Redesigning an Outpatient Pharmacy Workflow Using ......ii Redesigning An Outpatient Pharmacy...

Redesigning an Outpatient Pharmacy Workflow Using Generic Simulation Modelling to Maximize a Renovation

Opportunity

by

Janet C. Izumi

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

Department of Mechanical and Industrial Engineering University of Toronto

© Copyright by Janet C. Izumi 2015

ii

Redesigning An Outpatient Pharmacy Workflow using Generic

Simulation Modelling to Maximize a Renovation Opportunity

Janet C. Izumi

Master of Applied Science

Department of Mechanical and Industrial Engineering

University of Toronto

2015

Abstract

The renovation plans of the outpatient pharmacy at Princess Margaret Cancer Centre presented

an opportunity to apply discrete event simulation (DES) to help decision makers estimate the

resource and space required for the pharmacy to support prescription volume growth over a ten-

year horizon. There is an extensive literature on renovating existing pharmacy workspaces, but

research on future capacity planning of pharmacy workspaces is sparse. The trend of increasing

spending on prescription drugs underscores the importance of researching capacity planning of

outpatient pharmacies. The goal of this study was to develop a generic simulation model that

could be reused to plan the future capacity of any outpatient pharmacy. The generic DES was

applied to two different pharmacies to explore different workflow configurations and assess the

impact of introducing automation. Recommendations were provided to each pharmacy

management team on the number of workstations and space required to achieve the target patient

wait time.

iii

Acknowledgments

I would like to thank my supervisor, Professor Michael W. Carter, for his patience and guidance

throughout this research. Every meeting with you challenged me to look at the problem from

different perspectives and always ended with encouragement. I am inspired by your commitment

to improving the health care system.

I would also like to thank the outpatient pharmacy team at Princess Margaret Cancer Centre and

Toronto General Hospital. The strong leadership of Esther Fung, Vivian Choi, Karen Chuk, and

Christine Tse was critical to the success of this research and the acceptance of the results. The

staff at University Health Network is dedicated to delivering the very best for patients and gave

their honest feedback throughout this research. Tessa Pietropaolo has been instrumental in

analyzing and summarizing data. I thank her for her positive energy and insightful discussions.

Carolyn Busby has been a great mentor and I would like to thank her for the technical expertise

and support.

Finally, I thank my family for their unwavering support and patience. I would like to recognize

my parents in supporting me in my academic pursuits and a special thanks to my sister and her

husband, Jennifer and Toby McNeill, for their belief in me.

iv

Table of Contents

Acknowledgments .......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ................................................................................................................................. vi

List of Figures ............................................................................................................................... vii

Chapter 1 ......................................................................................................................................... 1

1 Introduction ................................................................................................................................ 1

Chapter 2 ......................................................................................................................................... 4

2 Background and Problem Analysis ............................................................................................ 4

2.1 Case Study 1: Princess Margaret Outpatient Pharmacy ..................................................... 5

2.2 Case Study 2: Toronto General Hospital Outpatient Pharmacy ....................................... 12

2.3 Research Objectives .......................................................................................................... 14

Chapter 3 ....................................................................................................................................... 15

3 Literature Review ..................................................................................................................... 15

3.1 Hospital renovation planning ............................................................................................ 15

3.2 Pharmacy workflow and technology implementation ...................................................... 17

3.3 Generic Discrete Event Simulation ................................................................................... 18

Chapter 4 ....................................................................................................................................... 20

4 Methodology ............................................................................................................................ 20

4.1 Specific Discrete Event Simulation .................................................................................. 20

4.1.1 Model design ......................................................................................................... 20

4.1.2 Validation .............................................................................................................. 25

4.2 Generic Discrete Event Simulation ................................................................................... 26

4.2.1 Model design ......................................................................................................... 26

4.2.2 Validation .............................................................................................................. 29

v

4.3 Square footage estimates ................................................................................................... 29

Chapter 5 ....................................................................................................................................... 31

5 Results ...................................................................................................................................... 31

5.1 Case Study 1: Princess Margaret Outpatient Pharmacy ................................................... 31

5.1.1 Scenario 1: Double check workflow with barcode technology ............................ 32

5.1.2 Scenario 2: Triple check workflow with barcode technology .............................. 33

5.1.3 Sensitivity Analysis on prescription demand increase .......................................... 34


5.2.1 Scenario 1: Changes in staffing level and skill-mix ............................................. 36

5.2.2 Sensitivity Analysis on prescription demand increase .......................................... 37

Chapter 6 ....................................................................................................................................... 38

6 Discussion ................................................................................................................................ 38

6.1 Case Study 1: Princess Margaret Outpatient Pharmacy ................................................... 38


Chapter 7 ....................................................................................................................................... 40

7 Conclusion and Future work .................................................................................................... 40

Chapter 8 ....................................................................................................................................... 42

8 References ................................................................................................................................ 42

Appendix A: Kroll Workflow matrix ........................................................................................... 47

Appendix B: Process flow maps ................................................................................................... 48

Appendix C: Simulation screenshots ............................................................................................ 51

Appendix D: Input data analysis results ....................................................................................... 53

Appendix E: Pharmacy Floor plans .............................................................................................. 61

vi

List of Tables

Table 1. Percentage of fully and partially automated prescriptions by number of drugs on

prescriptions .................................................................................................................................. 10

Table 2. Input parameters for Princess Margaret Specific DES ................................................... 22

Table 3. Specific DES output of resource utilization and same day patient percentages ............. 24

Table 4. Comparison of actual PM data and simulation results of performance metrics ............. 25

Table 5. Space model for estimating square footage per workstation/work area of the current

state and future state of PM .......................................................................................................... 30

Table 6. PM Generic DES results of double-check workflow scenario with barcode technology 32

Table 7. PM Generic DES results of triple-check workflow scenario with barcode technology . 33

Table 8. Prescription and patient count projection for 4%, 6% and 8% annual growth rate

scenarios ........................................................................................................................................ 34

Table 9. PM Generic DES results of sensitivity analysis on annual prescription growth rates .... 34

Table 10. TGH data for nine input parameters ............................................................................. 35

Table 11. Comparison of actual TGH data and simulation results of performance metrics ......... 35

Table 12. TGH Generic DES results of different skill-mix and staff levels ................................. 36

Table 13. TGH Generic DES results of sensitivity analysis on annual prescription growth rates 37

vii

List of Figures

Figure 1. Canadian Drug Store and Hospital Purchases Audit, 2010, IMS Brogan. ...................... 6

Figure 2. Princess Margaret Cancer Centre prescription and patient process flow ........................ 7

Figure 3. Same day patient percentage as a function of expected wait time .................................. 8

Figure 4. Toronto General Hospital prescription and patient process flow .................................. 13

Figure 5. Specific Outpatient Pharmacy DES model of Princess Margaret Cancer Centre ......... 21

Figure 6. Lognormal PDF and histogram of Take-in service times (left), Q-Q plot of Take-in

service times (right) ...................................................................................................................... 23

Figure 7. Specific DES output of same day wait times by day of week and time of day ............. 24

Figure 8. Generic DES patient and prescription process flow ...................................................... 27

Figure 9. Generic Outpatient Pharmacy DES model .................................................................... 28

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Chapter 1

1 Introduction

Space in a hospital is limited and thoughtful allocation is required to plan for future growth or

decline. In April 2015, the Princess Margaret Space Transformation (PMST) project was

initiated to renovate the first floor of the Princess Margaret Cancer Centre (PM), which presented

a rare opportunity to redesign and expand the capacity of the hospital’s outpatient pharmacy. In

early planning meetings with architects and external consultants on the PMST project, the critical

decision of how much space the pharmacy would require to sustain future growth was left up to

the pharmacy management team to determine based on experience working in the pharmacy. The

team lacked decision support tools to make an informed decision and capitalize on this

opportunity to design a more efficient pharmacy. External consultants specializing in pharmacy

redesign gave general suggestions, but with few specifics on the number of workstations and

space required.

The pharmacy management team was inspired by the Pebble Project research initiative of the

Centre for Health Design to implement evidence-based design, which is the process of basing

decisions on credible research to achieve the best possible outcome (Taylor & Keller, 2010).

Operations research techniques provide evidence-based decision support tools for redesigning

the workflow to meet goals such as maximizing resource utilization and improving patient

outcomes through shorter wait times. There is extensive literature on renovating and retrofitting

the existing space of a pharmacy, but research on future capacity planning of pharmacy

workspaces is sparse. The trend of increasing spending on prescription drugs underscores the

importance of researching capacity planning of outpatient pharmacies. This research developed a

generic discrete event simulation to evaluate and estimate the pharmacy’s resource and space

requirements for different workflow scenarios.

Opening or relocating to a new facility provides a perfect opportunity to implement process

change in an organization. In order for the pharmacy to remain in operation throughout the

renovations, the pharmacy would need to move to a new location on the first floor. This was an

opportunity for the pharmacy staff to think outside the box and challenge the status quo, as they

2

would not be bound by the current layout or location. (Previous renovations of the pharmacy at

PM have been done in a piecemeal fashion, which has resulted in staff working in spaces outside

of the pharmacy or crammed in space originally designed for another purpose.)

There is a close interrelationship between the workflow and the floor plan. Studies have shown

that the floor plan is the foundation of any pharmacy because it dictates workflow. (Aguilar,

Chau, Giridharan, Huh, Cooley, & Warholak, 2012) An inefficient floor plan can lead to wasted

time, (Chou, et al., 2012), longer patient wait times (Groothuis, Goldschmidt, Drupsteen, de

Vries, Hasman, & van Merode, 2002) and decreased staff satisfaction (Lin, Leu, Breen, & Lin,

2008). Traditional lean techniques were applied to determine the most efficient workflow and

reassess the necessity and squence of each step in the process. Furthermore, the pharmacy

management team was keen on investing in new technology and automation that could

standardize workflow and increase inventory productivity. The most appropriate decision

support tool to understand the implication of adding, removing or changing the sequence of steps

in the workflow was discrete-event simulation (DES).

