Improving Patient Transportation Performance by Developing … · Abstract Improving Patient...

Improving Patient Transportation Performance byDeveloping and Implementing a Generic Simulation Model

by

Carly Henshaw

A thesis submitted in conformity with the requirementsfor the degree of Master of Applied Science

Graduate Department of Mechanical and Industrial EngineeringUniversity of Toronto

c© Copyright 2015 by Carly Henshaw

Abstract

Improving Patient Transportation Performance by Developing and Implementing a

Generic Simulation Model

Carly Henshaw

Master of Applied Science

Graduate Department of Mechanical and Industrial Engineering

University of Toronto

2015

Patient transportation is a department in hospitals responsible for transporting patients

or items from one point of a hospital to another. Without proper coordination of this

department, patient flow and hospitals functions are impacted. With the end goal of

improving portering efficiency, this thesis has two main objectives: the first is to develop

a generic portering simulation model; the second is to test the model’s generality by

using it as a decision support tool to improve porter performance at North York General

Hospital and Juravinski Hospital and Cancer Centre.

Once the simulation models were validated, improvement scenarios were tested. These

scenarios were developed in collaboration with the hospitals involved. The result of

this scenario testing yielded an improvement in key performance indicators including:

dispatch time, turnaround time and percentage of tasks completed within the target time.

This evidence-based research will be used to support future portering improvements.

ii

Acknowledgements

I would like to thank my supervisor, Professor Michael Carter for his encouragement,

patience and expertise during our meetings and throughout various milestones in my

research. If I was ever confused on what to do next or how I could improve my research,

he always gave me direction and reassurance.

I would also like to thank my lab colleagues for all their guidance as I settled into

my research, as well as their discussions and input regarding my research. Over the two

summer semesters of my Master’s, I had the help of high school student Christian Mele,

who I would like to thank for his dedication and eagerness towards my project.

From North York General Hospital, I would like to thank James Ibbott who shared

so much enthusiasm for my project and overall passion for improving healthcare, which

was very inspiring for me as I looked towards a career in healthcare. I appreciated his

leadership and patience throughout the two years we worked together. I would also like

to thank the portering manager, Kenny Paiva, for his commitment to my project.

At Juravinski Hospital and Cancer Center, from the Customer Support Services de-

partment, I would like to thank portering managers Frank Amatangelo and Ian Deans,

as well as site manager David DiSimoni for sharing their opinions and ideas throughout

this project. I would also like to thank Corey Stark for guiding me at the beginning of

this project and for sharing his technical expertise. Finally, I would like to thank Talha

Hussain for coordinating this project and offering me both research and career related

advice.

Lastly, I would like to thank my parents for always supporting and encouraging me

to never give up, and for taking the time to review some of my work. I would also like

to thank Ryan for his valuable feedback and opinions throughout the past two years.

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Contents

1 Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.1.1 North York General Hospital . . . . . . . . . . . . . . . . . . . . . 3

1.1.2 Juravinski Hospital and Cancer Centre . . . . . . . . . . . . . . . 6

1.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2 Literature Review 12

2.1 Root Causes for Patient Flow Delays . . . . . . . . . . . . . . . . . . . . 12

2.2 Adaptation of New Technology . . . . . . . . . . . . . . . . . . . . . . . 13

2.3 Adaptation of Qualitative Techniques . . . . . . . . . . . . . . . . . . . . 14

2.4 Optimization of the Porter Schedule . . . . . . . . . . . . . . . . . . . . . 16

2.5 Comparison to another Process . . . . . . . . . . . . . . . . . . . . . . . 18

2.6 Discrete Event Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.7 Generic Simulation Models . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3 Methodology 23

iv

3.1 Generic Simulation Model Development . . . . . . . . . . . . . . . . . . . 23

3.1.1 Model Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.1.2 Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.1.3 Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.2 Simulation Model: NYGH . . . . . . . . . . . . . . . . . . . . . . . . . . 30

3.2.1 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.2.2 Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.2.3 Assumptions and Limitations . . . . . . . . . . . . . . . . . . . . 36

3.2.4 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.3 Simulation Model: JHCC . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.3.1 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.3.2 Inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.3.3 Assumptions and Limitations . . . . . . . . . . . . . . . . . . . . 47

3.3.4 Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4 Scenario Testing 53

4.1 Improvement Scenarios: NYGH . . . . . . . . . . . . . . . . . . . . . . . 53

4.2 Improvement Scenarios: JHCC . . . . . . . . . . . . . . . . . . . . . . . 59

4.3 Sensitivity Analysis: NYGH . . . . . . . . . . . . . . . . . . . . . . . . . 64

4.4 Sensitivity Analysis: JHCC . . . . . . . . . . . . . . . . . . . . . . . . . 65

5 Results 66

5.1 Simulation Results: NYGH . . . . . . . . . . . . . . . . . . . . . . . . . 66

v

5.2 Simulation Results: JHCC . . . . . . . . . . . . . . . . . . . . . . . . . . 72

6 Conclusion 77

7 Future Research 79

References 80

A Process Maps for Dispatching Processes 86

A.1 Process Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

B Porter Shift Schedules 88

B.1 Porter Schedule: NYGH . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

B.2 Porter Schedule: JHCC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

C Further Scenario Testing Results 91

C.1 Further Results: NYGH . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

C.2 Further Results: JHCC . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

vi

List of Tables

1.1 Evaluation of NYGH’s Porter Performance . . . . . . . . . . . . . . . . . 10

1.2 Evaluation of JHCC’s Porter Performance . . . . . . . . . . . . . . . . . 11

3.1 Task Types Measured for each Performance Metric . . . . . . . . . . . . 30

3.2 Task Breakdown at NYGH . . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Personal Response Times at NYGH . . . . . . . . . . . . . . . . . . . . . 35

3.4 Task Times at NYGH . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

3.5 Cancellation Rates at NYGH . . . . . . . . . . . . . . . . . . . . . . . . 36

3.6 Task Breakdown at JHCC . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.7 Priority Level Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.8 Arrival Minutes at JHCC . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3.9 Patient Minutes at JHCC . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3.10 Cancellation Rates at JHCC . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.1 Adding Weekday Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

4.2 Adding Weekend Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4.3 Changing Weekday Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . 57

vii

4.4 Changing Weekend Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . 57

4.5 Scenario 8 Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

4.6 Adding Weekday Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

4.7 Adding Weekend Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.8 Changing Weekday Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.9 Changing Weekend Shifts . . . . . . . . . . . . . . . . . . . . . . . . . . 62

4.10 Scenario 6 Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

4.11 Sensitivity Analysis Scenarios: NYGH . . . . . . . . . . . . . . . . . . . 64

4.12 Sensitivity Analysis Scenarios: JHCC . . . . . . . . . . . . . . . . . . . . 65

5.1 NYGH Scenario Legend . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.2 NYGH Confidence Intervals for Weekday Results . . . . . . . . . . . . . 68

5.3 KPIs resulting from Simulation . . . . . . . . . . . . . . . . . . . . . . . 69

5.4 JHCC Scenario Legend . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.5 JHCC Confidence Intervals for Weekday Results . . . . . . . . . . . . . . 74

5.6 KPIs resulting from Simulation . . . . . . . . . . . . . . . . . . . . . . . 74

C.1 NYGH Confidence Intervals for Weekend Results . . . . . . . . . . . . . 92

C.2 JHCC Confidence Intervals for Weekend Results . . . . . . . . . . . . . . 92

viii

List of Figures

1.1 NYGH Arrival Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2 Timeline of Porter Process at NYGH . . . . . . . . . . . . . . . . . . . . 5

1.3 NYGH Porter Process Map . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 JHCC Arrival Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.5 Timeline of Porter Process at JHCC . . . . . . . . . . . . . . . . . . . . 8

1.6 JHCC Porter Process Map . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.7 Cause and Effect Diagram of Poor Porter Performance . . . . . . . . . . 10

3.1 Generic Simulation Model Outline . . . . . . . . . . . . . . . . . . . . . . 24

3.2 NYGH Porter Simulation Model . . . . . . . . . . . . . . . . . . . . . . . 32

3.3 Validation of Dispatch Time . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.4 Validation of Transport Time . . . . . . . . . . . . . . . . . . . . . . . . 39

3.5 Validation of Turnaround Time . . . . . . . . . . . . . . . . . . . . . . . 40

3.6 Validation of % Scheduled Tasks Completed by Appointment Time . . . 41

3.7 Validation of % Unscheduled Tasks Completed within 35 minutes . . . . 41

3.8 JHCC Porter Simulation Model . . . . . . . . . . . . . . . . . . . . . . . 42

3.9 Validation of Dispatch Time . . . . . . . . . . . . . . . . . . . . . . . . . 49

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3.10 Validation of Trip Time . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.11 Validation of Transaction Time . . . . . . . . . . . . . . . . . . . . . . . 50

3.12 Validation of % Prebooked Tasks On Time . . . . . . . . . . . . . . . . . 51

3.13 Validation of % Tasks Completed within 30 minutes . . . . . . . . . . . . 52

4.1 Supply-Demand Graph for an Average Thursday . . . . . . . . . . . . . . 54

4.2 Supply-Demand Graph for an Average Saturday . . . . . . . . . . . . . . 55

4.3 Determining Poor Performing Weekday Hours . . . . . . . . . . . . . . . 56

4.4 Determining Poor Performing Weekend Hours . . . . . . . . . . . . . . . 56

4.5 Supply-Demand Graph for an Average Thursday . . . . . . . . . . . . . . 59

4.6 Supply-Demand Graph for an Average Saturday . . . . . . . . . . . . . . 60

4.7 Determining Poor Performing Weekday Hours . . . . . . . . . . . . . . . 62

4.8 Determining Poor Performing Weekend Hours . . . . . . . . . . . . . . . 62

5.1 Weekday Turnaround Time: Evaluating Improvement Scenarios . . . . . 69

5.2 Weekend Turnaround Time: Evaluating Improvement Scenarios . . . . . 70

5.3 Weekday Turnaround Time: Sensitivity Analysis . . . . . . . . . . . . . . 70

5.4 Weekend Turnaround Time: Sensitivity Analysis . . . . . . . . . . . . . . 70

5.5 Weekday Transaction Time: Evaluating Improvement Scenarios . . . . . 75

5.6 Weekend Transaction Time: Evaluating Improvement Scenarios . . . . . 75

5.7 Weekday Transaction Time: Sensitivity Analysis . . . . . . . . . . . . . . 76

5.8 Weekend Transaction Time: Sensitivity Analysis . . . . . . . . . . . . . . 76

A.1 NYGH Dispatcher Process Map . . . . . . . . . . . . . . . . . . . . . . . 87

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A.2 JHCC Dispatch Process Map . . . . . . . . . . . . . . . . . . . . . . . . 87

B.1 NYGH Porter Shift Schedule . . . . . . . . . . . . . . . . . . . . . . . . . 89

B.2 JHCC Porter Shift Schedule . . . . . . . . . . . . . . . . . . . . . . . . . 90

C.1 Weekday Results of KPIs: Base Case vs. All Scenarios . . . . . . . . . . 93

C.2 Weekend Results of KPIs: Base Case vs. All Scenarios . . . . . . . . . . 93

C.3 Weekday Results of KPIs: Base Case vs. All Scenarios . . . . . . . . . . 94

C.4 Weekend Results of KPIs: Base Case vs. All Scenarios . . . . . . . . . . 94

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Chapter 1

Introduction

Hospitals are extremely complex systems with many staff, physicians, nurses and patients

that are all expected to work together without much coordination [1]. A hospital requires

continuous patient flow so that new patients can enter the hospital as old patients leave.

The role of a patient transporter, or porter, in hospital operations is critical to ensuring

proper patient flow throughout a hospital. However, sometimes there are delays in the

porter process that are encountered which negatively affect patient flow. These delays, or

wastes, can be reduced by identifying the bottlenecks in the process and removing them

[2]. Making improvements to a hospital’s porter process is important because it can help

lead to an overall improved patient experience. Improvements in porter efficiencies are

directly related to Health Canada’s mission and vision of maintaining and improving the

health of Canadians [3].

Due to the increasing number of patients entering hospitals each year, along with

the uncertainty of when patients and items need to be transported, there are associated

delays in the completion of porter tasks. A delay in transporting patients or items affects

the entire hospital’s ability to function efficiently. For example, a delay delivering a

patient affects the schedules of surgical procedures and wastes the physician’s valuable

1

Chapter 1. Introduction 2

time. Also, delays affect the utilization of an expensive resource such as an MRI machine,

where its use should be maximized [4, 5].

