Outgoing call
8:20 AM
8:20 AM
00:00.0
13-Dec
184
greeter 1
Assisting family
8:20 AM
8:20 AM
00:00.0
13-Dec
185
greeter 2
Incoming call
8:25 AM
8:25 AM
00:00.0
13-Dec
186
greeter 2
Assisting family
8:28 AM
8:28 AM
00:00.0
13-Dec
187
greeter 2
Assisting family
8:29 AM
8:29 AM
00:00.0
13-Dec
188
greeter 2
Incoming call
8:33 AM
8:33 AM
00:00.0
13-Dec
189
greeter 2
Assisting family
8:33 AM
8:33 AM
00:00.0
13-Dec
190
greeter 2
Incoming call
8:35 AM
8:35 AM
00:00.0
greeter 1
greeter 2
46
2
13035, 13033??
Transport arrived to pick up
patient (13030?)
locker
13-Dec
191
greeter 1
Assisting family
8:32 AM
8:32 AM
00:00.0
13-Dec
192
greeter 1
Assisting family
8:36 AM
8:36 AM
00:00.0
13-Dec
193
greeter 2
Incoming call
8:40 AM
8:40 AM
00:00.0
13-Dec
194
greeter 1
Assisting family
8:40 AM
8:40 AM
00:00.0
13-Dec
195
greeter 1
Outgoing call
8:41 AM
8:41 AM
00:00.0
13-Dec
196
greeter 2
Incoming call
8:43 AM
8:43 AM
00:00.0
13-Dec
197
greeter 1
Assisting family
8:45 AM
8:45 AM
00:00.0
13-Dec
198
greeter 2
Incoming call
8:45 AM
8:45 AM
00:00.0
13-Dec
199
greeter 1
Incoming call
8:46 AM
8:47 AM
01:00.0
13-Dec
200
greeter 1
Outgoing call
8:49 AM
8:49 AM
00:00.0
13-Dec
201
greeter 1
Assisting family
8:50 AM
8:50 AM
00:00.0
13-Dec
202
greeter 2
Assisting family
8:47 AM
8:47 AM
00:00.0
13-Dec
203
greeter 2
Incoming call
8:53 AM
8:53 AM
00:00.0
13-Dec
204
greeter 1
Incoming call
8:53 AM
8:53 AM
00:00.0
13-Dec
205
greeter 2
Incoming call
8:56 AM
8:56 AM
00:00.0
13-Dec
206
greeter 2
Outgoing call
9:01 AM
9:01 AM
00:00.0
13-Dec
207
greeter 1
Incoming call
9:02 AM
9:02 AM
00:00.0
13-Dec
208
greeter 2
Assisting family
9:01 AM
9:01 AM
00:00.0
13-Dec
209
greeter 1
Assisting family
9:03 AM
9:03 AM
00:00.0
13-Dec
210
greeter 2
Assisting family
9:05 AM
9:05 AM
00:00.0
In addition to this full dataset excerpt, a PivotTable has been used to organize the data by
patient identifier to provide a sense of what data was captured for each individual patient
observed.
