Patient Transportation Staffing Simulation
Final Report
To:
LaKita Pogue B.S., Manager – Patient Transportation/Lift Team
Sam Clark, Industrial Engineer Lead - Program and Operations Analysis
Supervising Instructor:
Mark P. Van Oyen, Professor - IOE 481
Mary Duck, Industrial Engineer Lead - Program and Operations Analysis
From:
University of Michigan IOE 481 Project Team 8
Mr. Ethan Dennis
Mr. Connor McKee
Ms. Jenna Sparling
Ms. Yiwei Zhu
Date:
April 22, 2014
Table of Contents
EXECUTIVE SUMMARY ............................................................................................................ 1
Background ................................................................................................................................. 1
Methodology ............................................................................................................................... 1
Findings ....................................................................................................................................... 2
Conclusions ................................................................................................................................. 2
Recommendations ....................................................................................................................... 2
INTRODUCTION .......................................................................................................................... 3
BACKGROUND ............................................................................................................................ 3
Current Process ........................................................................................................................... 3
Current Staffing Schedules.......................................................................................................... 3
Midnight Shift.......................................................................................................................... 4
Afternoon Shift ........................................................................................................................ 4
Day Shift .................................................................................................................................. 4
Previous Staffing Analysis .......................................................................................................... 4
Patient Transportation Performance Goals ................................................................................. 4
Transport Request Acceptance Time ....................................................................................... 5
Transport Cancellation Rate .................................................................................................... 5
Transporter Production Rate .................................................................................................... 5
KEY ISSUES .................................................................................................................................. 5
GOALS AND OBJECTIVES ......................................................................................................... 5
PROJECT SCOPE .......................................................................................................................... 5
METHODOLOGY ......................................................................................................................... 6
Performed Literature Search ....................................................................................................... 6
Collected Data ............................................................................................................................. 6
Reorganized Data ........................................................................................................................ 6
Analyzed Data ............................................................................................................................. 7
Developed Simulation Base Model ............................................................................................. 7
Locations ................................................................................................................................. 7
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Entities ..................................................................................................................................... 7
The team created entities in the simulation named Patients. ................................................... 7
Arrivals .................................................................................................................................... 7
Shifts ........................................................................................................................................ 8
Resources ................................................................................................................................. 8
Processing ................................................................................................................................ 8
Variables .................................................................................................................................. 8
Macros ..................................................................................................................................... 8
Validated Simulation Baseline Model......................................................................................... 9
Adjusted Validated Simulation Baseline Model to Determine Impacts ..................................... 9
FINDINGS .................................................................................................................................... 11
Transport Tracking Data ........................................................................................................... 11
From Proposed Staffing Model ................................................................................................. 11
From Modified Shift Schedules ................................................................................................ 13
CONCLUSIONS........................................................................................................................... 15
Standard Shift Times ................................................................................................................. 15
UMHS Transport Request Acceptance Time ........................................................................ 15
Radiology Transport Request Acceptance Time ................................................................... 15
Overall Transport Time ......................................................................................................... 16
Modified Shift Times ................................................................................................................ 16
UMHS Transport Request Acceptance Time ........................................................................ 16
Radiology Transport Request Acceptance Time ................................................................... 16
Overall Transport Time ......................................................................................................... 16
RECOMMENDATIONS .............................................................................................................. 17
Increase in Patient Transportation Staffing Levels ................................................................... 17
Modification in Patient Transportation Shift Schedules ........................................................... 17
EXPECTED IMPACT .................................................................................................................. 17
APPENDIX A: PREVIOUS ANALYSIS .................................................................................... 19
APPENDIX B: ACCESS DATABASE ANALYSIS RESULTS ................................................ 20
APPENDIX C: SCREENSHOT OF SIMULATION ................................................................... 23
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List of Tables and Figures
Table 1: Current Staffing Level - Number of Transporters per day and shift………………...
8
Table 2: Comparison of Simulation Output Metrics to Real Life Scenario…………………... 9
Table 3: Proposed Staffing Level - Transporters per day and shift…………………………...
10
Table 4: Different Staffing Levels Considered………………………………………………..
10
Table 5: Modified Shift Times………………………………………………………………... 10
Figure 1: Completed Transports by Origin……………………………………………………
11
Figure 2: Average Transport Request Acceptance Times for 10 Staffing Levels…………….