Many hospitals measure their performance quantitatively using Key Performance In-

dicators (KPIs) [6, 7, 8]. The two hospitals involved in this project, North York General

Hospital and Juravinski Hospital and Cancer Centre monitor their porter performance

using defined KPIs, such as turnaround time (from task request to completion), response

time (from task acceptance to completion) and percentage of scheduled tasks completed

on time. Hospitals use KPIs to compare their performance to internal hospital targets

or targets of other hospitals. KPIs are also used to monitor progress towards these tar-

gets. Comparisons are important because if a hospital is below a benchmark, it is an

indicator that the process must be improved. General health care KPIs could be based

on improving process or patient flow and eliminating system bottlenecks.

When it comes to improving hospital operations, often times the porter process is

overlooked as an area of improvement that can have a lasting effect [5]. However, a

hospital cannot run efficiently and effectively without proper management, coordination

and scheduling of their portering department. Improvements to the porter process could

have a very beneficial impact on both patient experience and hospital functions [1].

However, evidence is needed to identify the improvements to be implemented.

1.1 Background

The two hospitals involved in this research are North York General Hospital (NYGH)

in Toronto and Juravinski Hospital and Cancer Center (JHCC) in Hamilton. Both hos-

pitals were motivated to improve their porter processes after determining their porter

performance was below a benchmark for their hospital. The benchmark was determined

by the portering departments at each hospital.


1.1.1 North York General Hospital

North York General Hospital’s portering department handles over 100,000 tasks per year

with about 325 tasks per weekday and 160 tasks per weekend. See Figure 1.1 for a

breakdown of these tasks by patient and item tasks (Figure 1.1a) and by hour of day

and day of week (Figure 1.1b). NYGH has seen an increase in porter tasks throughout

the past four years and accordingly an increase in patient transport times. According

to the porter manager at NYGH, a porter task can be classified as either scheduled or

unscheduled, patient or item, as well as by priority, either STAT or Routine. Unscheduled

tasks account for about 85% of all tasks and arrive as necessary throughout the day.

Scheduled tasks have an appointment time that is known in advance. Patient tasks tend

to take longer to complete than item tasks since there are more delays associated when

transporting a person compared to an item. Patient tasks at NYGH account for about

75% of all tasks. The final classification of tasks are STAT and Routine which is based

on priority. STAT tasks are urgent and move to the top of the dispatch queue whereas

Routine tasks are not urgent but still need to be completed in a timely manner.

The first stage in this study was to analyze the current operations of portering at

NYGH. To do this, meetings were conducted with the porter managers and patient flow

and improvement teams at each hospital, porters and dispatchers were shadowed and

discussions were had with the porters on how they completed their task duties. As a

result, a porter timeline was developed as well as process maps.

The timeline will be discussed first (Figure 1.2). The first checkpoint is receipt where

a porter task is received at the dispatch center; the second is dispatch where a porter is

assigned to a task by the dispatcher; the third is arrival where a porter arrives at the

origin of a task; and the forth is complete where a porter calls into the dispatch center

to report the task is completed. At the dispatch checkpoint, the porter receives a page

with the task information and responds to this page by locating a landline, calling in


(a) NYGH Arrivals by day of week and task type

(b) NYGH Arrivals by hour of day and day of week

Figure 1.1: NYGH Arrival Analysis

to the dispatcher and identifying that he or she is accepting or declining the task. At

the arrival and complete checkpoints, the porter calls in using a landline to report on

his/her progress. Key performance indicators (KPIs) are measured between each of these

checkpoints. The KPIs that are currently monitored are: turnaround time (time from

receipt to complete), response time (time from receipt to arrival), transport time (time

from arrival to complete), % tasks cancelled and % scheduled tasks on-time (scheduled

tasks only). Each of these KPIs has a target, defined by NYGH, which is currently not

being achieved.

Process maps were developed for both the porters as well as the manual dispatcher.


Figure 1.2: Timeline of Porter Process at NYGH

The process maps describe the decisions made by the porter or dispatcher at each stage in

the process. According to Hall, et al., this approach helps to identify delays and system

bottlenecks [9]. The resulting process map of the porter process at NYGH can be seen in

Figure 1.3. The process maps of the dispatcher at NYGH can be seen in Appendix A.

Figure 1.3: NYGH Porter Process Map

NYGH’s portering department has generated some ideas to improve the above men-


tioned KPIs. These improvements include supply-demand matching alternatives such as

adding more team attendants (team attendants are similar to porters but are decentral-

ized to a specific unit), developing surge capacity protocols and modifying the current

porter schedule. Other improvements include introducing better communication technol-

ogy, reducing elevator delays, adding more equipment, improving storage of equipment,

improving STAT call criteria, reducing double porter tasks, managing individual porter

performance, assigning porters based on unit/area, using real time porter data and using

monthly performance data more effectively. As these changes are not yet implemented, it

would be beneficial to test their impact on the KPIs measured at NYGH using a discrete

event simulation to determine the benefit, if any, from implementing them.

1.1.2 Juravinski Hospital and Cancer Centre

Juravinski Hospital and Cancer Centre processes about 128,000 porter tasks over one

year with about 390 tasks each weekday and 210 tasks per weekend. See Figure 1.4 for a

breakdown of these tasks by patient and item tasks (Figure 1.4a) and by hour of day and

day of week (Figure 1.4b). According to the porter manager at JHCC, their goal is to

identify opportunities to achieve efficiencies in porter utilization, reduce patient/provider

waiting times, and contribute to improvements in patient flow at the hospital. Tasks at

JHCC are categorized into scheduled and unscheduled, patient and item, and are given

a priority. At JHCC, there are roughly an equal number of patient and item tasks

processed each day. The priority matrix used at JHCC assigns a value from 0 to 8 to

tasks with 0 being extremely urgent and 8 not being urgent at all. Tasks can move up in

priority depending on how long they have been sitting in the dispatch queue as well as

other factors which will be discussed. In order to compare JHCC priorities with NYGH,

through discussion with the porter manager, a task with original priority 0 to 2 will be

considered STAT and an original priority of 3 to 8 will be considered Routine.


(a) JHCC Arrivals by day of week and task type

(b) JHCC Arrivals by hour of day and day of week

Figure 1.4: JHCC Arrival Analysis

The current operations of portering at JHCC were analyzed similarly to NYGH which

included meetings with porter managers, shadowing porters and dispatchers, and dis-

cussing porter task duties with porters and managers. As a result, a porter timeline was

developed as well as process maps.

A major difference between JHCC and NYGH is that JHCC has an automated dis-

patch system. This automated dispatch system is very complex and many factors go into

calculating a task’s priority. As soon as a new porter is available, the priorities of all tasks

waiting to be dispatched are re-calculated and the next highest priority task is assigned

to that available porter. The factors that affect this new priority include: how long the


task has been in the queue, how close is the porter to the origin of the waiting task, is

the task STAT, and is the task’s appointment time within 30 minutes. Depending on

the answers to these questions, task priorities may increase. A process map of the logic

behind the automated dispatch system can be seen in Appendix A. JHCC’s checkpoints

in their porter process include pending where a task is input via computer or telephone to

the portering system, dispatch where a task is assigned to an available porter, in progress

where a porter is en-route to the destination of the task, and finally complete where a

porter calls into the automated dispatching system to report the task is complete. The

time between dispatch and in-progress is arrival minutes and it is of interest to reduce

this time because this is where most delays occur. These delays can be attributed to

equipment delays, patient readiness delays, elevator delays, and other matters. JHCC’s

timeline of events in the porter process can be seen in Figure 1.5. A process map of the

porter process at JHCC can be seen in Figure 1.6.

Figure 1.5: Timeline of Porter Process at JHCC

Past improvements to the porter process at JHCC included the adaptation a few

years ago of the automated dispatching system which improved the time from pending to

dispatch (the dispatch time) by several minutes. Other improvements included studying

equipment availability for porters which was done by undergraduate engineering students

at McMaster University as part of their Capstone Project in 2014.


Figure 1.6: JHCC Porter Process Map

1.2 Problem Definition

Through observations and conversations at each hospital in this study, some potential

causes of inefficiency and bottlenecks in the current porter process were identified and

can be seen in Figure 1.7. These causes are divided into equipment, method, people

and environment-related issues. Reducing these issues and eliminating bottlenecks in the

process would increase the capacity of porters.

At NYGH, key performance metrics of the current porter department performance

were compared against benchmarks. Table 1.1 shows NYGH’s KPIs and it can be seen

that NYGH’s performance almost never meets the proposed target. Table 1.2 compares

JHCC’s porter performance to targets based on similar KPIs to NYGH. It can be seen

that NYGH is further from their defined targets than JHCC is from their own targets.

This is be due to a variety of factors, including differences between each hospital’s porter

process, which will be discussed. An objective of this research is to improve or eliminate


the gap between current performance and target performance.

Figure 1.7: Cause and Effect Diagram of Poor Porter Performance

Table 1.1: Evaluation of NYGH’s Porter Performance

KPI Target 2014

Turnaround Time (Receipt to Complete)Unscheduled Tasks

35 minutes 47.5 minutes

Response Time (Receipt to Arrival) Un-scheduled Tasks


Transport Time (Dispatch to Complete) 17 minutes 24.6 minutes

% of Cancelled Tasks 5% 11%

% Scheduled Tasks Completed On-Time 90% 35.8%

1.3 Research Objectives

The objective of this thesis is to develop a generic simulation model that can be applied

to the patient transportation process at North York General Hospital and Juravinski

Hospital and Cancer Centre. The simulation model calculates KPIs when changes to

the current process were tested. The simulation model runs for several scenarios that


Table 1.2: Evaluation of JHCC’s Porter Performance

KPI Target July-Dec. 2013

Transaction Minutes (Pending to Com-pletion) Unscheduled Tasks


Response Minutes (Receipt to InProgress) Unscheduled Tasks


Trip Minutes (Dispatch to Complete) 20 minutes 21.5 minutes

% of Tasks that are Cancelled N/A 4.67%

% Scheduled Tasks Complete within 30minutes of Scheduled Time

90% 85.6%

incorporate suggested improvements and resulting KPIs for the scenarios are compared

to targets. Results of the scenario testing are presented to management at each hospital

to support proposed portering improvements.

Chapter 2

Literature Review

This review analyzes previous work in the area of porter improvements. It reviews why

improvements in portering should be studied, how improvements such as adaptation to

new technology, qualitative techniques or optimizing the porter schedule have improved

the porter process and how problems that are similar to portering have been solved.

Since the methodology for this thesis is to develop a generic simulation model, the review

explains how discrete event simulation is used to improve healthcare processes, presents

cases where simulation is applied to improve the porter process, and discusses the benefits

of developing a generic simulation model.

2.1 Root Causes for Patient Flow Delays

Hospitals often feel pressure to remain competitive and reduce patient flow delays [10].

It has been identified that portering is a root cause of these delays [9, 11]. One study

identifying that portering is a root cause for patient flow delays examines the causes

of delays in transfers between the emergency department and the internal wards at the

Rambam Hospital in Israel [12]. In their cause-and-effect diagram, two of the four main

causes of delays relate to portering: miscommunication and process-related methods.

12

Chapter 2. Literature Review 13

Another reason for delays in patient flow is poor capacity management. Capacity

management is a supply and demand problem of determining how the demand of a

system can be satisfied by changing the capacity [13]. The capacity can be changed

through the use of scheduling, optimization and simulation models. Hospitals should

apply this methodology of capacity management to the portering department since it is

desirable to have the minimum number of porters working to satisfy demand.

2.2 Adaptation of New Technology

Improving communication technology within the portering department will greatly affect

patient flow by resulting in faster turnaround times [9]. A portering improvement study

by Dershin et al. was motivated due to a difficulty locating porters in the hospital

and assigning them to a task [14]. Locating equipment in a timely manner has also

proved to be difficult for the portering department. To solve this problem, two-way

communication was introduced in transportation services which improved collection of

performance data, reduced delays caused by incorrect information given to porters and

improved the assignment of porters to task requests based on their current location to

minimize distance travelled. This technology improvement reduced total transportation

time from 25 to 19 minutes and the transports that took over 30 minutes were reduced

from 30% to 8%.

Similar research was done at the University Hospital in Massachusetts which studied

the implementation of two-way radios in the portering department [15]. The implemen-

tation of a voice information system was studied where hospital units would input their

porter request through a touch-tone system. This improvement reduced miscommunica-

tion between dispatcher and transporter, callers were always able to get through to the

dispatcher and the dispatcher had more time to assign porters efficiently to tasks.