DES allows decision makers to test many scenarios before new policies and practices are

implemented. DES has been widely used in healthcare to make decisions on patient flow and

allocation of scarce resources (Jun, Jacobson, & Swisher, 1999). Although the focus has been

mainly on emergency departments, queuing theory and simulation have been used to optimize

pharmacy performance in hospitals (Bahadori, Mohammadnejhad, Ravangard, &

Teymourzadeh, 2014) (Ishimoto, Ishimitsu, Koshiro, & Hirose, 1990).

There are two types of DES models: specific and generic. A specific DES is tailored to a single

process for a single industry. A generic DES provides a framework to apply conceptual

principles such as theoretical queuing models to any process or industry. The generic DES

approach best suits the needs of this study because it allows for the flexibility to test multiple

workflow scenarios to assess the impact of process changes and technology implementations.

The reusable feature of a generic DES fulfills the pharmacy management team’s need to be able

to model two other outpatient pharmacies at a different hospital that are due for renovations in

the coming years. The primary objective of this study was to recommend options for the ideal

workflow and floor plan through the application of generic DES modelling. DES will answer the

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question of how many workstations and resources the pharmacy requires to keep up with the

growing demand on the pharmacy’s services.

This paper is organized as follows. Chapter 2 will provide the context of the problem and

present the problem analysis. Chapter 3 will discuss the previous work done in hospital

renovation planning, pharmacy workflow and technology implementation and generic DES.

Chapter 4 will discuss the methods used to develop and apply the generic DES to two outpatient

pharmacies at different hospital sites. Chapter 5 examines the model validation and the results of

what-if scenario testing. Chapter 6 will summarize the results in the context of generic DES

application in healthcare.

4

Chapter 2

2 Background and Problem Analysis

Expenditures on medications in Canada have grown to become 16.3% or $31.3 billion of health

spending (Canadian Institute for Health Information, 2011), a trend that is expected to continue.

The largest contributors of growth in spending were increased volume of use and changes in the

mix of treatments being used, which grew at an average annual rate of 6.2% and 2.0%,

respectively. (Canadian Institute for Health Information, 2012) Both volume and mix effects

were due in part to changes in treatment guidelines, increased disease prevalence and the uptake

of new drugs and the recent drug development trends suggest cancer drugs and

immunosuppressants will continue to be key areas of growth in the years to come (Canadian

Institute for Health Information, 2012). Pharmacies have been struggling to fulfill this growing

demand, in particular pharmacies specializing in dispensing cancer and immunosuppressants

drugs. The renovation plans for the first floor of PM was an opportunity to apply DES to model

the impact of the future growth in demand on the pharmacy workflow and patient wait times. It

would serve as a decision support tool to determine the appropriate capacity needed to support

growing demand. PM is part of the University Health Network (UHN), which internally operates

three outpatient pharmacies under one central management team and oversees the outpatient

pharmacy at Toronto Western Hospital privately run by Shoppers Drug Mart. A generic DES

would allow UHN to model any of its pharmacies with minimal effort.

The management team has identified long wait times, prescription rework, and prescription

filling errors as common challenges across all pharmacies. A poor experience at the outpatient

pharmacy has a lasting impression on the patient’s perception of their overall experience at the

hospital because the pharmacy is commonly the patient’s last stop before going home. The

pharmacy also maintains an on-going relationship with patients to refill their prescriptions

throughout their treatment. The main objectives of the pharmacy management team are patient

and staff safety and wait time reduction.

The purpose of this chapter is to provide background on patient demographic, prescription

growth and medication dispensing processes of the two UHN outpatient pharmacies modeled in

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this study. This chapter will also introduce the approach taken to estimate the future capacity

needs of the pharmacy in ten years and the research objectives.

2.1 Case Study 1: Princess Margaret Outpatient Pharmacy

The outpatient pharmacy at PM specializes in dispensing high-cost specialty cancer drugs

typically not dispensed by community pharmacies. The pharmacy is staffed with six full-time

equivalent (FTE) pharmacists and six FTE technicians to run the dispensing operations, not

including supervisory or supporting staff. This pharmacy is the focus of this research because a

budget of 60 million dollars was approved in April 2015 to initiate the Princess Margaret Space

Transformation project to renovate the first floor of the hospital. This section will discuss the

renovation project’s mandate, the factors of growth that justify planning for increased capacity,

the current workflow, future technology capabilities and the constraints of the current space.

The mandate of the renovation project is to increase space focused on patient care and to reduce

clinical office space. The plan is to keep the blood lab, outpatient pharmacy and patient waiting

areas on the main floor while the genecology clinic is being relocated to another floor of the

hospital. In addition, the development of a dedicated Palliative Care clinic and improved patient

and staff amenities are proposed. The renovations begin in 2015 and will be accomplished over

the next four years through several phases of renovations and relocations. The departments

planned to remain on the first floor were asked to present a case for why their department would

need additional space to accommodate future growth over the next ten years. In the case of the

outpatient pharmacy, increasing prescription volumes is the main reason for growth.

Prescription volumes have grown by 17% since the last renovation of the PM outpatient

pharmacy in 2008. The ten-year compound annual growth rate of prescription count at PM is

4.2%. The highest growth of 11% was in 2008 when there was major shift from IV injection

chemotherapy treatment to oral chemotherapy drugs. Since then, 19 new oral cancer drugs have

been approved by the Ministry of Health and Long-term Care and are dispensed by PM. Cancer

drugs were the third-largest drug class in terms of wholesale spending between 2000 and 2010

(Canadian Institute for Health Information, 2012) with one of the highest annual average growth

of 13.4%. The introduction of new biologic medications, which are often more expensive, is a

6

major driver of growth contributing 12.2% to the overall growth. Figure 1 breaks down the

factors that contibuted to the growth in cancer drug spending and captures growth from new

biologic medication under the Mix of Drug Types category. The duration of oral chemotherapy

treatment varies by patient, but the frequency of medication refills is every three months.

Figure 1. Canadian Drug Store and Hospital Purchases Audit, 2010, IMS Brogan.

Process flow mapping was used to document the current workflow at PM. First, the pharmacy

staff provided a brief overview of the process and then a full week was spent on-site observing

the pharmacy operations. Second, an interactive workshop was held with a focus group of 3-4

staff members with several years of experience working in different functions within pharmacy.

Out of the workshop, a process flow map that documents the sequence of steps taken by staff to

dispense medication to a patient was created. There was often a difference between what they

thought should happen and what actually happened. The process flow map was finalized after it

was presented to all the pharmacy staff for review and feedback. The process flow maps were

created to capture only the steps that need to occur in order to process a prescription. Supporting

operations, such as inventory replenishment and shift scheduling, billing and accounting

reporting, were excluded because these tasks did not directly impact the same day patient wait

times.

The processes within the pharmacy can be described in terms of the prescription pathway and the

patient pathway shown in Figure 2. The process begins with the patient dropping off the

prescription and ends with the patient receiving the appropriate counselling and leaving with the

correct drugs.

7

Figure 2. Princess Margaret Cancer Centre prescription and patient process flow

The first step of the prescription pathway is taking in the prescription. The patient takes a ticket

number from the q-matic machine and waits for their number to be called. The q-matic ticket

machine serves the purpose of maintaining the order of the queue and ensuring patient

confidentiality by calling patients on the intercom by ticket number rather than by name. Once

the patient is called to the take-in booth, the pharmacist confirms the patient’s identity with two

patient identifiers, such as date of birth and surname, and reviews the prescription with the

patient. Based on the take-in pharmacist’s experience, expertise and conversation with the

patient, the take-in pharmacist will select new patient prescriptions or prescriptions with a

change in dose that the patient is unaware of for pre-edit. This accounts for approximately

twenty-five percent of prescriptions dropped off at the pharmacy. Pre-edit is the process of the

lead pharmacist reviewing the prescription for clinical errors before it is entered into the

pharmacy information system by a technician. The pre-edit step is not a standardized process and

it was introduced because of the large amount of rework caused by clinical errors being found

during the pharmacist’s formal clinical check, which is the last step of the workflow. In the

second step, a technician enters and adjudicates the prescription in the pharmacy information

8

system. In the third step, a technician fills the prescription and checks for billing issues related to

insurance coverage. In the fourth step, a registered technician performs the first technical check

to catch any errors in dispensing. In the fifth step, a pharmacist completes a double check of

clinical appropriateness and content accuracy before signing off on the prescription to be

released to the patient.

The patient pathway has two streams: drop-off and pick-up. The patient’s pick-up preference,

categorized as either same day or next day, determines which stream the patient will follow. On

average 70% of patients opted to pick-up their prescriptions the next day. The patient’s choice

could be based on a variety of reasons, but an analysis of the data suggested that if the patient

was quoted an estimated wait time of over an hour they were more likely to opt for next day

pick-up. This relationship was modeled as a function of expected wait time and percentage of

same day patients shown in Figure 3. It was extrapolated from this relationship that the longer

the expected wait time the lower the percentage of patients willing to wait for same day service.