12
Figure 3: Average Radiology Transport Request Acceptance Times for 10 Staffing Levels… 12
Figure 4: Average Patient Transport Times for 10 Staffing Levels…………………………..
13
Figure 5: Average Transport Request Acceptance Times for 10 Staffing Levels at Modified
Shift…………………………………………………………………………………
13
Figure 6: Average Radiology Transport Request Acceptance Times for 10 Staffing Levels at
Modified Shift Times……………………………………………………………….. 14
Figure 7: Average Patient Transport Times for 10 Staffing Levels and Modified Shift Times
14
Table 6: Expected Impact on Patient Transportation with Regards to Performance Goals…... 18
Figure B1: Completed Transports by Origin………………………………………………….
20
Figure B2: Completed Transports by Destination…………………………………………….
20
Figure B3: Completed Transports by Day of Week…………………………………………..
21
Figure B4: Completed Transports by Hour of Day (12am through 11am)…………………… 21
Figure B5: Completed Transports by Hour of Day (12pm through 11pm)…………………...
22
Table B1: Mean and Standard Deviation of Transport Times by Origin……………………... 22
Figure C1: Screenshot of Simulation Model………………………………………………….
23
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EXECUTIVE SUMMARY
The Patient Transportation department within the University of Michigan Health System
(UMHS) is responsible for moving patients and medical equipment, such as stretchers, between
different units of the hospital. Current Patient Transportation staffing levels are believed to be
too small to cover the UMHS demand of the Patient Transportation department. The Manager of
Patient Transportation asked an IOE 481 student team from the University of Michigan to
develop a simulation containing various units of UMHS that will record impacts associated with
increasing staffing levels and provide recommendations for future staffing levels. These impacts
include, but are not limited to, increased patient throughput, decreased turnaround time, and
increased ancillary test machine utilization. The primary goal of this project is to simulate Patient
Transportation’s performance with current and proposed staffing levels to determine potential
impacts associated with increasing Patient Transportation staffing levels.
Background
The Patient Transportation process begins by a transport request from the area where the patient
is currently located. Once a patient transporter becomes available to fulfill a transport request,
the transporter accepts the request. The transporter will then locate the patient to be moved and
will take the patient to their destination. Once the transporter completes the request, the
transporter will confirm the transport was successful and call in to receive directions for the next
transport.
The Patient Transportation department was started to reduce workload of patient care providers,
by taking the responsibility of transporting patients from the patient care providers. Currently,
Patient Transportation fulfils an average of 536 orders per day, serving every area of UMHS with
the exception of the OR. Patient Transportation currently has a total of 70 full-time employees.
Methodology
The team performed seven types of tasks to determine the impacts (increased patient throughput,
decreased turnaround time, and increased ancillary test machine utilization) associated with
increasing Patient Transportation staffing levels and to provide recommendations on how to
move forward with staffing levels.
● Collected Transport Tracking Data. The team obtained data in Microsoft Excel format
from a Programs and Operations Analysis employee for all patient transport entries from
January 2013 to December 2013. This data contained 132,618 completed transports and
24,611 cancelled transports
● Reorganized Transport Tracking Data. The Team reorganized the Transport Tracking
System data by removing all room numbers from the data and grouping together specific
operations. The following operations were grouped with the Radiology unit: EMG, EEG,
General Imaging, MRI, Nuclear Medicine, Interventional Ultrasound, and Ultrasound.
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● Analyzed Transport Tracking Data. The team imported the reorganized data in Microsoft
Access and designed queries to summarize the completed and cancelled transportation
data by origin, destination, origin and destination (entire trip), day of week, and hour of
day. The average and standard deviation of transporter dispatch-to-completion times were
calculated.
● Developed Simulation Base Model. The team built the simulation base model with
ProModel based on the data analyzed. Variables were created to track the performance of
current and proposed staffing level.
● Validated Simulation Baseline Model. The baseline ProModel simulation was validated
by comparing the model’s pending-to-dispatch time, dispatch-to-completion time, and
weekly transport volume to the current metrics.
● Adjusted Simulation Baseline Model to determine impacts. Changes were made to the
model’s staffing levels and staffing schedule to determine how these changes would
impact the key metrics listed above
● Performed literature search. A literature search resulted in gaining valuable data and
information from a previous UMHS employee’s work.