In a study of the merger of four hospitals, which now make up the Queen Elizabeth II


Health Sciences Center in Halifax, a pneumatic tube system for transporting specimens

was installed which reduced time spent by porters transporting these specimens and

allowed porters to focus more on patient transports [16]. Technological advancements

such as categorizing porter requests by their priority due to a merger have eased the

transition for portering to the new hospital. These improvements eased the transition

for the portering department to the new hospital.

Installing an automated dispatch system for portering called TeleTracking proved

successful at the Harrogate District Hospital in the UK [17]. To request a porter, units

or departments log onto a computer and enter the task details. Once the task information

is online, the TeleTracking system locates the closest porter and notifies them through

a page of their next task. If the porter is busy, the system queues the task with high

priority. To accept a task, the porter finds a landline and calls in to an automated

system to accept the task. The porter then starts the task and calls in at various points

throughout the task, such as to report on their progress or if they encountered a delay.

Once the porter calls in to complete the task, he or she will either be assigned a new

task, based on location and priority of the waiting tasks, or will be notified that there

are no tasks in the queue. The TeleTracking system allows the units to keep track of

what stage the task is in, which porter is assigned and what time to expect the porter.

The implementation of this technology has seen positive results such as efficiency savings

that will compensate for the initial cost of the system, increased productivity of porters,

reducing the number of FTEs and decreased dispatch and completion times.

2.3 Adaptation of Qualitative Techniques

There are cases where more qualitative approaches are taken to improve portering. Since

these techniques do not involve adapting a new technology or purchasing new software,

they are at a relatively low cost to the hospital. One example of this was at the National


University Hospital in Singapore where portering improved when a Total Quality Man-

agement method was implemented [10]. The FOCUS-PDCA (Focus-Organize-Clarify-

Understand-Select Plan-Do-Check-Act) model is a Total Quality Management method

taken from the manufacturing industry. The model looked at improving a process by

using process charts, Pareto charts and statistical control charts.

Lean is another method adapted from the manufacturing industry that is applied

to healthcare [18, 19, 20]. Adapting lean involves eliminating or reducing waste. Tai-

ichi Ohno, the father of the Toyota Production System, developed seven key areas of

waste, where after eliminating these areas of waste, a process would see an increase in

productivity and efficiency [21]. Another type of waste was added to this list, making

up the eight most common forms of waste: transportation, inventory, motion, waiting,

over processing, over production, defects and skills [22]. An employee of TeleTracking

Technologies applied these eight wastes to patient transport [23]. This study addressed

how portering could be improved by using lean and eliminating or reducing each of these

wastes.

Wastes were observed at St. Paul’s Hospital in Vancouver when expensive resources

such as CTs, MRIs and ORs were blocked due to porter delays. To resolve this issue, with

the insight of management, porters and other hospital staff, ten steps were developed to

create an efficient porter system [24]. One of the key steps that St. Paul’s Hospital

followed in their improvement was to centralize porters which reduced errors in porter

requests and confusion among staff as to which porter to contact for the task.

An additional step in creating an efficient porter system is to improve communication

without any adaptation of new technology. A study at Southampton University Hospital

Trust involved co-participation training to improve communication issues between porters

themselves, and between porters and departments [25]. For example, a porter may think

that one department is responsible for cleaning the bed; however, that department may


think that cleaning the bed is the duty of the porter. This training cleared up some

issues like this between portering and other departments in the hospital.

2.4 Optimization of the Porter Schedule

It is seen in the literature that modifying a current porter schedule often has significant

benefits in the overall efficiency of the process. This section will focus on quantitative

solutions to improve scheduling porters. A study at the Methodist Evangelical Hospital in

Louisville, Kentucky, introduced a central transportation system for patients and material

transportation [26]. Originally at the hospital, nurses and ambulatory staff provided

patient transportation, but this proved to be inefficient and thus, porters were added to

take on this role. Demand charts were developed to note when supply exceeds demand, or

demand exceeds supply so that the schedule could be adjusted accordingly. It was noted

that some departments had enough demand to assign a dedicated porter to handle all

requests in that department. This study relied on paper records and some spreadsheets

to determine porter allocation.

When the Queen Elizabeth II Health Sciences Center was built and was faced with

merging four separate hospitals into one, each of the four original porter systems were

analyzed and it was determined that fewer porters were needed to serve the merged

hospital. With the development of a new porter schedule, it was determined that porters

would be required to complete more duties [16].

In 1999, a microcomputer based heuristic algorithm was developed for scheduling the

monthly roster of porters [27]. The algorithm developed had to take into account labour

constraints, such as satisfying the minimum number of days off per month, allowing a

minimum of 16 hours off between shifts and not allowing porters to work more than

six consecutive days; management constraints, such as always having a total of 40-41

porters on staff each day; and cleansing constraints, such as ensuring that a given number


of porters were assigned to clean twice daily and that this assignment was based on

familiarity and gender. This algorithm schedules porters on a day-by-day basis starting

from the first day of the month and is explained in detail in the paper [27]. The quality of

the schedule created by the algorithm is assessed based on constraint satisfaction (if daily

staffing requirements are met or not) and employee equity (the number of morning shifts,

afternoon shifts, evening shifts, days off, and cleansing duties taken by each employee

during each month).

Another scheduling tool was developed by Kuchera et al. at the Mayo Clinic, Rochester

location, to optimize staffing for their portering department [28]. It was noted that at

the Rochester location, which includes 2 hospitals and other medical buildings, workload

varied across each day and staff levels did not meet this variable demand. The goal of

this research was to ensure that staffing resources were used as efficiently as possible

while not compromising patient safety or quality of service. The scheduling tool was

built using Microsoft Excel and Visual Basic for Applications and has three components.

First, a combination of historical and forecast volumes of patient transports by hour of

day and day of week were determined. Second, a multi-server queuing model was used

to estimate the number of porters needed to satisfy demand by hour of day. Lastly,

using the supply estimates from the queuing model, an optimization model was used to

determine the number of transporters to schedule and the time of day to schedule them.

The optimization model was solved using mixed integer programming where the follow-

ing constraints were added: staff must work four, six or eight hour shifts, enough staff

should be working to meet demand, and only integer numbers of staff can be scheduled.

Results of this study included savings of two FTEs and schedule adjustments of two other

porters, as well as positive feedback from porters.

Once a porter shift schedule is determined, assigning the porters to tasks within these

shifts becomes the next challenge. One scheduling tool developed by Lefevre et al. had

the goal of easing the task of the dispatcher by improving assignment of porters to tasks


based on location of the available porter to the new task [29]. This study assumed that

all porter tasks for a given day are known in advance, along with the equipment needed

for that task. In order to develop a schedule for each porter of all the tasks they need

to complete that day along with where they should obtain the equipment needed for the

task, a local search algorithm was developed. This algorithm was used to update the

amount of equipment in each equipment room using the following equation: equipment

of type e in room p at the beginning of the day- equipment of type e taken out of room p

before time t + equipment of type e brought back to room p before time t. By solving this

equation using local search, feasible schedules were developed. This research concluded

that the transport dispatching problem is difficult due to equipment management.

2.5 Comparison to another Process

The porter process is sometimes compared to the vehicle routing and scheduling problem

or a dial-a-ride problem since there is an origin, destination, capacity and pick-up/drop-off

time. One study applied the methodology of a dial-a-ride problem to intra and inter-

hospital transportation by applying a computer-based planning and scheduling tool called

Opti-TRANS [30]. This tool determines a set of vehicle routes and schedules by solving

a multi-objective linear program. The objective function of the program minimizes total

lateness, total earliness, total driving time and total transport time of patients. This

program is run each time a new porter becomes available and there is often only enough

time to run the program to achieve a good feasible solution.

Another study also noted that the porter process could be compared to the vehicle

routing problem as long as all requests for porters are known before the work day [29].

This would result in the porter knowing all their tasks at the beginning of the day. Since

both of these studies involve knowing the task requests ahead of time, this does not

directly apply to our problem.


The task requests in our problem arrive dynamically, therefore, it resembles a taxi

dispatching problem more than a vehicle routing problem. One study done in Singapore

involves running a multi-agent taxi dispatch system in order to more efficiently dispatch

taxis and hence increase customer satisfaction [31]. The current dispatching system

attempts to match taxis to customers in the same geographic region so that customer

wait time is reduced. The dispatching process for porters could adapt this location

proximity idea in order to reduce the number of empty trips porters make. The research

presented in this thesis is interested in more than just how other methods of dispatching

can be applied to dispatching tasks to porters, but more on how the overall process of

transporting patients and materials could be improved.

2.6 Discrete Event Simulation

Often times, improvements are tested through the use of a simulation model in order to

see the outcomes before implementing the changes in reality. Discrete Event Simulation

(DES) is used in healthcare to study many processes with the goal of improving efficiency

and reducing costs. DES is sometimes chosen over mathematical modeling due to its

benefits in modeling complex patient flows as well as the ability to test “what if” scenarios

[32]. This paper by Jun et al. reviewed articles on the development of simulation applied

to healthcare from 1979 to 1999. They classified the simulation reviews as either related

to patient flow or allocation of resources. The simulations that were reviewed modelled

outpatient clinics, emergency departments, surgical centers, orthopedic departments and

pharmacies.

A study that uses DES to model portering was done at the Vancouver General Hos-

pital by Odegaard et al. [33]. The project’s objective was to analyze and evaluate the

current porter operations and provide recommendations for system improvements. Prob-

lems with the current porter system were identified through shadowing and interviews


and these problems were monitored by developing a simulation model. The simulation

model was built using the program Arena and evaluated improvement scenarios and how

these scenarios affected porter delays. Scenarios that were tested included centralizing

all porters, designating one porter to the OR, using an optimized staff schedule and re-

ducing dispatching times by 30 seconds and porter response times by one minute. The

results of these scenarios were compared based on the percentage of tasks (STAT, ASAP,

Routine and Prescheduled) dispatched within their target times. The most successful

scenario was reducing porter response times and dispatch times. Another key finding of

this research was that small or minor changes can have big improvement impacts.

Another study that uses simulation to model patient transportation services was de-

veloped for intra- and inter-hospital transportations at the Saarland University Hospital

located in southwest Germany [5]. Here, 90% of the transport requests were not known

in advance. The simulation model was developed with the software eM-Plant and tested

different scenarios while using performance metrics to evaluate the system. One of the

findings of using the simulation model was that patient wait times could be decreased

by 20%, on average, with a 10% travel time reduction. This would increase patient

satisfaction and produce cost savings. After this simulation model was developed, a

transportation planning system Opti-TRANS, which was discussed earlier in the liter-

ature review, was developed. Opti-TRANS supports all phases of the transportation

process such as booking a request, dispatching a request to a porter, monitoring the

porter’s progress and reporting on the performance of the porter and transport time of

the request.

This previous work in modelling the patient transportation process through simulation

can be applied to the research presented in this thesis. This research involves analyzing

a current process, creating a simulation model to represent that current process and

testing improvement scenarios just as the research discussed here has [5, 33]. Then,

the simulations developed will be used to create a generic simulation model for hospital


portering, which has not been found in the literature.

2.7 Generic Simulation Models

It is identified that the literature is often overflowed with models that are hospital specific

rather than developing a generic model or adapting a specific model to another hospital

[34]. One reason for this overflow of non-generalized models is that clients feel less

involved when a generalized model is used and therefore tend to want to develop the

model themselves. The clients do not feel as engaged in the model development process

as they would if they build the model from the beginning [35]. It is suggested that

convincing clients to use a generic model should be improved upon and is a key area for

future research.

Generic models are often developed to be reused and the expertise of the original

model developed can be passed on. Some describe a generic model as “transferable or

reused” [36]. One example of this type of model is studied by Sangster et al. where their

objective is to develop a reusable simulation model for diagnostic imaging clinics in Nova

Scotia. The term reusable in this context means that the model can be adapted based

on the specific inputs of each individual clinic using the model [37].

According to Fletcher et al. there are 4 levels of generalized models. Level 1 models

include a broad generic model that is not specific to any one industry. Level 2 models

allow the user to easily adapt the generic model to become a locally specific model.

Level 3 models can be easily adapted from one setting to another simply by adjusting

the input values. Lastly, Level 4 models involve a specific process and the models may

not be reused or transferable to the same process at a different setting [38]. This study

looks further into Level 3 (generic) and Level 4 (specific) models and demonstrates which

aspects are similar and which are different for each. For example, the right design of a

specific model will have a similar scope of the problem compared to a generic model, while


depending on the user for the model, the design will need to be quite different. Also to

note, some implementation factors between generic and specific model are similar, such

as capability of use and demonstrating that the model can be used to solve applicable

issues. According to these levels of generic models, the research presented in this thesis

aims to be classified as a Level 3 generic model.