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Figure 3. Same day patient percentage as a function of expected wait time

Home delivery is another option patients can choose. Delivery is free of charge and it can take

up to two days based on the patient’s address. There are some limitations on the types of drugs

that can be delivered, but it is a common option for refill prescriptions that are not urgent and

require minimal pharmacist counselling. The percentage of prescriptions delivered is low at 10%

because only 24% of prescriptions dispensed by PM are refills. Patients that have their

prescriptions delivered are captured as next day patients. Depending on the patient’s health

condition and oral chemotherapy treatment schedule, some patients are not able to wait for

delivery. Most importantly, pharmacists are considered an integral part of the patient’s care and

9

patients visit the pharmacy to not only pick-up their medication, but also receive important one-

on-one counselling from a pharmacist.

Next day patients compete with same day patients for the same scarce resources and the more

patients are deferred to the next day the less capacity there is to offer same day service. This

results in the pharmacy constantly playing ‘catch up’. The patient’s pathway diverges again

depending on whether the patient requires consultation by a pharmacist. Pharmacists are legally

required to counsel all patients starting a new prescription or patients prescribed targeted agents,

narcotics or controlled substances before the medication can be released to the patient. Based on

an analysis of dispensed prescriptions, 70% of patients require pharmacist consultation.

Kroll Version 9 is the pharmacy information system currently used by PM. Kroll is used for

filling prescriptions (patient, drug, doctor and directions to be written on the package or label for

the use of the patient (ie. SIG)), adjudication, label printing, inventory replenishment, accounts

receivable, reporting, and audit trails. It is the system of record for patient and inventory

information. Only the basic functions of Kroll are currently enabled even though upgrade

functions such as barcode technology, data validation and will call automation exist. Barcode

technology verifies that the correct product has been chosen, and in the correct pack size to

efficiently manage inventory. It ensures every drug is dispensed one at a time rather than in

batches to minimize the possibility of mixing stock bottles. It also allows for real-time tracking

of the prescription through the pharmacy workflow. Will call automation tracks when a

prescription is picked up and by whom. Data Validation verifies on the screen that the

prescription is clinically appropriate on the screen and enables pharmacists to electronically sign-

off on a prescription at that point in the workflow. Full details of all the workflow options

available with Kroll can be found in Appendix A. Installing the Kroll barcode tracking upgrade

would require a handheld scanner, an additional monitor, and label printing machine at every

filling workstation and there is insufficient counter space for this equipment in the current

workspace. The new workspace will be designed to accommodate the implementation of the

Kroll barcode tracking upgrade.

Another future technology capability that PM is considering is an automated dispensing machine

robot (ADM) to alleviate the workload on technicians in the dispensing step of the workflow and

to reduce prescription-filling time. The utilization rate of the ADM was determined by creating a

10

shortlist of drugs that could be stored in the ADM. The criteria for automation were based on

highest dispensing frequency. The list excluded drugs in liquid, injection or gel form because the

ADM can only handle only drugs in tablet or capsule format. Chemotherapy drugs were also

excluded due to cross-contamination concerns.

In this paper, a script refers to an instance a single drug is dispensed and a prescription refers to

the group of drugs prescribed to a patient. Data analysis revealed that a shortlist of 172 drugs

would allow 48% of 75,381 scripts dispensed in a year to be automated. Of the total 37,135

prescriptions dispensed in a year, 23% could be fully automated and 25% could be partially

automated. Partial automation means that one or more drugs on the prescription can be

automated, but a technician dispenses at least one drug on the prescription manually. Further

analysis of the data revealed that the percentage of fully and partially automated prescriptions

differed based on the number of drugs on the prescription. The percentages of fully and partially

automated prescriptions are shown in Table 1. A surprising result was that fully automated single

drug prescriptions were very low at 22% because chemotherapy drugs were excluded from the

list of automatable drugs. PM does not have sufficient room to install an automatic dispensing

machine in their current space.

Number of drugs on the prescription

Percentage of fully Automated prescriptions

Percentage of partially automated prescriptions

1 22% 0% 2 33% 32% 3 29% 58% 4 15% 80% 5 8% 88% 6 6% 93% >=7 3% 96%

Table 1. Percentage of fully and partially automated prescriptions by number of drugs on prescriptions

The PM outpatient pharmacy occupies approximately 1,549.3 sq. ft. on the first floor, dispensing

250-350 prescriptions a day. An additional 187.7 sq. ft. of private patient counselling booths for

medication reimbursement counselling is located on the fourth floor of the hospital. The

medication reimbursement consultation workflow was not included in the simulation models

11

because it was considered a separate process that does not directly involve the pharmacy staff.

Once a patient is referred to a medication reimbursement specialist, a consultation appointment is

booked and the patient either waits for their prescription until the government paperwork is

processed or, in special cases, the patient’s prescription is filled from the compassionate drug

supply in the interim. The processes described in the patient and prescription pathways currently

use 41% of the total square footage on the first floor. The remaining space is used for over-the-

counter retail space, inventory, office space, patient record storage and staff interaction area.

The practice of pharmacy is highly regulated. Pharmacies must abide by both Federal and

Provincial Acts and regulations monitored by the Ontario College of Pharmacists. These

regulations and policies have implications on how the pharmacy workspace can be designed. The

follow is a list of areas that the pharmacy management team would like to improve to align

closer or exceed standards set by these policies and regulations.

Lack of properly ventilated chemo counting dispensing area to ensure staff safety

Lack of dedicated outpatient IV hood-preparation area with an anteroom to prepare

intravenous admixtures (IVADS). Currently IVADS is outsourced to the inpatient

pharmacy.

Most patient consultation booths are not accessible to patients’ in wheelchairs/assisted

devices or equipment

Lack of privacy, noise and over-crowding of patient consultation booths

Lack of staff lounge large enough to accommodate all 23 staff members. The staff

lounge is required to be separate from drug dispensing area according to the Ministry of

Labour No Food/Drink policy

The pharmacy team set two goals for the redesign. The first goal was to improve the patient

experience by reducing the current average wait time of 1 hour and 20 minutes to 1 hour or less

and the second goal was to increase the percentage of patients receiving same day service.

12

2.2 Case Study 2: Toronto General Hospital Outpatient Pharmacy

The outpatient pharmacy at Toronto General Hospital (TGH) is a general pharmacy that does not

specialize in dispensing any one type of drug. As a general outpatient pharmacy, the pharmacy

has placed an emphasis on the expanded scope of practice of pharmacists, which includes

smoking cessation programs and flu shot administration. The pharmacy carries a wide-range of

drugs to cater to the diverse patient demographic at TGH. The pharmacy is staffed with three

FTE pharmacists and three FTE technicians to run the operations, not including supervisory

staff. The prescription count at TGH was on a growth trend, but declined drastically when a

specialized outpatient transplant pharmacy opened on the 12th

floor of TGH in 2013. The

general outpatient pharmacy would like to expand to treat Hepatitis C patients in the future. This

section will discuss Hepatitis C demand, the current workflow and technology capabilities and

the constraints of the current space.

Hepatitis C infects an estimated 0.8% of the population in Canada. While reported cases have

been on the decline, the health burden of existing cases that progress to more serious conditions

continues to escalate. (Centre for Communicable Diseases and Infection Control, 2012) There is

no licensed vaccine for Hepatitis C. Hepatitis C treatment includes a combination of antiviral

medications. There are approximately 400 patients at TGH being treated for Hepatitis C. The

current workflow at TGH can be described in terms of the prescription pathway and the patient

pathway. Figure 4 shows the patient and prescription process flow beginning with the patient

dropping off the prescription and ending with the patient receiving the appropriate counselling

and leaving with the correct drugs.

13

Figure 4. Toronto General Hospital prescription and patient process flow

The same process flow mapping approach that was used for PM was used for TGH. A full week

was spent on-site observing the pharmacy operations and another week was spent on collecting

feedback to make sure the workflow was captured accurately. Supporting tasks and operations

such as inventory replenishment and shift scheduling, billing and accounting reporting, while

critical to the pharmacy’s operations, were excluded from the process flows because these tasks

did not directly impact the same day patient wait times.

The prescription follows four mandatory steps through the pharmacy. In the first step, a

technician confirms the patient’s identity with two patient identifiers and enters and adjudicates

the prescription. In the second step, prescription labels are printed and passed on to a technician

to fill. Extensive travelling was observed to locate and retrieve medication stock bottles. In the

third step a pharmacist completes a clinical and content check before signing off on the

prescription to be released to the patient. TGH does not have an intercom system to call patients

to the counselling booth and there is poor visibility of waiting patients from inside the pharmacy.

Similar to PM, the patient pathway has two streams: drop-off and pick-up. The same patient

pick-up preferences and pharmacist consultation requirements are variables that influence the

path the patient follows through the process. TGH also uses the same Kroll Version 9 pharmacy

information system used by PM. Only the basic functions of Kroll are currently enabled, but the

14

pharmacy team is interested in upgrading to enable barcode technology and will call automation.

The prescription volumes of TGH were too low to warrant the capital investment in an ADM.

The TGH outpatient pharmacy occupies approximately 2,018 sq. ft. on the first floor of the

hospital, dispensing 150 scripts a day. The processes described in the patient and prescription

pathways currently use 38% of the total square footage. The remaining space is used for over-

the-counter retail space, inventory, office space, patient record storage and staff interaction area.

The TGH pharmacy team set two goals; guarantee 30-minute wait time for same day patients and

increase resource utilization.

2.3 Research Objectives

The primary objective of this study is to develop a generic DES to recommend options for the

ideal workflow and space requirements of an outpatient pharmacy. Two pharmacies, PM and

TGH, were modeled by the generic DES to answer the question of how many workstations and

resources the pharmacy would need to keep up with the growing demand on the pharmacy’s

services.