Findings
The team simulated ten different scenarios of staffing levels for 26 weeks using Patient
Transportation’s current shift schedules and modified schedules. Increasing staffing levels was
seen to exponentially decrease Patient Transportation performance metrics, which include
decreased patient transport request acceptance time, decreasing room turnaround time, and
increasing ancillary machine utilization. In addition to this, the team also saw the same
relationship with increasing staffing levels in addition to modifying the shift schedules.
Conclusions
The results of the simulation showed that when the percent increase in staffing levels for Patient
Transportation was over 10%; modified shift times helped Patient Transportation meet their
performance goal best. One of the Patient Transportation performance goals is to have 90% of
transport request acceptance times to be less than five minutes, and the simulation showed that
an increase in staffing of over 15% will allow Patient Transportation to meet this goal.
Recommendations
Based on the results of the simulation and previous analysis done, the team recommends the
following changes to Patient Transportation staffing levels: (1) Increase Patient Transportation
staffing levels by 13.4 FTE and (2) modify the shift schedules to fit high demand times of the
day.
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INTRODUCTION
The Patient Transportation department within the University of Michigan Health System
(UMHS) is responsible for moving mostly patients, but also medical equipment, such as
stretchers, between different units of the hospital. Patient Transportation has been unable to meet
their performance goals, which is believed to be attributed to staffing shortages, leading to longer
patient waiting times and low ancillary test machine utilization. The Manager of Patient
Transportation asked an IOE 481 student team from the University of Michigan to develop a
simulation containing various units of UMHS that will record the impacts of increasing staffing
levels and modifying the shift schedules. Furthermore, the team was asked to provide
recommendations as to how to move forward with staffing levels.
The team analyzed existing Patient Transportation data. From this data the team developed a
ProModel simulation that models the current staffing levels within Patient Transportation. The
team used this simulation to provide recommendations for how Patient Transportation should
proceed with its staffing levels. This report will outline how the team analyzed the Patient
Transportation data to develop the ProModel simulation, and how the team used the output from
the ProModel simulation to make staffing recommendations to Patient Transportation.
BACKGROUND
The Patient Transportation department was created to reduce the workload of patient care
providers by taking the responsibility of transporting patients from the patient care providers.
Currently, Patient Transportation fulfils an average of 536 orders per day, serving every area of
UMHS with the exception of the Operating Rooms (OR).
This section contains information regarding the current process, the current staffing schedules, a
previous staffing analysis completed by a Junior Management Engineer at Program and
Operations Analysis within UMHS, and the indicators of staffing shortages.
Current Process
The Patient Transportation process begins when a transport request is made from the area where
the patient is currently located. Once a patient transporter becomes available to fulfill a transport
request, the transporter accepts the request. The transporter will then locate the patient to be
moved and will take the patient to the required destination. Once the transporter completes the
request, the transporter confirms the transport was successful and calls in to receive directions
for the next transport.
Current Staffing Schedules
Patient Transportation has six part time employees and 70 full time employees. These employees
work in three shifts. These shifts include: the midnight shift, the afternoon shift, and the day
shift.
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Midnight Shift
The midnight shift takes place from 11:00 pm to 7:30 am. Five patient transporters work on
Sundays, Mondays, Fridays and Saturdays during the midnight shift. Out the five transporters,
four transport and one manages transportation equipment (such stretchers, wheelchairs, etc.). Six
patient transporters work Tuesdays through Thursdays. Out of the six transporters, five transport
and one manages transportation equipment.
Afternoon Shift
The afternoon shift takes place from 3:00 pm to 11:30pm. Seven transporters work on Sundays
during the afternoon shift. Out of the seven transporters, six transport and one manages
transportation equipment. On Mondays there are 14 transporters. Out of the 14 transporters, 11.5
transport, two manage the transportation equipment and one works in Physical Therapy (PT) for
half a shift (0.5). On Tuesdays through Fridays, 16 patient transporters work. Out of the 16
transporters, 13.5 transport, two manage transportation equipment, and one works in PT for half
a shift. Nine transporters work on Saturdays. Out of the nine transporters, eight transport and one
manages transportation equipment.
Day Shift
The day shift takes place from 7:00 am to 3:30 pm. On Sundays, 11 patient transporters work.