Chapter 3

Methodology

This section describes the development of a generic patient transportation simulation

model and how this model was adapted to the two hospitals involved in this study. First,

the generic model’s design is discussed, along with the model inputs and model outputs.

Then, this simulation model is applied to the patient transportation department of North

York General Hospital and Juravinski Hospital and Cancer Centre. This thesis includes

descriptions of inputs, assumptions and limitations of the model, and validation of the

model.

3.1 Generic Simulation Model Development

To develop a generic discrete event simulation model, the process maps developed and

data obtained from the hospitals were used. The discrete event simulation program used

was SIMUL8. The process of building the simulation models took quite some time and

included much communication between the research team and the hospitals. The original

model developed lacked many improvements that were eventually included in the final

model. Some of these improvements included: adding a separate simulation model to

represent the porter breaks, adding multiple arrival points, adding more cancellation

23

Chapter 3. Methodology 24

points, adding two porter tasks, adding item specific porters and modifying the queue

prioritization logic. The final models were validated by comparing the outputs of the

simulation to the current performance, as well as validating the model with hospital

personnel involved in the project.

3.1.1 Model Design

The generic simulation model shows the movement of patients and items from receipt

of the task by the dispatching system, to when it is either cancelled or completed. An

outline of the the simulation model can be seen in Figure 3.1, with explanations of the

outline following.

Figure 3.1: Generic Simulation Model Outline

1. Arrivals: scheduled and unscheduled tasks

• Units will call in a porter request or input it themselves into the dispatching

system

• Scheduled tasks are received by the dispatching system a given number of

minutes before their appointment time

• Tasks arrive as either patient or item to be transported

• Further classification of tasks, including priority and the number of porters

needed, is given at the arrival point


2. Dispatch Queue: tasks wait here for an available porter

• Tasks are prioritized in this queue with STAT tasks at the top of the queue,

followed by either Routine tasks or scheduled tasks, depending on the hospital

• If waiting longer than a given time, e.g. 20 minutes, tasks will escalate in

priority

• A certain % of tasks can be cancelled from the dispatch queue

3. Assignment: available porters are assigned to a task

• Porter will receive a page informing them of their next task

• Task with highest priority is assigned first

• Tasks requiring two porters will wait for two porters to become available

• A certain % of tasks can be cancelled after they have been assigned to a porter,

but before the porter reaches the task origin

4. Arrival at origin: porter arrives at the task origin

• Porter will report that s/he has arrived at the origin

• A certain % of tasks can be cancelled after they have reached the task origin

but before they reach the task destination

5. Arrival at destination: porter arrives at the task destination and completes the task

• Porter will report that s/he has completed the task

• Porter is now available to be assigned to a new task

6. Cancellation points: three points where tasks can be cancelled

• 6a-task is cancelled before it is dispatched, this does not affect porters

• 6b-task is cancelled after it has been dispatched but before the porter reports

that they have arrived at the task origin


• 6c-task is cancelled after it has arrived at the origin, but before it arrives at

the task destination

• Since a porter is assigned to scenarios 6b and 6c, a time to cancellation exists

• There is no time to cancellation for scenario 6a since a porter is not assigned

to the cancelled task

3.1.2 Inputs

The inputs for the model are arrival rates, task breakdown, service times, cancellation

rates, time to cancellation, escalating priority, resource schedule and resource break times.

Arrival Rates

Arrival rates are the number of tasks that enter the simulation for each hour of the

day, and each day of the week. Tasks arrive at different rates depending if the task is a

scheduled patient, scheduled item, unscheduled patient or unscheduled item. Arrivals are

separated according to these task types to account for variation. For example, scheduled

tasks do not normally arrive in the overnight hours into the porter queue. Also, scheduled

tasks in the model will arrive a given amount of minutes before their appointment time,

for example, 30 minutes. This is done to ensure that a scheduled task is dispatched close

to their appointment time. The average interarrival rates are the inputs for the model,

however, to account for variation, these arrivals will follow an exponential distribution.

Task Breakdown

Once the tasks have arrived, they are further divided by priority and the number of

porters that are required to complete them. This means that the following percentages

must be determined:


• % scheduled patient one porter vs scheduled patient two porter tasks

• % unscheduled patient one porter vs unscheduled patient two porter tasks

• % unscheduled patient STAT vs Routine tasks

• % unscheduled item STAT vs Routine tasks

Dispatch Logic

The escalating priority is the next input. This input describes an expiration time for

a waiting task where once this time is achieved, the task will increase in priority. This

can depend on task type. For example, if a STAT task has been waiting for 20 minutes

compared to a Routine task waiting 20 minutes, the STAT task would be escalated before

the Routine task due its priority.

Resource Schedules and Breaks

The final input of the model is the resource information. This includes the number of

porters available during each 15 minute interval of the day. It also includes the number

of porters scheduled to be on break. The porter break times are also calculated by using

an average and standard deviation of how long a porter will be on their 15 or 30 minute

break. This resource information was obtained from the porter managers.

Service Times

Service times include the time it takes a porter to complete a task. This time is made

up of the personal response time and the task time. For both of these times, the average

and standard deviation should be determined for the following types of tasks:

• scheduled patient one porter

• scheduled patient two porters


• scheduled item

• unscheduled patient STAT one porter

• unscheduled patient STAT two porter

• unscheduled patient Routine one porter

• unscheduled patient Routine two porters

• unscheduled item STAT one porter

• unscheduled item Routine one porter

Cancellation Rates and Time to Cancellation

Tasks can be cancelled at points along the portering process as identified in Figure 3.1.

Therefore, at each checkpoint, the following cancellation rates must be determined:

• % of scheduled patients

• % scheduled items

• % unscheduled patients

• % unscheduled items

Also, the time to cancellation should be determined. This includes an average and

standard deviation of the time between a checkpoint and its cancellation.

3.1.3 Outputs

Outputs of the generic simulation model are referred to as performance metrics. The

generic simulation model is run for one week with a day of warm up time. After run-

ning the model, details of the completed and cancelled tasks can be easily exported to

Microsoft Excel. From there, analysis on this data is performed to obtain the results of

the following performance metrics:


• Dispatch time (Receipt-Dispatch)

• Transport time (Dispatch-Completion)

• Turnaround time (Receipt-Completion)

• % Scheduled tasks completed within 30 minutes of their appointment time

• % Tasks completed within the target turnaround time

The results of these metrics are reported on by comparing weekdays to weekends, time

of day and task type. Table 3.1 shows the task types that are measured for each of the

performance metrics. The table shows the level of detail that each of the performance

metrics are capable of capturing.


Table 3.1: Task Types Measured for each Performance Metric

Performance Metric Task Type

% Scheduled taskscompleted within 30minutes of appointmenttime

ScheduledScheduled patientScheduled itemScheduled 1 porterScheduled 2 porters

% Tasks completed withintarget turnaround time(unscheduled tasks only)

STATRoutinePatientItem1 porter2 porters

Dispatch time (unscheduledtasks only)

STATRoutinePatientItem1 porter2 porters

Transport time

ScheduledUnscheduled STATUnscheduled RoutineScheduled PatientScheduled ItemUnscheduled PatientUnscheduled ItemScheduled 1 porterScheduled 2 portersUnscheduled 1 porterUnscheduled 2 porters

Turnaround time Unscheduled

3.2 Simulation Model: NYGH

The development of the simulation model representing the current situation at NYGH

will now be discussed.


3.2.1 Data Analysis

Once the porter process was understood, the appropriate data was requested from the

hospitals. NYGH uses the data collection software Crothall where 2014 porter data was

extracted. This data includes all porter tasks that took place in the given time frame with

each task’s corresponding task ID, time task was cancelled or completed, transporter as-

signed, origin unit, destination unit, priority, appointment date/time, receipt date/time,

dispatched date/time, either arrival or in progress date/time, completed date/time, de-

lay reason, cancelled date/time, cancelled reason, and transport item. This extensive

amount of detail provided for each task resulted in much time determining which fields

would be useful.

The data provided came with limitations in accuracy and consistency. One limitation

was that the delay reporting was not always accurate. It was clear from analyzing the

data that not all porters reported delays, as this is an extra step they have to complete,

and porters that do report delays do not always consistently report them. For example,

on one shift a porter may report 10 delays and another shift s/he may report none.

Another limitation of the data is that not all tasks performed were recorded by the

system. Sometimes porters may forget to phone in to report their status until it is

too late to do so. This is evident in the data when times from dispatch to arrival and

arrival to completion are less than a minute, which is unrealistic. Another occurrence of

misleading data is when units request porters to complete a task and this task would not

be officially called in to the dispatcher. The last limitation faced in this study is that

NYGH’s data did not indicate if a task escalated in priority from Routine to STAT. This

would require a field called original priority, which is the priority of the task when it was

received; and a field called final priority, which is the priority of the task when it was

dispatched.


3.2.2 Inputs

NYGH’s simulation model layout is displayed in Figure 3.2.

Figure 3.2: NYGH Porter Simulation Model

Adapting the Generic Model to NYGH

Other than simply changing the inputs to reflect the NYGH data, the generic model

included one small change. This was not using the resource item porter since NYGH did

not have an item specific porter.

Arrival Rates and Task Breakdown

The blue rectangle in Figure 3.2 outlines where the tasks arrive in the simulation. Tasks

can arrive as four different types: scheduled patient, scheduled item, unscheduled patient

and unscheduled item. The arrival rates of these tasks vary between hours of the day and

days of the week. Further characteristics of tasks such as their priority and the number

of porters required to be assigned to the task are indicated at the arrival point. This


breakdown of nine task types can be seen in Table 3.2. It is important to note that item

tasks never involve two porters and scheduled tasks always arrive as Routine, which is

why there is no breakdown based on number of porters involved in item transports and

no priority for scheduled tasks.

Table 3.2: Task Breakdown at NYGH

Task Type NYGH

Scheduled Patient 1 Porter 9.6%

Scheduled Patient 2 Porters 1.6%

Scheduled Item 4.6%

Unscheduled Patient STAT 1 Porter 16.6%

Unscheduled Patient STAT 2 Porters 4.5%

Unscheduled Patient Routine 1 Porter 30.3%

Unscheduled Patient Routine 2 Porters 11.0%

Unscheduled Item STAT 7.4%

Unscheduled Item Routine 14.4%

Dispatch Logic

After arriving and being classified into a task type, tasks will wait in the queue that is

surrounded by a purple border. For all tasks, there is a minimum wait time in the queue

of 30 seconds to account for the dispatcher to manually input the task information and

assign the task to a porter. The order in which tasks are assigned is based on priority

rules. The first rule states that STAT tasks will be given priority over Routine tasks.

The second rule states that a task can escalate in priority depending on how long it has

been waiting in the queue. STAT tasks are escalated almost instantly, scheduled tasks

will escalate in priority after 30 minutes and Routine tasks escalate after 60 minutes.



The resources in the model are represented by the healthcare personnel at the bottom

of Figure 3.2 and are called upon at the activities surrounded by the green border to

process porter tasks. Their availability depends on the shift schedule that was obtained

by the portering manager. From there, the number of porters at work during any given

interval of 15 minutes was determined. Since NYGH does not have item specific porters,

only patient porters are used. The first activity surrounded by the green border requires

one porter to process a task and the second activity requires two porters.

In order to incorporate their breaks, a smaller simulation was built, in the bottom

right-hand corner of Figure 3.2. Whenever a porter’s break should be starting, a work

item will arrive in this smaller simulation and will require a resource to process it at

the activities. Requiring a resource at the break activities has a higher priority than the

activities where resources process porter tasks. This will ensure that resources start their

breaks on time. For example, if a porter becomes available and both a porter task and

a break work item are waiting to be processed, the porter will process the break work

item first. Porters assigned to a 4-hour shift receive one 15-minute break and porters

assigned to a 8-hour shift receive two 30-minute breaks. Porters often take a longer time

on break than allocated in their schedule. By analyzing daily porter transactions, it was

determined that instead of a 15-minute break, porters spend an average of 26 minutes

with a standard deviation of 10.56 minutes on break. For a 30-minute break, porters

spend an average of 36.54 minutes with a standard deviation of 9.89 minutes. Details of

the porter schedule can be seen in Appendix B.