15

Chapter 3

3 Literature Review

Renovation projects in healthcare are costly and infrequent. Operations research (OR) techniques

provide evidence-based decision tools for redesigning the pharmacy workflow to maximize

resource utilization and improve patient outcomes through shorter wait times. This chapter will

discuss previous research in hospital renovation planning, pharmacy workflow and technology

implementation, and generic DES applications in healthcare and other industries.

3.1 Hospital renovation planning

It is common in manufacturing to use OR techniques to inform workflow, capacity and layout

decisions in newly designing or renovating a facility (Greasley, 2008)(Savsar,1991). In

healthcare, the research published on the application of OR in the early architectural planning

phases is limited because full-scale renovations or new facility projects are rare. However, there

is a strong case to apply OR to hospital renovation projects. Carnes and Koons (2010) describe

the key factors of success in renovating a pharmacy, as early planning, strong leadership and

thorough knowledge of the rules set by regulating and accrediting organizations. Inadequate

pharmacy layout and poorly designed workflow patterns were identified as barriers to provision

of patient counselling (Angelo & Ferreri, 2005). A study of the effect of the physical

environment on hospital pharmacists’ work outcomes found that the space and functional design

of the dispensing area and the noise generated from drug-related equipment contributed the most

to pharmacists’ dissatisfaction with the workspace (Lin, Leu, Breen, & Lin, 2008). For this

review, the literature on hospital renovation planning is organized by the phase that the research

was involved in: 1) early conceptual phase, 2) middle planning phase, 3) late detailed design

phase.

Early in the conceptual design phase of the renovation, the results of research studies can

influence capacity planning and space allocation. Wiinamaki & Dronzek (2003) successfully

applied simulation to project the bed capacity for an emergency care centre (ECC) expansion.

The simulation model of the patient cycle from admission to discharge was used in designing the

basic high-level concepts of layout, number of rooms and beds, and hours of operations. The

16

study accounted for an annual increase in visits to the ECC of 5% and performed sensitivity

analysis on the impact of increased aging of the population on the length of stay and bed

capacity. Although Wiinamaki & Dronzek’s model had a custom spreadsheet user interface to

allow for easy data entry and display of results, it was not designed to be generic. Miller, Ferrin,

& Messer (2004) developed a simulation program of an emergency department to inform future

expansions of hospital facilites on capacity and process improvements. They mention that

opening a new facility provides a perfect opportunity to implement process change in an

organization.

In the middle of the design phase, space is no longer a variable, but a constraint. A study of the

relocation of the phlebotomy department used simulation to run experiments to understand the

effects of different architectural layout and changes in organizational procedures on select

performance indicators within a defined space. (Groothuis, Goldschmidt, Drupsteen, de Vries,

Hasman, & van Merode, 2002). The study found that increasing the number of take-in counters

reduced the overall patient wait time even though there would be fewer nurses available to

provide phlebotomy services (Groothuis, Goldschmidt, Drupsteen, de Vries, Hasman, & van

Merode, 2002). A separate study of a pharmacy renovation recommended allocating more space

to patient counselling booths to enhance the patient-pharmacist interaction (Angelo & Ferreri,

2005).

Late in the design phase, the redesign is limited to rearranging workstations within the existing

space. Layout redesign studies specific to outpatient pharmacies were limited to rearranging the

current layout to drive efficiencies by reducing high traffic areas, designing space by function

(Aguilar, Chau, Giridharan, Huh, Cooley, & Warholak, 2012), and reducing transportation

distances (McDowell & Huang, 2012). OR techniques are useful at any stage of planning a

hosptial renovation, but the earlier in the planning process these techniques are applied the better

informed decision makers can be of the outcomes of each new design.

17

3.2 Pharmacy workflow and technology implementation

There are two areas of pharmacy redesign that are well studied: workflow, and technology

implementation. There is an abundance of research on applying reengineering and quality

improvement techniques to improve the quality of care through workflow changes.

Reengineering and quality improvement techniques have been used to identify delays,

unnecessary steps, or the potential of errors and removing them. Studies on pharmacy workflow

emphasis increasing the time pharmacists spend on interacting and counselling patients.

Technical tasks such as prescription filling, label and stock bottle retrieval, will-call preparation

(Jenkins & Eckel, 2012) or adjudication and data entry (Angelo & Ferreri, 2005) were identified

as tasks that should be delegated to technicians rather than pharmacists. Kelly and Redman

(2009) describe design principles in redesigning a pharmacy workspace and workflow such as

applying Lean principles to reduce waste in motion, storage, and time and to create assembly

line-like flow, promote efficient hand-offs between processes to eliminate redundancies and gaps

in communication and plan for short-term and long-term changes by building flexibility in the

design. Another study restructured the inpatient pharmacy deparment into a decentralized model

with pharmacy satellies dispersed by department. (Al-Shaqha & Zairi, 2000) Pharmacists were

recorded as making a greater contribution to patient care, but vacation coverage proved to be

harder in a decentralized model because it required cross-training to transfer in-depth patient

specific knowledge.

As new technologies are developed to increase the efficiency of pharmacies, numerous studies

have assessed the impact of technology on patient wait times, pharmacy workflow and staff

satisfaction. Creative uses of technology have been explored in the outpatient pharmacy setting

such as electronic pagers to recall patients undergoing chemotherapy, (Farrugia, Ingledew,

Dawes, & Moss, 2006) but the main area of focus in new technology development has been in

reducing errors and improving the efficiency of the dispensing function of the pharmacy. While

there is evidence in the literature to suggest that automation reduces prescription-filling time

(Lin, Huang, Punches, & Chen, 2007) (James, et al., 2013), the performance and implications of

new technology on the pharmacy operations can be context-dependent. Tan, Chua, Yong, & Wu,

(2009) concluded automation alone could not help the pharmacy achieve the target wait time

because there was a bottleneck at filling and at patient counselling. Two machines or a machine

twice as fast could help achieve the wait time target and reduce pharmacy technicians by 27%.

18

Lin, Huang, Punches, & Chen (2007) concluded that automation was enough to allow the

pharmacy to handle a 5% increase in prescriptions. The evaluation of carousel dispensing

technology allowed the pharmacy to reduce the amount of technician labour required to dispense

medication; however, the average turnaround time did not significantly improve (Temple &

Ludwig, 2010). There is evidence that technology reduces dispensing errors (Poon, et al., 2006);

however, there is also evidence that technology can introduce new kinds of errors (Oswald &

Caldwell, 2007). In a study of patient misidentification, it was found that linear line barcode

identification methods are not failsafe and careful control of bar code scanning and printing

equipment specifications are recommended to minimize the threat to patient safety (Snyder,

Carter, Jenkins, & Fantz, 2010).

An area of controversy is the impact of automation on pharmacy staff. A study conducted by

James et al. (2013) found that automation had an overall positive impact on staff experience and

stressors, improving working conditions and workload. However, technicians reported that

automation devalued their skills. The authors recommended rotating staff activities and role

expansion. While the adoption of electronic health records, storage technologies and repackaging

automation is widely adopted, Siska & Tribble (2011) point to a lack of a technology solution to

integrate electronic patient records with computerized prescriber-order-entry that could provide

timely relevant clinical decision support at the point of care.

3.3 Generic Discrete Event Simulation

Simulation has been extensively used in healthcare to assess the efficiency of health care

delivery systems, test ‘what-if’ scenarios and design new systems. In a study that compiled over

200 journal articles on simulation in healthcare, the problems addressed by simulation are largely

categorized into patient flow and resource allocation (Jun, Jacobson, & Swisher, 1999). Patient

flow problems address the impact of patient scheduling and admissions and staff scheduling,

while resource allocation problem address the impact of bed capacity planning, room sizing and

planning, and resource sizing.

Generic models have been described in terms of four levels of genericity using the key

dimensions of abstraction and transportability (Fletcher, Halsall, Huxham, & Worthington,

2007). The first level is a high-level conceptual model of generic principles such as theoretical

queuing models. The second level is a generic framework or toolkit that uses multiple modules

19

of generic processes specific to an industry. The third level is specific to an industry and process,

but transportable to different sites. The fourth level is setting specific and not transportable to

other service providers. This research will develop a level three DES. This section will discuss

the specific and generic DES models built in healthcare.

Specific DES pharmacy models built for patient flow problems investigate the relationship

between patient wait times and demand conditions and modes of operations (Myers, Johnson, &

Egan, 1972) or plan the change from a decentralized to a centralized robotic dispensing machine

(Buchanan, 2003). Specific DES pharamcy models built for resource allocation problems

determine the optimum number of staff (Ishimoto, Ishimitsu, Koshiro, & Hirose, 1990)

(Bahadori, Mohammadnejhad, Ravangard, & Teymourzadeh, 2014) or assess the changes in staff

level and skill-mix and increased robot utilization to patient wait times (Reynolds, et al., 2011).

Generic DES have been developed for applications ranging from supply chain (Umeda & Zhang,

2006) (Hung, Kucherenko, Samsatli, & Shah, 2004) to space launch operations (Kaylani,

Mollaghasemi, Cope, Fayez, Rabadi, & Steele, 2008), but in healthcare the majority are applied

to emergency departments (Fletcher, Halsall, Huxham, & Worthington, 2007) (Steins &

Walther, 2013). Sterilization services have been modelled (Di Mascolo & Gouin, 2013), but

there is a lack of generic models for pharmacy services, which this research will develop.

20

Chapter 4

4 Methodology

This chapter describes the three main steps taken to develop a generic DES for an outpatient

pharmacy. First, a specific DES was built and validated for the PM outpatient pharmacy site.

Second, the specific model was made generic to accommodate the range of simple to complex

standard pharmacy procedures. Third, the generic DES resource recommendations were

converted to square footage estimates for renovation planning purposes.