Out of the 11 transporters, 10 transport and one completes mini-tasks. On Mondays, Tuesdays,
Wednesdays, Thursdays and Fridays there are respectively 29, 34, 33, 31, and 34 patient
transporters. Out of the transporters that work Mondays through Fridays, three manage
transportation equipment, five work in PT, one completes mini-tasks and the rest transport. On
Saturdays, 12 patient transporters work. Out of the 12 transporters, 10.5 transport, and 1.5
completes mini-tasks.
Previous Staffing Analysis
In fall 2013, a Junior Management Engineer at Program and Operations Analysis within UMHS,
proposed a staffing model for Patient Transportation. The staffing model uses a linear program in
Microsoft Excel to calculate the staffing levels that would be required to meet the department’s
performance goals. The final result of the previous staffing model was a request for an additional
17 full-time employees for Patient Transportation. To determine the impacts of increasing
staffing levels, the simulation project was requested.
Patient Transportation Performance Goals
According to the previous staffing analysis, Patient Transportation has been unable to meet their
performance goals (Appendix A). These performance goals include: Transport Request
Acceptance Time, Transport Cancellation Rate, and Transporter Production Rate. The Manager
of Patient Transportation believes they can attribute staffing shortages to their inability to meet
these performance goals.
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Transport Request Acceptance Time
The transport request acceptance time represents the time from when a transport request is being
submitted to when a transporter becomes available to accept the request. The goal of Patient
Transportation is to have 90% of transport request acceptance times less than five minutes.
Currently, Patient Transportation has 51% of transport request acceptance times less than five
minutes (Appendix A).
Transport Cancellation Rate
A transport can be cancelled for a number of reasons. Some of these cancellation reasons include
a patient being transported by a different transporter or a patient’s wait time being too long. The
goal of the Patient Transportation department is to have a cancellation rate of 5%. Currently,
Patient Transportation has a cancellation rate of 16%.
Transporter Production Rate
Patient Transportation has a performance goal for each transporter to complete 2.5 transports per
hour. Currently patient transporters average 1.53 transports per hour per hour. In order to
complete this goal, the average overall transport time must be less than 24 minutes.
KEY ISSUES
The following key issues are driving the need for this project:
● Long transportation order acceptance times
● High transport order cancellation rates
● High workload and utilization required of patient transporters
GOALS AND OBJECTIVES
The primary goal of this project is to simulate Patient Transportation’s performance with current
and proposed staffing levels to provide potential impacts associated with increasing Patient
Transportation staffing levels. To achieve this goal, the team addressed the following objectives:
●
●
●
●
Increase patient throughput
Decrease transportation turnaround time
Increase ancillary test equipment utilization
Provide recommendations for Patient Transportation staffing levels
PROJECT SCOPE
This project included patient transportation that originates within UMHS. The units that were
focused on in the simulation include: inpatient nursing units grouped by floor (Floors 4 through
9), Radiology, Adult Emergency Department, Cardiovascular Center (CVC), and Children and
Women’s (CW). The project simulated the time between when a transport request was made to
when the patient transporter confirmed the transportation was completed.
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This project did not include any hospital operations besides patient transportation, nor did it
include patient transports that originate outside of the areas that Patient Transportation currently
provides service. The following units were not included in the simulation: Mott Hospital,
Taubman Center, Cancer Center, and Med Inn.
METHODOLOGY
The team performed seven types of tasks to determine the impacts (increased patient throughput,
decreased turnaround time, and increased ancillary test machine utilization) of increasing Patient
Transportation staffing levels and to provide recommendations on how to move forward with
staffing levels. These tasks include performing a literature search, collecting data, reorganizing
data, analyzing data, developing a simulation base model, validating the simulation base model,
and adjusting the validated simulation base model to determine impacts.
Performed Literature Search
The team looked at a previous staffing model analysis developed by a UMHS Program and
Operations Analysis employee in fall 2013, which is summarized in Appendix A below. The
team used the proposed staffing levels from this analysis as input for the proposed staffing levels
in developing the simulation.
Collected Data
An employee in the Program and Operations Analysis department at UMHS provided the team
with data from the Transport Tracking system in Excel format on January 31, 2014. This data
included all patient transport entries from January 2013 to December 2013, which contained
132,618 completed transports and 24,611 cancelled transports. The origin, destination, and
monitored timestamps of the transports were included as fields in this data. All patient identifiers
were removed from the data before the data was handed to the team.
Reorganized Data
With the Transport Tracking system data, the team used Microsoft Excel to reorganize the data.