Service Times

The part of the simulation where a resource spends time servicing a task is outlined

by the yellow border in Figure 3.2. This service time includes: personal response time


(dispatch to arrival) and task time (arrival to completion). Personal Response times

for different task types are considered and are modelled using a lognormal distribution

(including mean and standard deviation times as inputs) (Table 3.3). Task times are

only measured for three task types: patient 1 porter tasks, patient 2 porter tasks and

item 1 porter tasks (Table 3.4). Further breakdown by priority and whether the task

is scheduled or unscheduled is not necessary for task time because it was determined

that times were very similar between STAT, scheduled and 1 porter tasks for patient and

item tasks. Once this time has elapsed, tasks and resources move to the activities with

a black border in Figure 3.2. Here, the resource is released and available to be assigned

to another task.

Table 3.3: Personal Response Times at NYGH

Task Types AverageStandardDeviation

Scheduled Patient 1 Porter 4.63 6.98

Scheduled Patient 2 Porters 4.59 6.39

Scheduled Item 4.38 9.40

Unscheduled Patient STAT 1 Porter 4.10 5.37

Unscheduled Patient STAT 2 Porters 4.07 5.18

Unscheduled Patient Routine 1 Porter 4.73 7.58

Unscheduled Patient Routine 2 Porters 4.66 7.75

Unscheduled Item STAT 3.58 5.33

Unscheduled Item Routine 4.78 13.81

Table 3.4: Task Times at NYGH


Patient 1 Porter 22.39 10.01

Patient 2 Porters 22.35 13.42

Item 1 Porter 13.51 10.63



If tasks are not completed by a porter they are cancelled at some point along the way,

as seen in the red borders in Figure 3.2. Tasks can be cancelled either before dispatch,

which is ideal since no porter time is wasted; before arrival, potentially due to a problem

upon arriving at the origin of the task; or before completion, which is very rare since this

means the move is cancelled en-route to or at the destination. Unique cancellation rates

at each cancellation point for all task types are considered and can be seen in Table 3.5.

Time to cancellation is the time a porter is occupied by a task that will eventually

be cancelled. This includes the time from dispatch to cancel or arrival to cancel, which

follow a lognormal distribution. The time from dispatch to cancel has an average of 13.63

minutes and a standard deviation of 23.37 minutes. The time from arrival to cancel has

an average of 9.78 minutes and a standard deviation of 7.77 minutes.

Table 3.5: Cancellation Rates at NYGH

Task TypeCancelbefore

Dispatch

CancelbeforeArrival

Cancelbefore

Complete

Scheduled Patient 6% 1% 7%

Scheduled Item 5% 0.5% 1.5%

Unscheduled Patient STAT 3% 1% 3%

Unscheduled Patient Routine 6% 1% 4%

Unscheduled Item STAT 4% 1% 2%

Unscheduled Item Routine 19% 1% 2%

3.2.3 Assumptions and Limitations

The NYGH simulation model has the following assumptions:

• Porters always start their shift on time


• Porters will not start a new task 15 minutes before the end of their shift; the

simulation allows porters to finish tasks if they are already working on one

• Porters that only do linen tasks and porters that are off-system (do not receive

tasks through dispatching software) are not included in the number of available

resources in the simulation

• Scheduled tasks can be dispatched 30 minutes before their appointment time

• STAT tasks will be dispatched first, then Routine tasks

• All tasks will have a minimum wait time in the dispatch queue of 0.5 minutes to

account for the manual input of the task by the dispatcher

• Tasks escalate in priority based on task type and how long they have been waiting

for an available porter; STAT tasks are immediately escalated, scheduled tasks wait

30 minutes before escalating and Routine tasks wait 60 minutes before escalating

• Scheduled tasks have a priority of Routine

• Two porter tasks are known at receipt of the task; tasks cannot start as a one

porter task and then change to a two porter task

• Two porter tasks arrive in the simulation only between 7:00 and 17:00; there are

too few porters working outside these hours to handle a two porter task. If in

reality a two porter task exists outside these hours, a nurse or other staff member

will help with the task

Some aspects of the current situation at NYGH were not easily captured in the simulation

model. Those limitations are listed here:

• Normally for a two porter task, one porter becomes available first (primary porter)

and the second porter becomes available at a later time (secondary porter); to

simplify this concept in the simulation, the first porter will wait for the second to

become available and then both porters will be assigned to the task at the same

time


• The current simulation model does not take into account where a task originates or

its destination; this limits the simulation from using location as a basis of assigning

porters

• Due to the subjective judgment of the dispatchers at NYGH, the simulation model

will use a dispatching priority algorithm only based on how long a task has been

waiting in the queue and the original priority of the task

3.2.4 Validation

The simulation model was run using data from the current situation of portering at

NYGH. Key Performance Indicators of the current situation were compared with simula-

tion results. These KPIs are: percentage of scheduled tasks on-time (within 30 minutes),

percentage of tasks complete within 35 minutes of being received, dispatch time, trans-

port time and turnaround time. Figures 3.3 to 3.7 show results of KPIs during the

weekdays between 7AM and 5PM. In certain cases, the simulation matches the current

situation, however in other cases, the simulation does not match the current situation

exactly, reasons of which will be explained.

Figure 3.3 shows how long certain types of tasks will wait after being received to

being assigned to a porter, also known as the dispatch time. The simulation tends to

dispatch tasks 1-2 minutes faster than the current situation. This gap in performance is

partly due to the fact that sometimes a dispatcher will not dispatch a task even though

there is an available porter; the dispatcher is just waiting for an available porter close

to the origin of the next task. The simulation model does not have this logic built into

the dispatching algorithm and will therefore take less time to dispatch a task. Another

reason the simulation model dispatches tasks faster is that the model assumes there is a

30 second lag from receiving the task to dispatching the task, where in reality this time

could be longer depending how busy the dispatcher is at the time.


Figure 3.3: Validation of Dispatch Time

The differences between current situation transport times and simulation results are

very minimal, as seen in Figure 3.4. Sometimes the current situation has a faster trans-

port time and sometimes the simulation does, which is due to the variation in their

standard deviations.

Figure 3.4: Validation of Transport Time

Figure 3.5 shows that the turnaround time of the simulation is about two minutes

faster than the current situation. This graph is a combination of the previous two graphs;


adding dispatch time and transport time makes up turnaround time. Since the simulation

dispatch times were 1-2 minutes faster than the current situation and the transport times

were relatively even across the simulation and current situation, it makes sense that the

overall turnaround time has a similar difference between the simulation and current

compared to the dispatch times.

Figure 3.5: Validation of Turnaround Time

In Figure 3.6, the simulation completes 5-10% more tasks on time (within 30 minutes

of their appointment time) than does the current situation at NYGH. A reason the

current situation performs poorly compared to the simulation is that sometimes in reality

dispatchers forget about a scheduled task until a few minutes before its appointment time,

which makes it hard to complete the task within 30 minutes of the appointment. The

dispatching algorithm in the simulation will not forget about the scheduled tasks.

The percentage of tasks complete within 35 minutes is measured in Figure 3.7. This

35 minutes starts from the time a task is received to the time the porter completes the

task. The simulation completes around 10% more tasks than the current situation. The

reason for this is attributed to the difference in dispatch time between simulation and

current situation. Once assigned to a porter, tasks are not being completed any faster

by the simulation since their transport time is an input, therefore, the difference is due

to the dispatch time.


Figure 3.6: Validation of % Scheduled Tasks Completed by Appointment Time

Figure 3.7: Validation of % Unscheduled Tasks Completed within 35 minutes

These graphs were shared with hospital personnel at NYGH to ensure they were

satisfied with how the simulation represented the current situation. Since it was con-

cluded that the simulation developed accurately represents the current situation, results

of improvement scenarios will be compared to the base case simulation model, not to the

current situation at NYGH.


3.3 Simulation Model: JHCC

3.3.1 Data Analysis

After mapping out the porter process at JHCC, the appropriate data was requested.

JHCC has data collection software, Connexall, from which data from July 2013 to De-

cember 2013 was extracted. This data includes all porter tasks that took place in the

given time frame with details, similar to NYGH’s data, on each task. As with any ini-

tial raw data set, it came with inaccuracies. Delay reporting was not always completed

when a delay actually occurred, not all tasks completed by porters were recorded and

forgetfulness of porters to report on their progress all lead to inaccuracies in the data.

3.3.2 Inputs

JHCC’s simulation model layout is displayed in Figure 3.8.

Figure 3.8: JHCC Porter Simulation Model


Adapting the Generic Model to JHCC

The generic model did not need to go through any changes, other than changing the

input values to reflect the JHCC data, in order to be adapted to JHCC.

Arrival Rates and Task Breakdown

In JHCC’s simulation model, the blue border in Figure 3.8 indicates where tasks arrive.

Tasks can arrive in one of four ways: scheduled patient, scheduled item, unscheduled

patient and unscheduled item. Table 3.6 shows the breakdown of the nine task types at

JHCC. At their arrival, tasks are given a priority and the number of porters required,

based on the task breakdown. At JHCC, tasks are given an original priority level of 0

or 2-8, where levels 0 and 2 represent a higher priority task (STAT task) and levels 3-8

represent a lower priority task (Routine task). Even though tasks cannot originate as a

priority level 1, they can escalate from a level 2 to a level 1. The most common task

types of each priority are shown in Table 3.7.

Table 3.6: Task Breakdown at JHCC

Task Type JHCC

Scheduled Patient 1 Porter 11.43%

Scheduled Patient 2 Porters 0.31%

Scheduled Item 1.91%

Unscheduled Patient STAT 1 Porter 7.81%

Unscheduled Patient STAT 2 Porters 1.12%

Unscheduled Patient Routine 1 Porter 28.78%

Unscheduled Patient Routine 2 Porters 1.55%

Unscheduled Item STAT 0.04%

Unscheduled Item Routine 47.04%


Table 3.7: Priority Level Analysis

PriorityLevel

Most CommonTask Types

PercentageSecond Most

Common TaskTypes

Percentage

0 PACU 48% Emergency 25%2 Express Unit 30% Ambulatory Unit 11%3 Transfusion Medicine 21% X-Ray 11%4 Transfusion Medicine 10% Lab Core 10%5 Medicine Nurse 14% Surgical Nurse 11%6 Patient Registration 13% Scheduled Run 8%7 Scheduled Run 19% Endocrinology 8%8 Patient Registration 24% ICU Nurse 5%

Dispatch Logic

Surrounded by a purple border in Figure 3.8 is the dispatch queue. The task with the

highest priority when a porter is available will be dispatched. This priority is calculated

based on two rules. The first rule states that STAT tasks will be given priority over

Routine tasks. The second rule states that a task can escalate in priority depending on

how long it has been waiting in the queue. STAT tasks can originate as priority 0 or 2

and Routine tasks can originate as priority 3-8 with priorities 7 and 8 always being item

tasks, and priorities 3-6 primarily being patient tasks. STAT tasks escalate in priority

after waiting 14 minutes, scheduled tasks escalate after 22 minutes and Routine tasks

escalate after 30 minutes.


Once a porter becomes available, s/he will be assigned to a task at points in the simulation

surrounded by a green border. JHCC has patient porters and item porters. Patient

porters can be assigned to both patient and item tasks, while item porters can only be

assigned to item tasks.

To incorporate porter breaks, a smaller simulation was built, in the bottom right-hand


corner of Figure 3.8. Porters assigned to a 4-hour shift receive one 15-minute break and

porters assigned to an 8-hour shift receive two 15-minute breaks and one 30-minute break.

Porters often take a longer time on break than allocated in their schedule. Instead of a

15-minute break, porters spend an average of 17.98 minutes with a standard deviation of

5.29 minutes on break. For a 30-minute break, porters spend an average of 32.80 minutes

with a standard deviation of 7.28 minutes. Details of the porter schedule can be seen in

Appendix B.

Service Times

Once the task has the correct number of resources assigned to it, it will move to the

points in the simulation surrounded by a yellow border in Figure 3.8 and the time each

task spends at each point with the assigned porter is known as the task’s service time.

This service time includes: arrival minutes (dispatch to in progress) and patient minutes

(in progress to completion). The arrival minutes for different task types are considered

and are modelled using a lognormal distribution (including mean and standard deviation

times as inputs) (Table 3.8). Patient minutes are only measured for three task types:

patient 1 porter tasks, patient 2 porter tasks and item 1 porter tasks (Table 3.9). Further

breakdown by priority and whether the task is scheduled or unscheduled is not necessary

for task time because it was determined that times were very similar between STAT,

scheduled and 1 porter tasks for patient and item tasks. Once this time has elapsed,

tasks and resources move to the next set of activities, outlined by the black border. The

resource is now released and available to be assigned to another task.