4.1 Specific Discrete Event Simulation

The specific DES was built to model the current workflow of the PM outpatient pharmacy. The

reason this site was selected for the specific DES was two-fold. First, PM had the most complex

standard procedures out of any of the other outpatient pharmacies observed at UHN. A double-

check is built into their procedures to ensure the correct dose and quantity of oral chemotherapy

drugs are dispensed. By building the highest level of complexity into the specific DES,

pharmacies with simpler procedures could be easily modelled by eliminating steps. Second, the

PM pharmacy will be renovated as part of the PMST project this year. This was an opportunity

to introduce the concept of simulation models to the pharmacy management team using a process

they were familiar with, thereby gaining their buy-in and confidence in the model. In the next

section, the model design, main input parameters, model outputs and validation of the specific

DES will be discussed.

4.1.1 Model design

The specific DES, shown in Figure 5, starts with the patient arriving at the pharmacy to drop off

a prescription in-person or via fax/phone and ends with the patient receiving their filled

prescription on either the same day or a later date. After the Take-in step of the process, the

work item is duplicated in the simulation to represent the patient in the waiting room as one work

item and the prescription flowing through the pharmacy in a basket as another work item. When

the prescription is ready and the patient is present, the work items are assembled at the Will Call

step or the Edit step of the process. The simulation excludes supporting operational tasks

21

performed by the inventory technician, billing clerk, cashier and medication reimbursement

specialists. These supporting operational tasks include inventory replenishment, billing

activities, delivery packaging activities and medication reimbursement consultation.

Figure 5. Specific Outpatient Pharmacy DES model of Princess Margaret Cancer Centre

The simulation takes into account patients arriving at the pharmacy to drop off a prescription as

well as patients arriving to pick up prescriptions dropped off the previous day. If the prescription

is not ready at Will Call when a patient arrives to pick up their prescription, then the patient is

expected to wait in the waiting area approximately 15 minutes before returning to the Cashier

booth to ask for an update on their prescription. In this situation, the cashier can spend up to 10

minutes speaking with the patient and physically searching the pharmacy for the basket with the

prescription to put a “Waiting” tag on it. The model also considers prescriptions that need to be

reworked. Rework is identified at Check and Edit and sent back to a technician at Order Entry or

a pharmacist at Pre-Edit, depending on the issue that needs to be resolved. Prescriptions are

prioritized from earliest to latest estimated pick-up time. Patients physically line up at the Take-

in and Cash counters and are, therefore, prioritized by first in first out.

The pharmacy is open Monday to Friday and accepts prescription drop-offs from 9:00AM until

5:30PM. Pharmacist counselling booths remain open until 6:30PM or until the last same day

patient is dispensed their medication. Overtime is captured as pharmacist hours spent working

past 6:30PM. Technicians are unionized and do not work overtime.

22

The specific DES has nine main input parameters, listed in Table 2. Data was collected on these

parameters from various data sources such as Kroll pharmacy patient records, q-matic ticket

machine reports, self-reported time studies and observation. Patients pick up a ticket number

from the q-matic machine to maintain their order in the queue. Pharmacists use the intercom to

call patients to the take-in booth by ticket number rather than patient name to ensure

confidentiality.

Input parameters

Data source

Sample size

Distribution Adj R

2

p-value

Chi-squared

K-S A-D

Drop-off Patient arrivals

q-matic n=8,748 Poisson

Rx count on prescription

Kroll n=37,135 Empirical

Take-in service time

q-matic n=8,748 Lognormal (meanlog 1.797, sdlog 0.597)

0.0005 0.21 0.166

Pre-edit service time

Observation Average (2 min)

Order Entry service time

Time study n=153 Fixed μ nRx= 1.55335*(n), where n=Rx count

0.95

Filling service time

Time study n=154 Exponential μ nRx= 3.0579*(n) -0.8133, where n=Rx count Residual λ= 0.89535

0.95 0.5379 6.361e-07

7.792208e-06

Check service time

Time study n=151 Fixed μ nRx= 1.51004 *(n) -0.51025, where n=Rx count

0.96

Edit service time Time study n=112 Exponential (μ min= 5 min, Residual λ= 0.119786)

< 2.2e-16

0.12 0.10

Counseling service time

Observation Exponential (μ min= 10 min, Residual λ= 0.119786)

< 2.2e-16

0.12 0.10

Cash out service time

Observation Average (2 min)

Table 2. Input parameters for Princess Margaret Specific DES

23

First, a histogram was plotted of the sample data collected for each parameter to identify the

data’s distribution. The left graph of Figure 6 shows the histogram of the take-in service time

data and the lognormal probability distribution function (pdf). Second, a Q-Q plot was generated

to compare the theoretical distribution to the data sample. The right graph of Figure 6 shows the

Q-Q plot of the take-in service time data. There are notably many data points at 20 minutes

because service times were reported by q-matic as the average service time in discrete 20-minute

intervals. Therefore, the maximum service time for take-in would be capped at 20 minutes.

Tickets taken after closing that were not processed expire at the end of day and were excluded

from the sample data. From further analysis of the collected data, Order Entry, Filling and Check

service times were found to be linearly correlated with the number of drugs on a prescription that

can varying from a minimum of 1 drug to 34 drugs. For these steps, a statistically significant

linear regression model was created.

Figure 6. Lognormal PDF and histogram of Take-in service times (left), Q-Q plot of Take-in service times (right)

There are two variables that can change the path a patient takes: pick-up time, which can be

categorized as same day or next day and mandatory pharmacist counselling. Pick-up time is

modeled as a function of the expected wait time as described in Section 2, Figure 3. It was

extrapolated from the data that the longer the expected wait time the lower the percentage of

patients willing to wait for same day service. Pharmacist counselling is required for patients

0 5 10 15 20 25 30

05

10

15

20

25

mydata$ServiceTime

sim

data

0 5 10 15 20 25 30

0.0

00.0

50.1

00.1

5

Lognormal pdf and histogram

xhist

yhis

t

24

starting new prescriptions or patients prescribed targeted agents or controlled substances and

narcotics. The percentage of patients that require counselling is an additional input into the

model. At PM, this percentage is 70%.

There are three main outputs of the model that ensure the pharmacy processes are designed to

meet the pharmacy team’s goals. The first is the average same day patient wait time in minutes

for every three-hour interval from 9:00AM to 8:00PM of every weekday. The second is the

resource utilization rate of pharmacists and technicians. The third is the percentage of patients

dropping off prescriptions who are served the same day. Figure 7 and Table 3 shows all three

outputs of a sample run of the specific DES. The simulation was run for 65 trails with a one-

week warm-up period.

Figure 7. Specific DES output of same day wait times by day of week and time of day

Resource Utilization % Pick-up time % of patients

Pharmacists 78% Same day 34% Technicians 90% Next day 66% Overtime (Pharmacist) 5%

Table 3. Specific DES output of resource utilization and same day patient percentages

25

4.1.2 Validation

The specific DES was validated on the three performance metrics. Table 4 shows that the actual

data is within the 95% confidence interval range of the simulation results. There was one notable

variance between the collected data and the simulation results. The maximum same day wait

time collected in the data is double that of the simulation results. This variance was attributed to

an outlier in the data. During the data collection period, a prescription was dropped off in the

morning and pick-up at the end of the day and it is assumed that the patient was not waiting in

the waiting area for eight hours.

Actual Simulation

Performance Metrics Low 95% Range Average High 95% Range

Same day cycle time (min) Avg 82 70.13 83.46 96.78 Max 486 133 231 329 Min 6 25 33 41

Take-in wait time (min) Avg 20 13.26 18.42 23.58

Max 72 55 73 91 Min 0 0 0 0

Same day patient (%) 34% 24% 30% 36% Next day patient (%) 66% 76% 70% 64% Number of patients per week

699 663 689 716

Table 4. Comparison of actual PM data and simulation results of performance metrics

26

4.2 Generic Discrete Event Simulation

The generic DES was based on the specific DES of PM outpatient pharmacy. The purpose of the

generic model is to estimate the appropriate capacity required to achieve a target wait time. The

generic model simulates the steps taken in dispensing medication in pharmacy that follow a

specific sequence. The model also allows the modeler the flexibility to incorporate automation in

dispensing, perform the clinical check by the pharmacist are various points along the process or

add a technical check by the technician after filling. In the next section, the model design, main

input parameters, model outputs and validation of the generic DES will be discussed.

4.2.1 Model design

The generic DES process flow is shown in Figure 8. The four steps identified as mandatory and

common among all UHN outpatient pharmacies were Take-in, Order Entry, Filling and Check by

a pharmacist. Four additional steps were considered optional and a global variable was setup to

turn on or off the optional steps. The step with the most variation was the pharmacists’ second

clinical check, which can be performed at the Pre-edit, Data Validation, or Edit step. There are

pros and cons to performing the pharmacists’ clinical check too early or too late. If done too

early, the pharmacists may not have all of the information necessary to sign off on the

prescription, resulting in the need for another pharmacist checkpoint at the end of the process. If

done too late, the pharmacist would identify an error after the prescription is already filled

resulting in extensive rework to return incorrect items back to stock or wastage and dispense the

correct drug.

27

Figure 8. Generic DES patient and prescription process flow

The generic DES, shown in Figure 9, was also created to test out the benefit of installing an

automated dispensing machine (ADM). The ADM can dispense a drug in 0.45 minutes

compared to an average of 2 minutes that it would take a technician to manually fill a drug. Part

of the technician’s time is spent traveling through the pharmacy to locate the drug and then

manually count the pills/tablets. The number and type of drugs that can be stored in the ADM

determine the benefit of automation. The percentage of prescriptions that can be fully or

partially automated is a required input of the model. Prescriptions are prioritized from earliest to

latest estimated pick-up time while patients are prioritized first in first out at the Take-in and

Cash counters.