The team reorganized the data by removing all room numbers from the data and grouping
together specific units. The following smaller units were grouped with the Radiology unit: EMG,
EEG, General Imaging, MRI, Nuclear Medicine, Interventional Ultrasound, and Ultrasound.
Once the data was organized by unit, it was adjusted to represent a 95% transport completion
rate, which meets Patient Transportation’s goal of a 5% cancellation rate. This was done by
combining the number of completed transports and cancelled transports for each unit by day of
week and hour of day, and then considering 95% of the resulting total of transport orders.
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Analyzed Data
Once the data was organized by unit and adjusted to represent a 95% transport completion rate,
the team used Microsoft Access to analyze the data. Queries were designed to compile all of the
data into a single database table as well as to calculate the transporter dispatch-to-completion
times, which represent the time a patient transporter is busy transporting a patient. Queries were
then written that summarized the completed and cancelled transportation data by:





Origin
Destination
Origin and destination (entire trip)
Day of week
Hour of day
In addition, queries were designed to analyze the average and standard deviation for the
transporter dispatch-to-completion times and transport order volume by unit.
Developed Simulation Base Model
Using the results from the Transport Tracking System data analysis, the team developed a
simulation model with ProModel with the purpose of determining the impacts of increasing
staffing levels. The following sections will discuss the locations, entities, arrivals, processing,
shifts, resources and variables used in the simulation model. A screenshot of this ProModel
simulation is attached in Appendix C.
Locations
The team created ten locations, which represent the ten units needed to be simulated: six
inpatient nursing units grouped by floor (UH 4-UH 9), Radiology, CVC, CW and the Emergency
Department.
Entities
The team created entities in the simulation named Patients.
Arrivals
The team used the arrival cycles function in ProModel to assign the percentage of transports
requested by hour of day, day of week, and the origin unit using distributions for transport order
volume. The team then created 70 arrivals of transport requests, one for each unit by day of week
(for example, Radiology on Monday and Radiology on Tuesday, etc.). The arrival cycles were
then assigned to each of the 70 arrivals. The arrivals of patients represent a transport order being
placed.
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Shifts
The team created 21 shift schedules. A schedule was created for the Midnight (11:00 pm - 7:30
am), Afternoon (3:00 pm - 11:30 pm) and Day Shift (7:00 am - 3:30pm) for each day of the
week.
Resources
The team created a variable number of resources, called Patient Transporters, depending on how
many Patient Transportation employees transport patients on that particular shift. Resources were
created for the Midnight, Afternoon and Day Shift for each day of the week. Shift schedules
were assigned to the corresponding resource (for example, the resource created for the Midnight
Sunday shift was assigned the shift calendar for Midnight Sunday).The number of each resource
for each day of the week and shift are shown in Table 1 below.
Table 1: Current Staffing Level - Number of Transporters per day and shift
Midnight
Afternoon
Day
5
7
11
Sunday
5
14
29
Monday
6
16
34
Tuesday
6
16
33
Wednesday
6
16
31
Thursday
5
16
34
Friday
5
9
12
Saturday
Processing
Once a patient (entity) arrives at a unit, the patient will wait until a Patient Transporter (resource)
is available. The entity will use the resource for the time that corresponds to the unit’s transporter
dispatch-to-completion time (Appendix B). The resource is then released after this time has
passed and the entity exits the system.
Variables
The team created variables to track the performance of different staffing level. These variables
include Radiology wait time, UMHS wait time, overall transport time, total number of patients
transported and total number of patients transported in Radiology.
Macros
To simulate the current and proposed staffing levels in ProModel’s Scenario Manager, the team
created 21 macros for each shift by day of week. The baseline of the macros is the current
staffing level, and these macros were changed while running different scenarios in Scenario
Manager.
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Validated Simulation Baseline Model
To provide staffing level recommendations to Patient Transportation, the team validated the
ProModel simulation to ensure the simulation accurately portrayed the current scenario. The
team considered the current staffing schedule, shown in Table 1 above, and compared the
model’s pending-to-dispatch time, dispatch-to-completion time and weekly transport volume to
the same metrics found through analyzing the Transport Tracking data. Previous analysis done
by a UMHS employee has shown that these three metrics follow Standard Normal distributions,
so the team was able to generate 95% Confidence intervals for each of these metrics. The 95%
confidence intervals, along with the ProModel simulation outputs, are shown for these three
metrics in Table 2 below.