Table 3.8: Arrival Minutes at JHCC


Scheduled Patient 1 Porter 21.18 8.60

Scheduled Patient 2 Porters 26.57 10.07

Scheduled Item 15.14 14.32

Unscheduled Patient STAT 1 Porter 18.47 10.21

Unscheduled Patient STAT 2 Porters 20.73 9.89

Unscheduled Patient Routine 1 Porter 17.87 12.69

Unscheduled Patient Routine 2 Porters 24.39 9.89

Unscheduled Item STAT 9.40 9.47

Unscheduled Item Routine 13.32 13.25

Table 3.9: Patient Minutes at JHCC


Patient 1 Porter 7.80 8.38

Patient 2 Porters 7.74 7.76

Item 1 Porter 4.25 7.56


The simulation points that are outlined by a red border in Figure 3.8 identify the three

cancellation points: before dispatch, before task is in progress, and before completion.

Unique cancellation rates at each cancellation point for all task types are considered and

can be seen in Table 3.10. Time to cancellation is the time from dispatch to cancel or in

progress to cancel; these times follow a lognormal distribution. Time from dispatch to

cancel has an average of 15.41 minutes and standard deviation of 19.55 minutes. Time

from in progress to cancel has an average of 6.38 minutes and a standard deviation of

8.19 minutes.


Table 3.10: Cancellation Rates at JHCC

Task TypeCancelbefore

Dispatch

CancelbeforeArrival

Cancelbefore

Complete

Scheduled Patient 1.5% 8.8% 0.7%

Scheduled Item 0.2% 1.2% 0.2%

Unscheduled Patient STAT 1.7% 4.4% 0.2%

Unscheduled Patient Routine 2.2% 3.2% 0.1%

Unscheduled Item STAT 4.3% 9.1% 0%

Unscheduled Item Routine 1% 1.3% 0.1%

3.3.3 Assumptions and Limitations

The simulation model has the following assumptions:

• Porters always start their shift on time

• Porters will not start a new task 15 minutes before the end of their shift; simulation

allows porters to finish tasks if they are already working on one

• Porters that only do linen tasks and porters that are off-system (do not receive

tasks through dispatching software) are not included in the number of available

resources of the simulation

• Item porters are included as a separate resource and can only complete item tasks

• Patient porters complete both item tasks and patient tasks

• Scheduled tasks can be dispatched 30 minutes before their appointment time

• STAT tasks include tasks with a priority of 0, 1 or 2 and Routine tasks have a

priority of 3-8

• All tasks are dispatched first come first served within priority class

• All tasks will have a minimum wait time in the dispatch queue of 0.1 minutes to

account for a lag in the dispatching software


• Tasks escalate in priority based on task type and how long they have been waiting

for an available porter; STAT tasks wait 14 minutes before escalating, scheduled

tasks wait 22 minutes before escalating, Routine tasks wait 30 minutes before es-

calating

• Two porter tasks are known at receipt of the task; tasks cannot start as a one

porter task and then change to a two porter task

• Two porter tasks arrive in the simulation only between 7:00 and 20:00; there are

too few porters working outside these hours to handle a two porter task. If in

reality a two porter task exists outside these hours, a nurse or other staff member

will help with the task

Some aspects of the current situation were not easily captured in the simulation model.

Those limitations are listed here:

• Normally for a two porter task, one porter becomes available first (primary porter)

and the second porter becomes available at a later time (secondary porter); to

simplify this concept in the simulation, the first porter will wait for the second to

become available and then both porters will be assigned at the same time

• The current simulation model does not take into account where a task originates or

its destination; this limits the simulation from using location as a basis of assigning

porters

• The automated dispatch system at JHCC described in section 1.1.2 is highly com-

plex including multiple priority levels; the dispatching algorithm in the simulation

model only uses two priority levels

3.3.4 Validation

Key Performance Indicators defined by JHCC were used to validate the simulation model

against the current porter situation. These KPIs are: percentage of prebooked tasks


on time (within 15 minutes), percentage of tasks complete within 30 minutes of being

received, dispatch time, trip time and transaction time. Figures 3.9 to 3.12 show KPI

results for weekdays between the hours of 7AM and 5PM and the graphs show that in

certain cases, the simulation matches the current situation very well, however in other

cases, the simulation may not match the current situation, the reasons for which will be

explained.

Figure 3.9 shows the time a task spends waiting for an available porter during the

weekdays and between 7AM-5PM. Tasks will spend more time, around one minute longer,

waiting for an available porter in the simulation compared to the current situation.

Figure 3.9: Validation of Dispatch Time

Figure 3.10 shows the trip time of the current situation and the simulation model

results. The current situation almost always matches the simulation. This makes sense

since trip time is an input. Sometimes the current situation has a faster trip time and

sometimes the simulation does, which is due to the variation in their standard deviations.

Figure 3.11 shows the transaction time comparison between the current situation and

the simulation result. Tasks in the simulation take about one minute longer on average

than the current situation. This transaction time combines dispatch time and trip time.

Since the simulation dispatch times were one minute slower than the current situation and


Figure 3.10: Validation of Trip Time

the trip times were relatively even across the simulation and current situation, it makes

sense that the overall transaction time has a similar difference between the simulation

and current to these dispatch times.

Figure 3.11: Validation of Transaction Time

In Figure 3.12, it is seen that there is minimal difference between the % prebooked

tasks completed on time resulting from the simulation and the current performance at

JHCC. The only task type where there is a significant difference between current and


simulation is the scheduled 2 porter tasks. By looking at the graph, the simulation

completes about 15% less tasks on time compared to the current situation. This is

because in the current situation, 2 porter tasks are always assigned as STAT, whereas in

the simulation, all scheduled tasks, both 1 and 2 porter tasks, have a priority of Routine.

Therefore, it will take longer for a scheduled 2 porter task to be escalated in priority in

the simulation and therefore, less likely to be completed on time.

Figure 3.12: Validation of % Prebooked Tasks On Time

The percentage of tasks complete within 30 minutes is measured in Figure 3.13. This

30 minutes includes the time from when a task is received to the time the porter completes

that task. The results vary between the current situation completing more tasks within

the target time and the simulation completing more tasks.

These graphs and other results of comparing the current situation with the simulation

performance were shared with hospital personnel at JHCC to ensure they were satisfied

with how the simulation represented the current situation. Since it was concluded that the

simulation developed accurately represents the current situation, results of improvement

scenarios will be compared to the base case simulation model, not to the current situation

at JHCC.


Figure 3.13: Validation of % Tasks Completed within 30 minutes

Chapter 4

Scenario Testing

The scenarios described in the following sections were developed in collaboration with

the hospitals.

4.1 Improvement Scenarios: NYGH

Scenario 1: Adding Weekday Shifts

This improvement scenario simulates how porter performance metrics are affected if a

new porter were hired during a weekday shift. Additions of 8-hour and 4-hour shifts

are simulated. The times of these additional shifts were determined by looking at the

demand of porter tasks entering the system at each hour of the day and comparing if the

supply of porters working during each hour was able to meet this demand. A supply-

demand graph shows these differences for an average Thursday (Figure 4.1). The times

throughout the day where demand most exceeds supply are where additional porter shifts

are added. These shifts are identified in Table 4.1.

53

Chapter 4. Scenario Testing 54

Figure 4.1: Supply-Demand Graph for an Average Thursday

Table 4.1: Adding Weekday Shifts

Scenario Details Shift Time Break Times

1AAdd an 8-hour

shift8:00-16:00 10:30-11:00/ 14:00-14:30

1BAdd a 4-hour

shift10:30-14:30 12:00-12:15

1CAdd two 8-hour

shifts8:00-16:00 and

10:00-18:00

10:30-11:00/ 14:00-14:30and 11:30-12:00/

15:30-16:00

1DAdd two 4-hour


13:30-17:3012:00-12:15 and 15:15-15:30

1EAdd an 8-hour

and 4-hour shift8:00-16:00 and

10:30-14:3010:30-11:00/ 14:00-14:30

and 12:00-12:15

Scenario 2: Adding Weekend Shifts


new porter were hired during a weekend shift. Additions of 8-hour and 4-hour shifts are

simulated. The times of these additional shifts were determined using a similar method

to Scenario 1 (Figure 4.2) and can be seen in Table 4.2.


Figure 4.2: Supply-Demand Graph for an Average Saturday

Table 4.2: Adding Weekend Shifts


2AAdd an 8-hour

shift8:00-16:00 10:30-11:00/ 13:30-14:00

2BAdd a 4-hour

shift9:00-13:00 11:30-11:45

2CAdd an 8-hour


9:00-13:0010:30-11:00/ 13:30-14:00

and 11:30-11:45

Scenario 3: Altering Weekday and Weekend Schedules

This improvement scenario simulates how porter performance metrics would be affected if

current porter shifts were altered to better meet demand. To do this, the following metrics

were used to assess the base case scenario’s performance by hour of day: % Scheduled

tasks on time, % STAT and Routine Tasks completed within 35 minutes, Dispatch time

of STAT and Routine tasks and Turnaround time of all unscheduled tasks. The averages

for each of these metrics were determined between the hours of 7:00 and 20:00. Then,

it was determined which hours performed below these averages. The hours that were

below average forthe % completed on time, shown in Figures 4.3 and 4.4, were consistent

across all metrics. These hours are between 11:00-13:00 and 17:00-19:00 on the weekdays

and 10:00-12:00 and 16:00-18:00 on the weekends. Since these time frames are around


lunch or dinner, the poor performance is due to too many porters assigned a break at

this time. Based on these hours, current schedules were shifted to add porters to these

poor performing hours. The changes resulting from this analysis can be seen in Tables

4.3 and 4.4.

Figure 4.3: Determining Poor Performing Weekday Hours

Figure 4.4: Determining Poor Performing Weekend Hours

Scenario 4: Improving Personal Response Time

This improvement scenario simulates how porter performance metrics would be affected

if personal response time was reduced. This reduction of personal response time is used


Table 4.3: Changing Weekday Shifts

ScenarioOldShift

OldBreak 1

OldBreak 2

NewShift

NewBreak 1

NewBreak 2

3A12:00-16:00

14:00-14:15

10:00-14:00

12:00-12:15

3B13:00-17:00

15:00-15:15

17:00-21:00

19:00-19:15

Table 4.4: Changing Weekend Shifts

ScenarioOldShift

OldBreak 1

OldBreak 2

NewShift

NewBreak 1

NewBreak 2

3C12:00-20:00

14:30-15:00

18:00-18:30

11:00-19:00

13:30-14:00

16:30-17:00

3D11:00-15:00

13:00-13:15

10:00-14:00

11:45-12:00

to simulate the use of iPads or other tablet devices which have the potential to speed

up the dispatch process. Using these devices would enable the porters to report on

their progress without having to spend time searching for a landline. This scenario also

simulates if reducing elevator delays and time searching for equipment has the potential

to improve porter performance.

The personal response time is represented using a lognormal distribution. Currently,

NYGH has an average personal response time of 4.48 minutes. For this scenario, this

time was reduced by 1, 2 and 3 minutes and the scenarios were labelled scenario 4A, 4B,

and 4C, respectively. The standard deviation of the base case personal response time was

reduced proportionally to match the lognormal distribution of the base case scenario.

Scenario 5: Improving Task Time


task time was reduced. This reduction of task time is used to simulate how improvements

in patient readiness, elevator delays and other delays would affect porter performance.


The current average task time at NYGH is 20.12 minutes. Similarly to Scenario 4, this

time was reduced by 1, 2 and 3 minutes to represent scenarios 5A, 5B, and 5C.

Scenario 6: Reclassifying Two Porter Tasks


50% of two porter tasks were reclassified. This reduction is used to simulate if the hospital

purchased new equipment where two porters are no longer needed. This scenario also

simulates if room to room transfers only required one porter rather than two. In the base

case scenario, 19% of tasks involve two porters. This number will be reduced to 9.5% in

this scenario and the number of one porter tasks will increase to 90.5%.

Scenario 7: Reclassifying STAT Tasks

This improvement scenario simulates how porter performance metrics are affected if 50%

of STAT tasks were reclassified. This reduction is used to simulate the use of better

classification and usage of a STAT task by hospital departments. Currently, over 90%

of tasks originating at or destined to Angiography, Endocrinology and the Operating

Room are classified as STAT. Tasks in these departments may not be theoretical STAT

tasks, but are classified as STAT because these departments know that the task will get

completed faster that way. In the base case scenario, 34% of tasks are STAT tasks. This

number will be reduced to 17% in this scenario and the number of Routine tasks will

increase to 83%.