The pharmacy operating hours can be manipulated to match each case study. The cut-off time

when the last same day prescription can be dropped off is manually set to test if longer hours will

alleviate wait times. Overtime is captured as pharmacist hours spent working past the end of

their shift. Technicians are unionized and do not work overtime.

28

Figure 9. Generic Outpatient Pharmacy DES model

The generic DES requires the same nine input parameters as the specific DES created for PM.

Generic service times can be used if the data cannot be collected; however, it is recommended to

collect the patient arrival rate by time of day and day of week or with as much detail as possible.

For each case study in the research, data was collected for every service time through self-

reported time studies, observations or pharmacy system records. The automated dispensing

machine dispensing speed was taken from the machine specifications. The percentage of

automated prescription depends on the drugs selected for automation. The generic DES has the

same outputs as the specific DES.

29

4.2.2 Validation

Demonstrating that two different pharmacies could be modelled validated the generic model. The

generic model was applied the PM and TGH outpatient pharmacies. The generic model was

applied to each pharmacy by first updating the input parameters. The three outputs of the generic

model are same day patient wait times, percentage of same day patients, and prescription count.

The output results of the model were compared against the outputs of the current pharmacy

processes derived from data collected through time studies or subject matter expert input. If the

model results were within the 95% confidence interval range of the current pharmacy outputs,

the model was considered a valid representation of the pharmacy that was modelled. The

pharmacy staff was asked for their input in validating the model.

4.3 Square footage estimates

Square footage estimates of the current workspace were generated from physically measuring the

workstations that were considered part of the core operations of dispensing medication. The

space model, shown in Table 5, multiplies the number of workstations by the approximate

amount of space allotted for each type of workstation. As previously mentioned the current

spaces are crammed, so the future state square footage estimates were based on general

guidelines provided by the architects on the minimum amount of space required for the number

of staff and technology equipment that will be installed. The number of workstations

recommended by the simulation model was inputted into the yellow cells of the space model to

estimate the future square footage needs.

30

Description of use

CURRENT STATE (Status Quo)

FUTURE STATE (10 years)

OPERATIONS (non-patient facing)

NET Sq. ft. Units

Sq. ft./ Unit

NET Sq. ft. Units

Sq. ft./ Unit

Prescription filling area 64.6 3 21.5 96.9 3 32.3 Cytotoxic Chemotherapy counting and filling area 13.6 1 13.6 64.6 2.0 32.3

Manufacturing area 27.3 1 27.3 54.7 1.0 54.7

Sink in Prescription filling area 12.4 1 12.4 27.3 2.2 12.4

Technical Verification area (Check) 21.5 1 21.5 64.6 2.0 32.3

Clinical Data Verification area 21.5 1 21.5 96.9 3.0 32.3 Holding area for unfilled prescription bins 10.4 2.5 4.2 19.3 4.6 4.2

Holding area for filled prescriptions bins 21.9 3.5 6.3 43.5 6.9 6.3

Racks for bagged medication (A-Z) 38.9 1 38.9 73.9 1.9 38.9 Area to store/prepare prescriptions for delivery 2.8 0.5 5.6 33.6 6.0 5.6 Prescription filling area for Specialty medication 16.1 0.75 21.5 129.2 4.0 32.3

Workstation for Specialty medication 20.1 1 20.1 80.4 4.0 20.1

Order Entry workstation 32.3 1.5 21.5 43 2.0 21.5

Call-in workstation 21.5 1.0 21.5

Eye Wash Sink 12.4 1.0 12.4

OPERATIONS (patient facing)

NET Sq. ft. Units

Sq. ft./Unit

NET Sq. ft. Units

Sq. ft./Unit

Cashier 63.7 1 63.7 127.4 2.0 63.7

Take-in 63.7 1 63.7 254.7 4.0 63.7 Printer/fax (for Drop-off and Order Entry use) 6.3 1 6.3 18.9 3.0 6.3

Patient/Pharmacist counselling booth 129.1 2 64.5 322.7 5.0 64.5

Mandatory Private room 42.3 1 42.3 42.3 1.0 42.3

Printer/fax (Expanded Scope of Practice) 6.3 1 6.3 6.3 1.0 6.3

Patient line-up area 9.7 1 9.7 38.8 4.0 9.7

q-matic ticket machine 0.0 0 20.8 20.8 1.0 20.8

TOTAL: 625 1,694

Table 5. Space model for estimating square footage per workstation/work area of the current state and future state of PM

31

Chapter 5

5 Results

This section will present the results generated from applying the generic DES to the outpatient

pharmacy at PM and TGH. Each pharmacy site had slightly different goals, so the scenarios for

each case study varied from site to site. Once the final workflow design was determined, a

sensitivity analysis was run on prescription growth.


The PM pharmacy was modeled using detailed input parameters collected to build the initial

specific DES. The pharmacy management team’s two goals for the redesign were to improve

patient experience by reducing the current average wait time of 1 hour and 20 minutes to 1 hour

or less and to increase the percentage of patients receiving same day service. The technology

options considered in this case study were barcode technology and ADM. Every scenario

assumed the implementation of barcode technology because it is a relatively inexpensive upgrade

to the existing Kroll Version 9 pharmacy system. However, the installation of an ADM would

require a significant capital investment, so separate scenarios were run with and without the

installation of an ADM to assess the operational benefits. This case study explored two

workflow scenarios double-checking and triple-checking processes that incorporated the Data

Validation step available in the Kroll Version 9.

Two scenarios were created as possible ways to incorporate the Data Validation step into PM’s

workflow. The first scenario is inserting Data Validation after Entry, but before Filling and

eliminating the Edit, so errors would be identified earlier on in the workflow and extensive

rework would be prevented. Based on staff feedback and concerns with error checking, a second

scenario was built to introduce the Data Validation step without eliminating the Edit step, which

would work as a third check. Both scenarios were simulation with and without the installation of

an ADM.

32

5.1.1 Scenario 1: Double check workflow with barcode technology

In this scenario, the Data Validation step is introduced for a pharmacist to verify and sign off on

the clinical appropriateness of the prescription immediately after Order Entry, but before Filling.

The Edit step in the current process would be eliminated. This would still ensure a double-check

system. The first check is clinical at Data Validation and the second check is technical at Check.

This workflow scenario was simulated with and without the installation of an ADM.

The simulation results in Table 6 show that without automation one additional Filling

workstation and 2 FTE technicians are required to achieve the same day wait time of 60 minutes

or less and serve at least 50% of patients on the same day. The number of staff was varied based

on the variation in demand by day of week. The minimum number of staff is needed on Fridays

and the maximum number of staff is needed on Tuesdays and Wednesdays.

Performance metrics Current workflow Assume 8%

growth

Double check workflow Assume 8% growth

w/Automation w/o Automation

Same day cycle time in minutes

(Low 95% Range, Average, High 95% Range)

Mon 45, 46, 47 40.6, 41.3, 41.9 44.8, 45.6, 46.5 Tue 52, 53, 54 44.8, 45.7, 46.4 48.0, 49.0, 50.1 Wed 56, 58, 60 48.2, 49.3, 50.4 51.3, 52.5, 53.8 Thu 45, 47, 48 43.1, 44.1, 45.1 45.9, 46.9, 47.9 Fri 47, 48, 49 44.1, 45.1, 46.0 47.0, 48.0, 49.0

% of Same vs Next day

Same day patient (%) 53% 56% 54% Next day patient (%) 47% 44% 46%

Resources Pharmacist Min 13, Max 17 Min 11, Max 16 Min 11, Max 16

Technicians Min 13, Max 15 Min 11, Max 13 Min 12, Max 15 Resource utilization rate

Pharmacist 77% 76% 76% Technicians 81% 83% 89% Overtime 4% 6% 6%

Workstation Take-in counter 4 4 4

Pre-Edit 2 0 0 Order Entry 4 3 3 Data validation 0 4 4 Filling 5 4 5 Check 3 3 3 Counsel booths 6 5 5 Cash counter 2 2 2

Estimate square feet for operations 1,915 sq. ft. 1,797 sq. ft. 1,780 sq. ft.

Table 6. PM Generic DES results of double-check workflow scenario with barcode technology

33

5.1.2 Scenario 2: Triple check workflow with barcode technology

In this scenario, the Data Validation step is introduced the same as in Scenario 1, but the final

Edit step by the pharmacist at the end of the process is not eliminated. This new workflow

would effectively have a triple-check system. The first check is clinical at Data Validation by a

pharmacist, the second check is technical at Check by a registered technician, and the third check

is the clinical and technical check at Edit by the pharmacists. This workflow scenario was

simulated with and without the installation of an ADM.

The simulation results in Table 7 show that without automation an additional Filling workstation

is required to meet the 60 minute same day patient wait time target. Compared to the double-

check workflow an additional patient counselling booth was required to accommodate the triple-

check workflow.