Table 2: Comparison of Simulation Output Metrics to Real Life Scenario
Lower 95%
Upper 95%
2013 Transport
Confidence Level of Confidence Level of Tracking Data
Simulation Output
Simulation Output
Average
12.1 min
17.7 min.
12.6 min.
Pending-to-Dispatch Time
Dispatch-to-Completion
26.1 min
27.9min.
26.7 min.
Time
2547
2608
2576
Weekly Transport Volume
The 2013 Transport Tracking data’s pending-to-dispatch time was 12.6 minutes, which falls
within the 95% confidence interval of the pending-to-dispatch data from the simulation output.
The 2013 Transport Tracking data’s dispatch-to-completion time was 26.7 minutes, which falls
within the 95% confidence interval of the dispatch-to-completion data from the simulation
output. Lastly, the 2013 Transport Tracking data’s weekly transport volume was 2576 transports,
which falls within the 95% confidence interval for weekly transport volume from the simulation
output. The fact that these three output metrics are within their respective confidence interval
confirms the model accurately represents the real life scenario.
Adjusted Validated Simulation Baseline Model to Determine Impacts
To provide Patient Transportation with as much information as possible on proposed staffing
levels, the team considered ten different staffing level scenarios. The set of scenarios ranged
from the baseline staffing level shown in Table 1 to the staffing levels proposed by a Junior
Management Engineer at Program and Operations Analysis within UMHS, shown in Table 3
below.
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Table 3: Proposed Staffing Level - Transporters per day and shift
Midnight
Afternoon
Day
9
12
15
Sunday
Monday
9
18
34
Tuesday
Wednesday
Thursday
Friday
Saturday
12
11
11
10
11
18
19
18
19
14
33
37
33
35
19
In addition to the baseline staffing level and the proposed staffing level, the team also considered
a number of different staffing levels for the current staffing schedule. These staffing levels, their
Full Time Equivalent (FTE) increase, and their FTE percentage increase are shown in Table 4
below. A Full Time Equivalent employee is an employee that works five, 8-hour shifts in a
week.
Table 4: Different Staffing Levels Considered
Staffing Level
FTE Increase
FTE% Increase
Baseline
0
0%
Scenario 1
2.4
3.73%
Scenario 2
5.8
9.01%
Scenario 3
8.6
13.35%
Scenario 4
11.0
17.08%
Scenario 5
13.4
20.81%
Proposed Staffing Level
15.0
23.21%
Scenario 7
17.4
27.02%
Scenario 8
19.4
30.12%
Scenario 9
23.4
36.34%
The team also considered scenarios with modified start and end times for the three different
shifts, which was proposed by the client. The modified shift times are shown in Table 5 below.
Table 5: Modified Shift Times
Shift
Start Time End Time
11:00 A.M. 7:30 P.M.
Day
3:30 A.M.
Midnight 7:00 P.M.
Afternoon 3:00 A.M. 11:30 A.M.
10
These shifts were considered with the same baseline and proposed staffing levels, along with the
scenarios shown in Table 4 above.
FINDINGS
This section discusses the findings from the analysis of the Transport Tracking data and the
proposed staffing model.
Transport Tracking Data
To develop an adequate ProModel simulation, extensive analysis of Patient Transportation’s
2013 Transport Tracking data was required. The team found the number of completed transports
by origin and by destination for the ten units considered, which is shown in Figure 1 below.
Figure 1: Completed Transports by Origin
sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System
The 2013 Transport Tracking data was also stratified to represent to transport volume by:
destination unit, entire trip, day of week, and hour of day. The average and standard deviation of
transporter dispatch-to-completion times were also calculated. In addition, the team calculated
the percentage of transport requests for all ten units by hour of the day and by the day of week.
The results of the analysis are summarized in Appendix B.
From Proposed Staffing Model
The team looked at ten staffing level scenarios for the current shift times ranging from the
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baseline staffing level, to the proposed staffing level (with an increase of 15 FTE), to a staffing
level with an increase of 23.4 FTE.
The average transport request acceptance time (the time from when a patient requests a transport
until a transporter accepts the transport) for these scenarios is shown in Figure 2 below. The
average transport request acceptance time is a representation of room turnaround time.
Figure 2: Average Transport Request Acceptance Times for 10 Staffing Levels
simulation length: 26 weeks, scenarios: 10, source: ProModel simulation
Figure 3 below shows the average radiology transport request acceptance time for the 10
scenarios. The radiology transport request acceptance time directly relates to ancillary test
equipment utilization.