Scenario 8: Combining other improvement scenarios

Scenario 8 makes up seven scenarios where previously mentioned improvements will be

combined to achieve results closer NYGH’s targets (Table 4.5).


Table 4.5: Scenario 8 Improvements

ScenarioScenariosCombined

Weekday/WeekendAffected

8A 3A, 3B, 4C, 5A & 6 Weekday

8B 3C, 3D, 4C, 5A & 6 Weekend

8C 3A, 3B & 4C Weekday

8D 3C, 3D & 4C Weekend

8E 4C & 5A Both

8F 4C & 6 Both

8G 4C, 5A & 6 Both

4.2 Improvement Scenarios: JHCC

Scenario 1: Adding Weekday Shifts


new porter is hired during a weekday shift. Additions of 8-hour and 4-hour shifts are

simulated. The times of these additional shifts were determined by matching supply

and demand. A supply-demand graph shows these differences for an average Thursday

(Figure 4.5). The times throughout the day where demand most exceeds supply are

where additional porter shifts are added. These shifts are identified in Table 4.6.

Figure 4.5: Supply-Demand Graph for an Average Thursday


Table 4.6: Adding Weekday Shifts


1AAdd an 8-hour

shift7:00-15:00

9:00-9:15 / 11:15-11:45 /13:45-14:00

1BAdd two 8-hour


7:30-15:30

9:00-9:15 / 11:15-11:45 /13:45-14:00 and 9:30-9:45 /11:45-12:15 / 14:15-14:30

1CAdd a 4-hour

shift7:00-11:00 9:00-9:15

1DAdd two 4-hour


12:00-16:009:00-9:15 and 14:00-14:15

1EAdd an 8-hour


7:00-11:009:00-9:15 / 11:15-11:45 /13:45-14:00 and 9:00-9:15

Scenario 2: Adding Weekend Shifts


new porter were hired during a weekend shift. Additions of both 8-hour and 4-hour

shifts are simulated. The times of these additional shifts were determined using a similar

method to Scenario 1 (Figure 4.6) and can be seen in Table 4.7.

Figure 4.6: Supply-Demand Graph for an Average Saturday


Table 4.7: Adding Weekend Shifts


2AAdd an 8-hour

shift7:00-15:00

9:00-9:15 / 11:30-12:00 /13:45-14:00

2BAdd a 4-hour

shift10:00-14:00 12:00-12:15

2CAdd an 8-hour


10:00-14:009:00-9:15 / 11:30-12:00 /

13:45-14:00 and 12:00-12:15

Scenario 3: Altering Weekday and Weekend Schedules


if current porter shifts were altered to better meet demand. To do this, the following

metrics were used to assess the base case scenario’s performance by hour of day: %

Prebooked tasks on time, % STAT and Routine tasks completed within 40 minutes,

Dispatch time of STAT and Routine tasks and Transaction time of all demand tasks.

The averages for each of these metrics were determined between the hours of 7:00 and

20:00. Then, it was determined which hours performed below these averages. The hours

that were below average forthe % completed within 40 minutes, shown in Figures 4.7

and 4.8, were consistent across all metrics. These hours are between 7:00-17:00 on the

weekdays and 8:00-16:00 on the weekends. Based on these hours, current schedules were

shifted to add porters to these poorly performing hours. The new shift and break times

resulting from this analysis can be seen in Tables 4.8 and 4.9.

Table 4.8: Changing Weekday Shifts

ScenarioOldShift

OldBreak

1

OldBreak

2

OldBreak

3

NewShift

NewBreak

1

NewBreak

2

NewBreak

3

3A15:00-19:00

17:00-17:15

7:00-11:00

9:00-9:15

3B16:00-00:00

17:30-17:45

20:00-20:30

21:45-22:00

7:30-15:30

9:15-9:30

11:30-12:00

14:00-14:15


Table 4.9: Changing Weekend Shifts

ScenarioOldShift

OldBreak

1

OldBreak

2

OldBreak

3

NewShift

NewBreak

1

NewBreak

2

NewBreak

3

3C15:00-23:00

17:15-17:30

18:30-19:00

21:30-21:45

8:00-16:00

10:15-10:30

11:30-12:00

14:00-14:15

3D12:00-20:00

14:45-15:00

17:15-17:45

18:30-18:45

10:00-18:00

12:45-13:00

15:15-15:45

16:30-16:45

Figure 4.7: Determining Poor Performing Weekday Hours

Figure 4.8: Determining Poor Performing Weekend Hours

Scenario 4: Improving Arrival Minutes


if arrival minutes were reduced. Arrival minutes include the time from dispatch to in


progress. This reduction of arrival minutes is used to simulate how improvements in

patient readiness, elevator delays and time searching for equipment would affect porter

performance.

To adjust the base case to model this scenario, the average of the base case arrival

minutes (16.14 minutes) was reduced by 1, 2 and 3 minutes and the scenarios were

labelled scenario 4A, 4B, and 4C, respectively. The standard deviation of the base case

arrival minutes was reduced proportionally to match the lognormal distribution of the

base case scenario.

Scenario 5: Improving Patient Minutes


if patient minutes were reduced. This reduction of patient minutes is used to simulate

if upper floors were used to transport patients rather than going down to the basement.

The current average patient minutes at JHCC is 6.05 minutes. Similarly to Scenario 4,

this time was reduced by 1, 2 and 3 minutes to represent scenarios 5A, 5B, and 5C.

Scenario 6: Combining other improvement scenarios

Scenario 6 makes up three scenarios where previously mentioned improvements will be

combined to achieve results closer JHCC’s targets (Table 4.10).

Table 4.10: Scenario 6 Improvements

ScenarioScenariosCombined

Weekday/WeekendAffected

6A 3A, 3B & 4C Weekday

6B 3C, 3D & 4C Weekend

6C 4C & 5A Both


4.3 Sensitivity Analysis: NYGH

The scenarios described in Table 4.11 are tested using the simulation model to determine

how the current porter system could handle increases in personal response time, in task

time, in demand of certain task types, or even in demand overall. To adjust the base

case to each of the scenarios here, similar methods in the Improvement Scenarios section

of this report were used. Scenario 9 involves increasing and decreasing overall demand.

To do this, demand rates for each day of the week were calculated and they were either

increased or decreased by 10%. The difference in demand between the base case and

either this increase or decrease was calculated. The increased demand was added across

various hours between 7:00 and 23:00 and the decreased demand was removed from

various hours between 7:00 and 23:00.

Table 4.11: Sensitivity Analysis Scenarios: NYGH

Scenario FactorIncrease orDecrease

Amount

4DPersonal Response

TimeIncrease 1 minute

5D Task Time Increase 1 minute6B # Two porter tasks Increase 50%7B # STAT Tasks Increase 50%9A Demand Increase 10%9B Demand Decrease 10%


4.4 Sensitivity Analysis: JHCC

Sensitivity testing was also done on the JHCC simulation model. These include increasing

arrival minutes, patient minutes, increasing demand and decreasing demand. These

scenarios are described in Table 4.12.

Table 4.12: Sensitivity Analysis Scenarios: JHCC

Scenario FactorIncrease orDecrease

Amount

4D Arrival Minutes Increase 1 minute5D Patient Minutes Increase 1 minute7A Demand Increase 10%7B Demand Decrease 10%

Chapter 5

Results

This chapter describes the results of scenario testing at the two hospitals involved in this

research: North York General Hospital and Juravinski Hospital and Cancer Center.

5.1 Simulation Results: NYGH

Once the generic simulation model included all the NYGH inputs from historical data and

this simulation was validated according to the current performance at NYGH, scenario

testing began. Table 5.1 summarizes NYGH’s scenarios. The simulation model was run

10 times for each scenario tested to account for variation in arrival rates, service times,

cancellation rates and break times. The simulation was also run for 10 trials because this

was an appropriate sample size to calculate a 90% confidence interval. This confidence

interval was calculated to ensure that 90% of the time, the mean is within a certain

range (see Table 5.2 for these ranges). Confidence intervals for weekend results can be

seen in Appendix C. In order to reduce these intervals, more trials are required. Results

by hour for an average weekday and weekend were collected for all five KPIs and for

many different task types. In this report, the results of the following task types for the

66

Chapter 5. Results 67

five KPIs will be discussed (Table 5.3).

Table 5.1: NYGH Scenario Legend

Scenario Description1A Adding one weekday 8-hour shift1B Adding two weekday 8-hour shifts1C Adding one weekday 4-hour shift1D Adding two weekday 4-hour shifts1E Adding one weekday 8-hour and one 4-hour shift2A Adding one weekend 8-hour shift2B Adding one weekend 4-hour shift2C Adding one weekend 8-hour and one 4-hour shift3A Change weekday 4-hour morning shift3B Change weekday 4-hour afternoon shift3C Change weekend 8-hour afternoon shift3D Change weekend 4-hour afternoon shift4A Reduce Personal Response time by 1 minute4B Reduce Personal Response time by 2 minutes4C Reduce Personal Response time by 3 minutes4D Increase Personal Response time by 1 minute5A Reduce Task time by 1 minute5B Reduce Task time by 2 minutes5C Reduce Task time by 3 minutes5D Increase Task time by 1 minute6 Reclassify 50% of two porter tasks

6B Increase # two porter tasks by 50%7 Reclassify 50% of STAT tasks

7B Increase # STAT tasks by 50%8A Combination of 3A, 3B, 4C, 5A & 68B Combination of 3C, 3D, 4C, 5A & 68C Combination of 3A, 3B & 4C8D Combination of 3C, 3D & 4C8E Combination of 4C& 5A8F Combination of 4C & 68G Combination of 4C, 5A & 69A Increase Demand by 10%9B Decrease Demand by 10%

Results in Figures 5.1 and 5.2 display the turnaround times of all scenarios tested

compared to the base case during the weekday and weekend (refer to Appendix C for

the details). These figures show that all scenarios tested improve on the base case result.


Table 5.2: NYGH Confidence Intervals for Weekday Results

Scenario Mean90% Confidence

IntervalBase Case 39.57 1.08

1A 34.47 0.931B 33.64 1.721C 37.90 1.741D 37.19 1.921E 32.02 1.573A 37.61 1.883B 38.49 1.764A 36.25 1.124B 35.37 1.494C 32.40 1.655A 38.19 0.915B 37.86 1.155C 34.25 2.096 35.19 1.617 38.47 1.55

8A 23.38 1.228C 32.20 2.368E 27.16 1.298F 26.17 0.558G 24.34 1.04

This means that implementing any of the suggested scenarios will result in an improved

turnaround time, and several of the scenarios even result in a turnaround time that

achieves the target of 35 minutes. Again, refer to Table 5.2 for ranges of these results.

From Figure 5.1, it is seen that Scenarios 8A, 8C, 8E, 8F and 8G meet the target

turnaround time of 35 minutes and also have a shorter turnaround time than adding a

full time and part time porter. Often times, adding a resource is seen as an easy solution,

but this analysis shows that there exist other alternatives to this. Similar results can be

seen for the weekend (Figure 5.2).

Figures 5.3 and 5.4 show results of sensitivity analysis by comparing average turnaround

times of each scenario. The gap between performance and target is increasing for the


Table 5.3: KPIs resulting from Simulation

KPI Task Type% Scheduled tasks on-time Scheduled% Tasks complete within 35 minutes of receipt STAT, RoutineDispatch time STAT, RoutineTransport time Scheduled, STAT, RoutineTurnaround time Unscheduled

Figure 5.1: Weekday Turnaround Time: Evaluating Improvement Scenarios

scenarios tested here.

In summary, the results show that during the weekday, Scenario 8A performs the best

and during the weekend, Scenario 8B performs the best. This is because these scenarios

combine multiple improvement scenarios together to ensure that the target turnaround

time of 35 minutes is achieved. The combined improvements include reducing personal

response time, reducing task time, reclassifying two porter tasks, and altering existing

schedules. By implementing these scenarios, a weekday turnaround time of 23.38 minutes

and weekend turnaround time of 29.96 minutes will result.