Performance Metrics Current workflow Assume 8%

growth

Triple check workflow Assume 8% growth

w/Automation w/o Automation



Mon 45, 46, 47 44.1, 44.8, 45.6 45.7, 46.5, 47.2 Tue 52, 53, 54 50.4, 51.7, 52.9 48.1, 49.1, 50.0 Wed 56, 58, 60 55.3, 56.5, 57.6 51.5, 52.5, 53.4 Thu 45, 47, 48 46.8, 47.7, 48.6 45.4, 46.2, 47.0 Fri 47, 48, 49 47.8, 48.8, 49.8 47.2, 48.1, 48.9



Resources Pharmacists Min 13, Max 17 Min 13, Max 16 Min 13, Max 16


Pharmacists 77% 82% 82% Technicians 81% 78% 78% Overtime 4% 8% 8%




Table 7. PM Generic DES results of triple-check workflow scenario with barcode technology

34

5.1.3 Sensitivity Analysis on prescription demand increase

Based on the results of the double and triple check scenarios, the pharmacy team decided on the

double check workflow for the final design. There was a lot of dialogue on the pros and cons of

each workflow, but it was generally accepted that the triple check was over processing and not

required as long as barcoding technology was implemented to increase accuracy. Sensitivity

analysis was performed on the double check workflow. This was assumed as the lowest possible

growth rate. A growth rate of 4%, 6% and 8% was tested to understand the incremental

resources required to sustain the growth while achieve their goal of same day patient wait time of

60 minutes or less and 50% or greater same day service.

Rx count Patient count Mon Tue Wed Thu Fri Mon Tue Wed Thu Fri

4% growth 409 425 550 383 398 186 193 250 174 181 6% growth 506 524 680 475 499 230 238 309 216 227 8% growth 620 638 834 579 592 282 290 379 263 269

Table 8. Prescription and patient count projection for 4%, 6% and 8% annual growth rate scenarios

Performance metrics Prescription growth rate scenarios 4% growth 6% growth 8% growth



Mon 41, 42, 43 42, 52, 53 40.6, 41.3, 41.9 Tue 46, 47, 49 50, 52, 53 44.8, 45.7, 46.4 Wed 47, 48, 49 51, 52, 54 48.2, 49.3, 50.4 Thu 40, 41, 42 45, 46, 48 43.1, 44.1, 45.1 Fri 51, 53, 54 48, 50, 52 44.1, 45.1, 46.0





Pharmacists 77% 82% 76% Technicians 76% 75% 83% Overtime 0% 0% 6%




Table 9. PM Generic DES results of sensitivity analysis on annual prescription growth rates

35


The TGH pharmacy was modeled using five out of eight steps in the generic DES. A time study

was conducted for two months to collect data on patient arrivals and service times for the eight

input parameters in Table 10. The pharmacy management team’s goal was same day patient wait

time of 30 minutes or less and increased resource utilization. TGH emphasizes the expanded

scope of practice of pharmacists, which includes smoking cessation programs and flu shot

administration. Achieving the 30-minute wait time target was particularly challenging during flu

shot season, as this increases the demand on pharmacists. The DES was developed to determine

the appropriate staffing of the workflow process to get through this seasonal peak in demand.

This case study explored one workflow scenario to change staffing level and skill-mix and

performed a sensitivity analysis on prescription demand increase driven by Hepatitis C patients.

Process Service Time Number of servers available

Resource (in order to preference)

Drop-off patient arrival rate Poisson distribution Rx count on prescription Empirical distribution Take-in 0 min N/A Technician Pre-edit N/A N/A N/A Order Entry 1.44 min/Rx 1 Technician Filling 3.36 min/Rx 3 Technician/Pharmacist Check 2.5 min/Rx 3 Pharmacist Edit N/A N/A N/A Counsel 6 min 2 Pharmacist Cash 2 min 1 Cashier/Technician

Table 10. TGH data for nine input parameters

Actual Simulation

Performance Metrics Low 95% Range Average High 95% Range

Same day cycle time (min) Avg 45 43 45 46 Max 224 256 318 381 Min 3 8.5 8.6 8.7

Same day patient (%) 55% 55% 55.7% 56% Next day patient (%) 45% 45% 44.3% 64% Number of prescriptions per week

436 425 432 440

Table 11. Comparison of actual TGH data and simulation results of performance metrics

36

5.2.1 Scenario 1: Changes in staffing level and skill-mix

This scenario tests the hypothesis of whether sharing the filling task between technicians and

pharmacists would shorten the patient wait time and maximize resource utilization. Three

different scenarios were run compared to the current workflow. In scenario A, the filling task

was assigned to only the technicians, improving the same day patient wait time by 4 minutes, but

did not achieve the target wait time of 30 minutes. In scenario B, the filling task was assigned to

only the technicians and an additional pharmacist shift was added. The same day wait time

reduced further to 33 minutes; however, the pharmacist’s utilization rate dropped from 80% to

69%. In scenario C, the filling task was shared between technicians and pharmacists and an

additional pharmacist shift was added. Scenario C was best, with a same day wait time of 32

minutes and balanced resource utilization. Adding an additional pharmacist does not increase

the square footage required for operations because under the current staffing there are idle Filling

and Check workstations.

Performance Metrics Current workflow

Filling task shared

Scenario A: Filling task not shared

Scenario B: Filling task

not shared +1 pharmacist

Scenario C: Filling task shared +1

pharmacist


Low 95% Range 43 39 32 31 Average 45 41 33 32 High 95% Range 46 43 34 33


Same day patient (%) 55% 57% 57% 57% Next day patient (%) 45% 43% 43% 43%

Resources Pharmacists Min 1, Max 3 Min 1, Max 3 Min 1, Max 4 Min1, Max 4

Technicians Min 1, Max 3 Min 1, Max 3 Min 1, Max 3 Min 1, Max 3 Resource utilization rate

Pharmacists 77% 80% 69% 71% Technicians 81% 79% 80% 76% Overtime 4% N/A N/A N/A

Workstation Take-in counter 1 1 1 1

Pre-Edit 0 0 0 0 Order Entry 1 1 1 1 Data validation 0 0 0 0 Filling 3 3 3 3 Check 3 3 3 3 Counsel booths 2 2 2 2 Cash counter 1 1 1 1

Estimate square feet for operations 761 sq. ft. 761 sq. ft. 761 sq. ft. 761 sq. ft.

Table 12. TGH Generic DES results of different skill-mix and staff levels

37

5.2.2 Sensitivity Analysis on prescription demand increase

This scenario tests the hypothesis of whether the pharmacy has the capacity to dispense

medication to an estimated 400 Hepatitis C patients who will require two refills to be cured.

This would be equivalent to a 5% increase in prescription volume. In scenario A, the growth of

demand was assumed to be 5% in the first year and 1% annual growth for nine years. Additional

resources and one more Order Entry workstations is required to achieve a same day wait time of

37 minutes. Scenario B assumed an annual growth rate of 3% for the next nine years after the

initial 5% growth in the first year.

Performance Metrics Current workflow

Filling task shared

Scenario A: Filling task shared

Scenario B: Filling task not

shared Hep C growth 1%

annual growth Hep C growth 3%

annual growth


Low 95% Range 43 35 35 Average 45 37 36 High 95% Range 46 38 37





Pharmacists 77% 83% 80% Technicians 81% 76% 75% Overtime 4% N/A N/A



Estimate square feet for operations 761 sq. ft. 852 sq. ft. 852 sq. ft.

Table 13. TGH Generic DES results of sensitivity analysis on annual prescription growth rates

38

Chapter 6

6 Discussion

This section will discuss the results generated from applying the generic DES to the outpatient

pharmacy at PM and TGH and summarize the recommendations presented to the pharmacy

management teams. Scenarios were compared against the baseline, which was defined as the

resource and space needed to support the future prescription volume growth with the current

process.


Two scenarios, double check and triple check, were run for PM to compare possible new

workflows for the pharmacy. The number of staff and workstations, and staff schedules were

modified to achieve the goal of same day patient wait times of 60 minutes or less and same day

patient percentage of 50% or greater. The number of recommended staff is higher than the

number of workstations because extra staff is needed to cover staff on lunch break. Lunch

breaks are an hour long and taken between 10:45AM to 1:45PM, which coincides with the peak

time when patients drop off their prescriptions after morning appointments with the clinician.

Both the double check and triple check workflows required fewer staff than the status quo.

However, only the double check workflow increased the same day patient percentage compared

to the status quo; triple check workflow did not. In terms of space requirements, double check

required 100 square feet less and triple check required more space than the status quo. In all

cases, implementing an ADM reduced the technician headcount by two staff. There was only a

small difference of approximately 20 square feet between the scenarios with and without the

ADM. This is because the ADM requires 50 square feet and a technician’s filling counter space

is 32 square feet. In conclusion, the double check process was recommended over the status quo

and an investment to install an ADM was recommended.

39


The first scenario tested whether or not it was more productive to share the task of filling

prescription between the technician and pharmacists. With the current staffing level of 3 FTE

technicians and 3 FTE pharmacists, it is not recommended to share filling tasks during the peak

flu shot season. Only pharmacists can administer flu shots, so allowing pharmacists to

concentrate on the tasks only they can complete reduced the same day patient wait times by 4

minutes. However, if an additional FTE pharmacist was added to the shift, it is recommended to

share filling tasks. The resource utilization decreased to 70%, which suggests that hiring a part-

time pharmacist during the peak hours would be better than a FTE. Unfortunately this was not

an option available to the TGH management staff.

The second scenario verified that TGH can treat approximately 400 new Hepatitis C patients

with an increase of 91 square feet to their operational space. The results of varying the annual

growth rate after the initial 5% surge of Hepatitis C patient referrals suggested that the

operational space would need to expand by 91 square feet to accommodate 1% or 3% of annual

growth. Additional technician and pharmacist shifts would need to be added to support the

growth, but it was concluded that that was sufficient floor space. The model assumed that

pharmacists continue the practice of counselling patients in the extended scope of practice

private booth or in the retail space. The first option is physically very far from the pharmacy and

requires a lot of travelling; this was accounted for in the service time. The second option is not

ideal for patient confidentially. It is recommended that a proper standing counselling booth be

created for the TGH site.