Figure 3: Average Radiology Transport Request Acceptance Times for 10 Staffing Levels
simulation length: 26 weeks, scenarios: 10, source: ProModel simulation
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The average patient transport time for each of the 10 scenarios is shown in Figure 4 below.
Figure 4: Average Patient Transport Times for 10 Staffing Levels
simulation length: 26 weeks, scenarios: 10, source: ProModel simulation
From Modified Shift Schedules
The team looked at the ten staffing level scenarios for the modified shift times. The average
transport request acceptance time (the time from when a patient requests a transport until a
transporter accepts a transport) for each of these metrics is shown in Figure 5 below. The average
transport request acceptance time is a representation of room turnaround time.
Figure 5: Average Transport Request Acceptance Times for 10 Staffing Levels at Modified Shift
simulation length: 26 weeks, scenarios: 10, source: ProModel simulation
13
The average radiology transport request acceptance time for each of the 10 scenarios is shown in
Figure 6 below. These times are a representation of ancillary test equipment utilization.
Figure 6: Average Radiology Transport Request Acceptance Times for 10 Staffing Levels at
Modified Shift Times
simulation length: 26 weeks, scenarios: 10, source: ProModel simulation
The average patient transport time for each of the 10 scenarios is shown in Figure 7 below.
Figure 7: Average Patient Transport Times for 10 Staffing Levels at Modified Shift Times
simulation length: 26 weeks, scenarios: 10, source: ProModel simulation
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CONCLUSIONS
The simulation was run using both Patient Transportations current shift schedules and modified
shift schedules, seen in Table 5. The results of both of these simulations are summarized below.
Standard Shift Times
The simulation was first run using the current shift schedules seen in Table 5 for Patient
Transportation. The simulation was run for 26 weeks, using 10 scenarios of staffing levels. The
results are summarized below.
UMHS Transport Request Acceptance Time
The value represented by UMHS transport request acceptance time is the room turnaround time
for UMHS as a whole, and can be seen in Figure 2. Room turnaround time can be represented by
UMHS transport request acceptance time because the acceptance time represents the amount of
time that a patient is waiting for transportation while there is no transporter currently available.
Reducing this time will allow for more patients to move throughout the hospital.
The transport request acceptance time using the current staffing levels of Patient Transportation
was simulated to be 19 minutes. When the staffing levels were incremented upwards, there is an
exponential drop off of transport request acceptance time. The performance goal of the Patient
Transportation department is to have 90% of these values less than 5 minutes. The transport
request acceptance time associated with the proposed increase of staff, 15.0 FTE, is 1.2 minutes.
Radiology Transport Request Acceptance Time
The value represented by the Radiology transport request acceptance time is the increase in
ancillary test machine utilization, and can be seen in Figure 3. Ancillary test machine utilization
can be represented by Radiology transport request acceptance times because it represents the
time that a patient in Radiology is waiting for transportation while there are no patient
transporters currently available. Decreasing this time will allow for patients to be transported to
and from ancillary test machines more quickly.
The Radiology transport request acceptance times using the current staffing levels of the Patient
Transportation was simulated to be 20.7 minutes. These times also drop off exponentially when
the staffing levels are incremented. The transport request acceptance time associated with the
proposed increase of staff, 15.0 FTE increase, is 1.4 minutes. Having a higher ancillary test
machine utilization rate will increase UMHS revenue because there will be less wasted time for
the machines.
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Overall Transport Time
The overall transport time represents the time that passes between a transport order being placed
and when the transport is completed and confirmed, and can be seen in Figure 4. The simulated
overall transport time using the current staffing levels of the Patient Transportation department is
46.1 minutes. The overall transport times decreased exponentially when the staffing levels were
increased. When the staffing levels were increase to the proposed levels, 15.0 FTE increase, the
overall transport time decreased to 28.3 minutes.
Modified Shift Times
The simulation was then changed to run using the modified shift times seen in Table 5 for Patient
Transportation. The simulation was run for 26 weeks using ten scenarios of staffing levels. The
results are summarized below.