To reduce personal response time by three minutes (Scenario 4C), NYGH will need to

implement projects or gain support from management to eliminate pager use by switching


Figure 5.2: Weekend Turnaround Time: Evaluating Improvement Scenarios

Figure 5.3: Weekday Turnaround Time: Sensitivity Analysis

Figure 5.4: Weekend Turnaround Time: Sensitivity Analysis

to a tablet or smart phone system, revise policy on targets for personal response times,

and pre-assign porters for the next job prior to completing the current task.


To reduce task time by one minute (Scenario 5A), NYGH will need to: change policies

in order to reduce unit, patient or elevator delays, better manage the productivity of

porters, and improve access to equipment.

To reclassify the number of two porter tasks (Scenario 6), unit staff at NYGH will

need to be utilized more for room to room transfers, or the policy on when two porters

are needed for a transport will need to be revised.

Finally, in order to alter existing schedules both during the weekdays (Scenario 3A

& 3B) and weekends (Scenario 3C & 3D), discussions must be had with the Union to

determine if changes to the collective agreement need to be made.


5.2 Simulation Results: JHCC

The JHCC simulation model was also run 10 times for each scenario tested to account for

variation in arrival rates, service times, cancellation rates and break times. Similarly to

NYGH, the simulation was also run for 10 trials because this was an appropriate sample

size to calculate a 90% confidence interval. This confidence interval was calculated to

ensure that 90% of the time, the mean is within a certain range (see Table 5.5 for these

ranges). Confidence intervals for weekend results can be seen in Appendix C. Table 5.4

summarizes JHCC’s scenarios. Results by hour for an average weekday or weekend were

collected for all six KPIs and for many different task types. In this report, the results of

the following task types for the five KPIs will be discussed (Table 5.6).

Results in Figures 5.5 and 5.6 display the turnaround times of all scenarios tested

compared to the base case during the weekday and weekend (refer to Appendix C for

the details). These figures show that all scenarios tested improve on the base case result.

This means that implementing any of the suggested scenarios will result in an improved

turnaround time, and several of the scenarios even result in a turnaround time that

achieves the target of 30 minutes. Again, refer to Table 5.5 for ranges of these results.

Figure 5.5 shows that Scenarios 4C, 5B, 5C, 6A and 6C meet the target turnaround

time of 30 minutes and also have a shorter turnaround time than adding a full time and

part time porter. JHCC was very excited about this result and they were glad to have

this evidence to support a future decision regarding staffing. Similar results can be seen

for the weekend (Figure 5.6).

Figures 5.7 and 5.8 show results of sensitivity analysis by comparing average turnaround

times of each scenario. The gap between performance and target is increasing for the

scenarios tested here.

In summary, the results show that during the weekday, Scenario 6A performs the best


Table 5.4: JHCC Scenario Legend

Scenario Description1A Adding one weekday 8-hour shift1B Adding two weekday 8-hour shifts1C Adding one weekday 4-hour shift1D Adding two weekday 4-hour shifts1E Adding one weekday 8-hour and one 4-hour shift2A Adding one weekend 8-hour shift2B Adding one weekend 4-hour shift2C Adding one weekend 8-hour and one 4-hour shift3A Change weekday 4-hour morning shift3B Change weekday 8-hour morning shift3C Change weekend 8-hour afternoon shift3D Change weekend 8-hour afternoon shift4A Reduce Arrival minutes by 1 minute4B Reduce Arrival minutes by 2 minutes4C Reduce Arrival minutes by 3 minutes4D Increase Arrival minutes by 1 minute5A Reduce Patient minutes by 1 minute5B Reduce Patient minutes by 2 minutes5C Reduce Patient minutes by 3 minutes5D Increase Patient minutes by 1 minute6A Combination of 3A, 3B & 4C6B Combination of 3C, 3D & 4C6C Combination of 4C& 5A7A Increase Demand by 10%7B Decrease Demand by 10%

and during the weekend, Scenario 6B performs the best. This is because these scenarios

combine multiple improvement scenarios together to ensure that the target turnaround

time of 30 minutes is achieved. The combined improvements include reducing arrival

minutes, reducing patient minutes and altering existing schedules. By implementing

these scenarios, a weekday transaction time of 24.21 minutes and weekend transaction

time of 21.08 minutes will result.

In order to reduce arrival minutes by three minutes (Scenario 4C), JHCC will need to

implement projects to reduce patient readiness delays, reduce time searching for equip-

ment and reduce elevator delays.


Table 5.5: JHCC Confidence Intervals for Weekday Results



1A 35.12 3.121B 29.84 0.601C 37.51 2.491D 37.33 1.171E 33.59 1.253A 36.80 2.093B 35.72 1.004A 35.79 1.534B 34.07 2.244C 29.74 1.625A 36.30 1.465B 31.85 0.995C 27.70 0.916A 24.21 0.696C 25.91 1.26

Table 5.6: KPIs resulting from Simulation

KPI Task Type% Prebooked jobs on-time Scheduled% Tasks complete within 30 minutes of receipt STAT, RoutineDispatch time STAT, RoutineTrip minutes Scheduled, STAT, RoutineTransaction time UnscheduledBottlenecks All jobs

To reduce patient minutes by one minute (Scenario 5A), JHCC will investigate uti-

lizing upper floors to transport patients rather than always using the basement.

Finally, to alter existing schedules both during the weekdays (Scenario 3A & 3B) and

weekends (Scenario 3C & 3D), discussions must be had with the Union to determine if

changes to the collective agreement can be made.


Figure 5.5: Weekday Transaction Time: Evaluating Improvement Scenarios

Figure 5.6: Weekend Transaction Time: Evaluating Improvement Scenarios


Figure 5.7: Weekday Transaction Time: Sensitivity Analysis

Figure 5.8: Weekend Transaction Time: Sensitivity Analysis

Chapter 6

Conclusion

The purpose of the research presented in this thesis was to model the patient transporta-

tion, or portering, department by developing a generic discrete event simulation. To

test its applicability, this model was used to represent the current porter operations at

two Toronto area hospitals: North York General Hospital and Juravinski Hospital and

Cancer Center. The simulation model was used to test improvement scenarios virtually

to see their impact on key performance metrics.

Once the simulation model was developed, it needed to be validated to ensure it

accurately represented the current situation at both hospitals involved in this research.

To validate the model, the current situation at the hospitals and the simulation result

were compared using KPIs such as % scheduled tasks completed on time, % unscheduled

tasks completed within the target turnaround time, dispatch time, transport time and

turnaround time. Historical data was analyzed to determine these KPIs for the current

situation, and simulation outputs were analyzed to determine KPIs for the simulation

result. Once their results were similar enough and hospital clients were happy with the

accuracy of the simulation, the research could move forward with scenario testing.

Scenarios were developed by the research team with heavy input from the hospitals.

77

Chapter 6. Conclusion 78

These scenarios were used to simulate improvements to the current process and how

these improvements would impact KPIs. Some scenarios included adding porters during

peak hours during with weekday and weekend, altering existing porter shifts, reducing

services times, and many combinations of multiple improvement scenarios in order to

reach the defined targets. It was determined that, for both hospitals, combining multiple

improvement scenarios together would produce the best result and many of the defined

targets for the key performance indicators would be met by implementing these improve-

ments. For example, at NYGH, if Scenario 8A was implemented, which involves changing

existing shifts, improving personal response time, improving task time and reclassifying

the number of two porter tasks, it would result in an average turnaround time of 23.38

minutes, an improvement from the base case turnaround time of 39.31 minutes. Similar

results were seen during the weekends (scenario 8B). Since these scenarios resulted in

the best turnaround time, it was recommended that NYGH implement scenario 8A to

see improvements during the weekdays and scenario 8B to see improvements during the

weekends. At JHCC, if Scenario 6A was implemented, which involves changing exist-

ing shifts, improving arrival minutes and improving patient minutes, an improvement of

13.65 minutes is realized, bringing the average turnaround time to 24.21 minutes dur-

ing the weekdays. Similar results were seen during the weekends (scenario 6B). Since

these scenarios resulted in the best turnaround time, it was recommended that JHCC

implement scenario 6A to see improvements during the weekdays and scenario 6B to see

improvements during the weekends.

This simulation model acted as a decision support tool for the hospitals involved to

move forward in their decision making process as to which improvement scenarios will

have the biggest impact on KPIs. The results were shared with NYGH and JHCC and

each hospital seemed enthusiastic about the improvements to portering that lie ahead.

Chapter 7

Future Research

To test the generality of the simulation model, for future research, it should be applied

to portering departments of at least three other hospitals. One hospital that has already

expressed interest in applying this model to improve their portering department is The

Scarborough Hospital, in Toronto. TSH has two hospital sites interested in using the

model. This will test the model’s applicability and there may be additions to the model

based on other hospital’s porter processes.

One of these additions could be including location as a further level of detail of each

task as this could increase the accuracy of the model. Currently at both hospitals studied

in this research, the dispatcher or manual dispatching system uses the starting location

of the task and the current location of an available porter to help assign porters to tasks

based on location proximity. This is done so that porters do not have to travel far empty

handed from finishing one task to starting the next. This aspect of the current situation

was found to be too difficult to capture in the current simulation model. To include this

in a new simulation model would require assigning a starting and ending location for

each task and ensuring the closest porter available is assigned.

Other areas of future research include finding ways to reduce the number of limitations

79

Chapter 7. Future Research 80

in the current model. For example, one option is changing a one porter task to a two

porter task after the first porter arrives at the origin. Another limitation to overcome

is including more logic in the dispatching system so that it can better reflect what a

human dispatcher’s decision making process involves. Overcoming these limitations and

applying the model to other hospitals to test its generality will increase the model’s

accuracy, and hence increase the confidence of its users.

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Appendix A

Process Maps for Dispatching

Processes

A.1 Process Maps

Figures A.1 and A.2 show the process maps, or decision making logic, when assigning

porter tasks to available porters at NYGH and JHCC.

86

Appendix A. Process Maps for Dispatching Processes 87

Figure A.1: NYGH Dispatcher Process Map

Figure A.2: JHCC Dispatch Process Map

Appendix B

Porter Shift Schedules

B.1 Porter Schedule: NYGH

Figure B.1 shows the porter schedule, including start times, end times and break times.

At NYGH, porters working eight hours receive two 30-minute breaks and porters working

four hours receive one 15-minute break. Overnight shifts take their breaks whenever there

are no porter requests.

B.2 Porter Schedule: JHCC

Figure B.2 shows the porter schedule, including start times, end times and break times.

At JHCC, porters working eight hours receive three breaks: the first and the third are

15-minutes long and the second is 30-minutes long. Porters working four hours or less

receive one 15-minute break.

88

Appendix B. Porter Shift Schedules 89

(a) Weekday Shift Schedule

(b) Weekend Shift Schedule

Figure B.1: NYGH Porter Shift Schedule

Appendix B. Porter Shift Schedules 90

(a) Weekday Shift Schedule

(b) Weekend Shift Schedule

Figure B.2: JHCC Porter Shift Schedule

Appendix C

Further Scenario Testing Results

C.1 Further Results: NYGH

Table C.1 shows the 90% confidence intervals for the results displayed in the Results

section of this report. Figures C.1 and C.2 are tables comparing the results of the base

case to all scenarios tested for five KPIs tested for different task types on the weekday

and weekend, respectively.

C.2 Further Results: JHCC

Table C.2 shows the 90% confidence intervals for the results displayed in the Results

section of this report. Figures C.3 and C.4 are tables comparing the results of the base

case to all scenarios tested for six KPIs tested for different task types on the weekday

and weekend, respectively.

91

Appendix C. Further Scenario Testing Results 92

Table C.1: NYGH Confidence Intervals for Weekend Results



2A 44.77 2.462B 46.28 2.322C 42.09 2.083C 55.77 2.113D 65.89 2.524A 54.13 1.934B 50.06 2.404C 49.46 2.035A 56.55 2.545B 50.52 2.115C 49.57 2.348B 29.33 1.868D 39.00 1.998E 42.40 3.048F 43.36 2.528G 36.61 2.02

Table C.2: JHCC Confidence Intervals for Weekend Results



2A 28.42 1.582B 29.91 1.372C 24.31 0.923C 29.11 1.503D 31.05 1.674A 31.26 1.344B 28.27 1.144C 24.15 1.415A 32.92 0.995B 30.89 1.405C 25.83 1.816B 21.08 0.686C 22.48 1.62


Figure C.1: Weekday Results of KPIs: Base Case vs. All Scenarios

Figure C.2: Weekend Results of KPIs: Base Case vs. All Scenarios


Figure C.3: Weekday Results of KPIs: Base Case vs. All Scenarios

Figure C.4: Weekend Results of KPIs: Base Case vs. All Scenarios

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