40

Chapter 7

7 Conclusion and Future work

Cancer drugs have been the fastest-growing component of pharmaceutical spending in the last

six years, with an average annual growth rate of 13.1% (Canadian Institute for Health

Information, 2012). The outpatient pharmacy at Princess Margaret Cancer Center (PM),

specializing in dispensing cancer drugs, was faced with the opportunity to redesign and expand

the capacity of the pharmacy to meet the demand. The goals of the redesign were to improve the

patient experience by reducing the current average wait time of 1 hour and 20 minutes to 1 hour

or less and to increase the percentage of patients receiving same day service. There is extensive

literature on renovating existing pharmacy workspaces, but research on future capacity planning

of pharmacy workspaces is sparse.

This research was motivated by the need to redesign an outpatient pharmacy and estimate the

pharmacy’s resource and space requirements in ten years to inform the renovation planning. A

generic discrete event simulation (DES) was developed to test multiple scenarios of workflow

and resource levels. The generic model proved to be useful to PM pharmacy management staff

and architects in the early planning phases of the renovation. The pharmacy was recommended

to expand the operations space from 625 square feet to 1,797 square feet in ten years. The results

of the model were taken into consideration and incorporated into the preliminary architectural

plans. The reusability of the model was demonstrated by applying the model to a second

outpatient pharmacy site at TGH with similar patient wait times and same day service goals. It

was concluded that TGH has ample space to accommodate growth for new Hepatitis C patients,

but the use of the space would need to be reviewed.

For future work, a study involving patient interviews could be conducted to identify the factors

contributing to the patients’ preference to pick up the next day rather than wait. Booking next

day patients that need pharmacist counselling into appointment blocks throughout the week

could smooth patient arrivals. A staff schedule that would match pharmacist and technician shifts

to the new patient arrival profile could be developed. Scenarios could be run to understand the

impact of smoothing next day patient arrivals on same day patient wait times. The pharmacy

41

staff felt that the possibility of patients not returning to the pharmacy the next day at the

promised time could prove booking pharmacist consultation appointments difficult. From the

staff’s experience, they observed that patients are very unpredictable and could arrive earlier or

later than the promised pick-up time. The model can also be applied to the Transplant Outpatient

pharmacy at TGH in preparation for future relocation.

42

Chapter 8

8 References

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47

Appendix A: Kroll Workflow matrix

There are eight standard workflow options available in Kroll Version 9 for two different types of

label paper; laser label and thermal label. The user, based on the need for Packaging, Pharmacist

Data Validation and Will Call Automation, selects the appropriate workflow.

Kroll Workflow options (Version 9) Comments

1 No workflow options Current PM and TGH settings

2 Barcode technology option only [A]

3 Data Validation option only [B]

4 Will call automation option only [C]

5 Barcode technology and Data Validation options [A+B]

6 Barcode technology and Will Call automation options

[A+C]

TGH future state upgrade

7 Data Validation and Will call automation option [B+C]

8 Barcode technology, Data Validation and Will Call

automation options [A+B+C]

PM future state upgrade

A Barcode technology Option – verifies that the correct product has been chosen, and in the

correct pack size to efficiently manage inventory. If more than one pack is required, then each

pack must be scanned individually. When packaging mixtures, the user must scan the UPC of

each ingredient, and it can be configured to have the user enter the Lot # and Expiry Date of each

ingredient.

B Data Validation Option – verifies that the prescription is clinically appropriate on the screen.

The patients profile, prescription image, adjudication results, packaging information, and all

other steps that were completed up to this point are viewable for the pharmacist to review before

approving the prescription. No extra peripheral hardware pieces are required here, unless the

pharmacy wishes to utilize paperless functionality, and electronic signature capture (depending

on provincial legislation).

C Will Call automation Option – tracks when a prescription is picked up and by whom. If the

prescription is not ready yet, this option will provide a status of where the prescription currently

resides in the workflow process. When tracking the identification of who picked up a medication,

if a 2D barcode scanner is connected to the terminal, the barcode on the back of provincial

identification can be used to enter the details.

48

Appendix B: Process flow maps

49

50

51

Appendix C: Simulation screenshots

Sp

ecific

Outp

atie

nt P

ha

rma

cy M

ode

l

52

53

Appendix D: Input data analysis results

Histogram of mydata$TakeIn

mydata$TakeIn

Fre

que

ncy

0 5 10 15 20 25 30

010

20

30

40

50

60

0 5 10 15 20 25 30

05

10

15

20

25

mydata$ServiceTime

sim

data

0 5 10 15 20 25 30

0.0

00.0

50.1

00.1

5

Lognormal pdf and histogram

xhist

yhis

t

54

Call: lm(formula = mydata$ServiceTime ~

mydata$Rxcount)

Residuals:

Min 1Q Median 3Q Max

-1.5501 -0.6766 0.2167 0.3234 1.2167

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 0.12328 0.08031 1.535

0.127

mydata$Rxcount 1.55335 0.02844 54.616

<2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05

‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.6129 on 151

degrees of freedom

Multiple R-squared: 0.9518, Adjusted R-

squared: 0.9515

F-statistic: 2983 on 1 and 151 DF, p-value: <

2.2e-16

Histogram of mydata$Entry

mydata$Entry

Fre

que

ncy

5 10 15

010

20

30

40

5 10 150

510

15

20

25

mydata$ServiceTime

sim

data

5 10 15

0.0

0.1

0.2

0.3

0.4

0.5

Exponential pdf and histogram

xhist

yhis

t

55

2 4 6 8 10 12 14

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Fitted values

Resid

uals

lm(mydata$ServiceTime ~ mydata$Rxcount)

Residuals vs Fitted

149150

126

-2 -1 0 1 2

-2-1

01

2Theoretical Quantiles

Sta

ndard

ized

resid

uals


Normal Q-Q

149 150

126

2 4 6 8 10 12 14

0.0

0.5

1.0

1.5

Fitted values

Sta

ndard

ized r

esid

uals


Scale-Location

149150

126

0.00 0.02 0.04 0.06 0.08 0.10

-3-2

-10

12

Leverage

Sta

ndard

ized

resid

uals


Cook's distance0.5

Residuals vs Leverage

149150

153

56


mydata$Rxcount)

Residuals:


-1.4763 -0.4184 -0.2447 0.6974 1.7553

Coefficients:


(Intercept) -0.81325 0.13951 -5.829 3.22e-08

***

mydata$Rxcount 3.05791 0.05768 53.017 <

2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’

0.1 ‘ ’ 1

Residual standard error: 0.9241 on 152 degrees of

freedom


squared: 0.9484


2.2e-16

Histogram of mydata$Filling

mydata$Filling

Fre

que

ncy

0 10 20 30 40 50

010

20

30

40

50

5 10 15 20

010

20

30

mydata$ServiceTime

sim

data

5 10 15 20

0.0

00.0

50.1

00.1

50.2

00

.25

0.3

00.3

5


xhist

yhis

t

57

5 10 15

-10

12

Fitted values

Resid

uals


Residuals vs Fitted

707172

-2 -1 0 1 2

-10

12

Theoretical Quantiles

Sta

ndard

ized

resid

uals


Normal Q-Q

707172

5 10 15

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Fitted values

Sta

ndard

ized r

esid

uals


Scale-Location

707172

0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07

-2-1

01

2

Leverage

Sta

ndard

ized

resid

uals


Cook's distance


152153

144

58


mydata$Rxcount)

Residuals:


-2.57010 -0.50983 0.00021 0.49017 2.00021

Coefficients:


(Intercept) -0.51025 0.10159 -5.023

1.38e-06 ***

mydata$Rxcount 1.51004 0.02476 60.988

< 2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05

‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8998 on 156

degrees of freedom


squared: 0.9595


2.2e-16

Histogram of mydata$Check

mydata$Check

Fre

que

ncy

0 5 10 15 20 25 30

010

20

30

40

50

0 5 10 15 20 25 300

510

15

20

25

mydata$ServiceTime

sim

data

0 5 10 15 20 25 30

0.0

00.0

50.1

00.1

50.2

00.2

50

.30


xhist

yhis

t

59

0 5 10 15 20 25 30

-3-2

-10

12

Fitted values

Resid

uals


Residuals vs Fitted

1489293

-2 -1 0 1 2

-3-2

-10

12

Theoretical Quantiles

Sta

ndard

ized

resid

uals


Normal Q-Q

14892 93

0 5 10 15 20 25 30

0.0

0.5

1.0

1.5

Fitted values

Sta

ndard

ized r

esid

uals


Scale-Location

1489293

0.00 0.05 0.10 0.15 0.20

-3-2

-10

12

Leverage

Sta

ndard

ized

resid

uals


Cook's distance

1

0.5

0.5

1


157156

148

60

Histogram of mydata$ServiceTime

mydata$ServiceTime

Fre

que

ncy

0 10 20 30 40 50

05

10

15

20

25

30

0 10 20 30 40 50

010

20

30

40

50

mydata$ServiceTime

sim

data

0 10 20 30 40 50

0.0

00

.02

0.0

40.0

60

.08

0.1

00

.12

0.1

4


xhist

yhis

t

61

Appendix E: Pharmacy Floor plans

Case study #1: Princess Margaret Cancer Centre

Dispensing area

Patient counselling booths

Take-in booth

Staff Interaction area

Cashier

Over-the-Counter Retail space

Office space

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Case study #2: Toronto General Hospital

Dispensing area

Staff Interaction area

Office space

Office space


Cashier


Over-the-Counter Retail space

Take-in booth

Date post:	10-Oct-2020
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