UMHS Transport Request Acceptance Time
Compared with the standard shift times, the modified shift times did not perform as well as the
current shift times when the increase was below 6.0 FTE. However, the modified shift times
performed better than the current shift times for increases above 6.0 FTE. Since the performance
goal of the Patient Transportation department is to have 90% of these values less than 5 minutes,
an increase above 6.0 FTE should be considered, as can be seen from Figure 2 and Figure 5. The
transport request acceptance time associated with the proposed increase of staff with modified
shift times is 0.5 minutes, which is 0.7 minutes less than the standard shift times.
Radiology Transport Request Acceptance Time
The current shift time model performed better when the increases was below 6.0 FTE and the
modified shift time model performed better for increases above 6.0 FTE. Since the performance
goal of the Patient Transportation department is to have 90% of transport request acceptance
times less than 5 minutes, an increase above 6.0 FTE should be considered, as can be seen from
Figure 3 and Figure 6. The Radiology transport request acceptance time associated with the
proposed increase of staff with modified shift times is 0.5 minutes, which is 0.9 minutes less than
the standard shift times at the same increase in staffing.
Overall Transport Time
The standard shift time model has a better performance when the increase was below 6.0 FTE,
while the modified shift time model does better above an increase of 6.0 FTE. Since the
performance goal of the Patient Transportation department is to have 90% of these values less
than 30 minutes, an increase above 6.0 FTE should be considered, as can be seen from Figure 4
and Figure 7. The transport request acceptance time associated with the proposed increase of
staff with modified shift times is 27.6 minutes, which is 0.7 minutes less than the standard shift
times.
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RECOMMENDATIONS
Based on the results of the team’s simulation the team has developed the following
recommendations:
Increase in Patient Transportation Staffing Levels
Increasing the Patient Transportation staffing levels will allow for the Patient Transportation
department to make their current performance goals, according to the team’s simulation. The
team recommends an increase in their current staffing levels of 13.4 FTE. This increase in
staffing levels corresponds to transport request acceptance times of 30-60 seconds, regardless of
shift schedule.
Modification in Patient Transportation Shift Schedules
The team discovered that when the shift times were changed to the modified shift schedules seen
in Table 5, in conjunction to an increase of 13.4 FTE, the performance of the Patient
Transportation department was higher than the simulation using the current shift schedules at the
same increase in staffing. This can be seen by comparing the values of Figure 2 and 3 at the 13.4
increase in FTE. When the current shift schedules were used, the transport request acceptance
time was 1.2 minutes, while the modified shift schedules showed this value was 0.5 minutes.
EXPECTED IMPACT
Should the Patient Transportation department implement the recommendations provided above,
they can expect to see the following improvements:
● Increase of patient throughput
● Decrease in room turnaround time
● Increase in ancillary test equipment utilization
While it’s difficult to know how well the model would represent Patient Transportation’s
workload if there was a staffing level increase and/or shift schedule modification, the team
believes their model will accurately portray the Patient Transportation department. The expected
impact on Patient Transportation’s staffing, with regards to their performance goals, is shown in
Table 6 below.
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Table 6: Expected Impact on Patient Transportation with Regards to Performance Goals
Performance Goal Current Performance FTE Increase Resulting Performance
90% transport
request acceptance
times < 5 mins
95% completion rate
2.5 transports per
hour per transporter
51% < 5 mins
12.6 min (average)
13.4
90% < 5 mins
0.5 min (average)
84%
13.4
95%
1.53 (average)
13.4
2.17 (average)
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Copy available upon request
APPENDIX A: PREVIOUS ANALYSIS
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APPENDIX B: ACCESS DATABASE ANALYSIS RESULTS
Figure B1: Completed Transports by Origin
sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System
Figure B2: Completed Transports by Destination
sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System
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Figure B3: Completed Transports by Day of Week
sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System
Figure B4: Completed Transport by Hour of Day (12am through 11am)
sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System
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Figure B5: Completed Transports by Hour of Day (12pm through 11pm)
sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System
Table B1: Mean and Standard Deviation of Transport Times by Origin
Transport Time (minutes)
Origin
Average
Standard Deviation
31.4
10.8
Emergency Department
23.31
11.52
Radiology
27.37
9.15
UH 4
27.83
9.71
UH 5
28.34
15.18
UH 6
27.76
10.9
UH 7
28.44
9.9
UH 8
27.19
8.67
UH 9
29.29
10.96
CVC
27.41
12.54
CW
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APPENDIX C: SCREENSHOT OF SIMULATION
Figure C1: Screenshot of Simulation Model
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