Patient Identifier
Arrival
13001
Initial
Processing
Leading to
room
0:03
13002
0:00
0:02
13003
0:00
0:01
13004
0:00
0:03
13005
0:00
0:04
13006
0:00
0:03
13007
0:00
0:03
13008
0:00
0:02
13009
0:00
0:02
13010
0:00
0:02
13011
0:00
0:03
13012
0:00
0:01
13013
0:00
0:02
13014
0:00
0:02
13015
0:00
0:02
47
Other
Herding
Total
Transport
Transport to
DAS
13016
0:00
0:02
13017
0:00
0:01
13018
0:00
0:02
13019
0:00
0:02
13020
0:00
0:02
13021
0:00
0:03
13022
0:00
0:03
0:06
13023
0:00
0:04
0:09
13024
0:00
0:02
13025
0:00
0:02
13026
0:00
0:03
13027
0:00
0:04
13028
0:00
0:03
13029
0:00
0:05
13030
0:00
0:02
13031
0:00
0:03
0:09
13032
0:00
0:02
0:08
13033
0:00
0:04
13034
0:00
0:04
13035
0:00
0:03
13036
0:00
0:06
13037
0:00
0:04
0:03
0:00
0:06
0:06
0:07
14001
0:00
14002
0:01
14003
0:00
14004
14005
0:01
0:00
0:04
14006
0:02
0:02
0:02
0:00
0:01
0:03
14007
0:00
0:04
0:01
0:01
0:01
14008
0:00
0:04
0:00
0:00
0:03
14009
0:00
0:03
0:02
0:00
0:03
14010
0:00
0:03
0:03
14011
0:00
14012
0:00
0:03
14013
0:00
0:03
0:02
0:00
0:03
14014
0:00
0:02
0:01
0:00
0:02
0:02
0:00
0:02
0:00
0:03
14017
0:02
0:00
0:02
14018
0:02
0:01
0:02
14019
0:01
0:00
0:03
14020
0:00
14021
0:01
14015
14016
18001
0:01
48
0:06
18002
0:00
0:02
18003
0:00
0:02
18004
0:00
0:02
18005
0:00
0:02
18006
0:00
0:04
18007
0:00
0:03
18008
0:00
0:02
18009
0:00
0:04
18010
0:00
0:02
18011
0:00
0:02
18012
0:00
0:02
18013
0:00
0:04
18014
0:00
0:04
18015
0:00
0:02
18016
0:00
0:02
18017
0:00
0:02
18018
0:00
0:02
21001
0:00
0:02
21002
0:00
0:02
141001
0:00
0:06
0:09
0:12
0:01
141002
Grand Average
0:07
0:01
0:00
A.2
0:02
0:01
0:00
0:00
0:07
0:02
HISTORICAL SURGERY SCHEDULE DATA
The arrival schedule for the simulation models is based on historical surgery schedule
data for the 2012 calendar year. The data includes weekdays Monday, Tuesday, Thursday and
Friday; the data does not include Wednesdays, weekends, or holidays. These days are excluded
in order to simplify the model and ensure reliability, since they are different in terms of arrivals
and scheduling.
49
50
APPENDIX B: SIMULATION MODEL INFORMATION
B.1
MODEL ASSUMPTIONS
The assumptions for the models are summarized below for reference purposes.
51
B.2
MODEL IMAGES
There were five models constructed over the course of the study. The models have been
discussed in detail in the text of this essay. However, images of the models have been included
below for further reference.
52
B.3
FURTHER ANALYSIS DETAIL
A strengths, weaknesses, opportunities, and threats analysis of the current state process is
included for reference below.
53
The detail surrounding the cost differential analysis is included for reference below.
The detail surrounding the ease of implementation analysis is included for reference below.
54
55
APPENDIX C: FLOOR PLANS
C.1
TRANSPORT PATH 1
The following diagrams depict the patient and family transport process for the current state, and
alternatives 1, 3, and 4.
56
C.2
TRANSPORT PATH 2
The following diagrams depict the patient and family transport process for alternative 2.
57
58
APPENDIX D: IMPLEMENTATION PLAN GANTT CHARTS
D.1
PHASE I
59
D.2
PHASE II OPTIONS
60
APPENDIX E: SIMULATION MODEL RESULT REPORTS
E.1
CURRENT STATE MODEL
61
62
63
64
65
66
67
E.2
ALTERNATIVE 1 MODEL
68
69
70
71
72
73
E.3
ALTERNATIVE 2 MODEL
74
75
76
77
78
79
E.4
ALTERNATIVE 3 MODEL
80
81
82
83
84
85
86
E.5
ALTERNATIVE 4 MODEL
87
88
89
90
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