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Eindhoven University of Technology

MASTER

Improving the admission and capacity planning in a dermatology oncology outpatient clinic

Romero, H.L.

Award date:2010

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https://research.tue.nl/en/studentthesis/improving-the-admission-and-capacity-planning-in-a-dermatology-oncology-outpatient-clinic(faa1b949-4a85-4482-9161-23d13e2d9b43).html

Eindhoven, december 2009

Student identity number 0641533

in partial fulfillment of the requirements for the degree of

Master of Science

in Operations Management and Logistics

Supervisors:

Dr.ir. N.P. Dellaert, TU/e, OPAC

Dr.ir. M. Jansen-Vullers, TU/e, IS

Improving the admission and

capacity planning in a dermatology

oncology outpatient clinic

by

Heidi L. Romero

ii

TUE. Department Industrial Engineering & Innovation Sciences

Series Master Theses Operations Management and Logistics

Subject headings: healthcare, admission planning, resources allocation, simulation

iii

Management Summary

Introduction

This study was conducted in the dermatology oncology outpatient clinic at the Catharina

hospital. The arrival rate of patients diagnosed with basal cell carcinoma has been constantly

increasing during the last years. Due to the increased demand of patients diagnosed with basal

cell carcinoma and the limited availability of resources for treatment, the average waiting time of

patients for treatment varies from 2 weeks until 3 months. The clinic administration has been

looking for alternatives to improve their services, i.e. reducing the time that patients spend after

they are diagnosed with skin cancer until they receive their treatment.

A previous study presented by a colleague student proposed a redesigned one-day-

treatment (ODT) process in which patients have their consult meeting, diagnosis and treatment

all during one day. A qualitative evaluation of this redesign was shown using the devil’s

quadrangle (Reijers and Mansar, 2005). This proposal was considered as very attractive to the

clinic, considering that it is aligned with their vision. However, the clinic is interested in a

quantitative evaluation of this redesign and the advantages for patients. This leads to the

following goal for this project:

Methodology

The methodology followed to answer the research questions stated in this study included the

following:

1. To conduct a literature review identifying the potential factors that could influence the

implementation of a process redesign, from a logistic perspective. The factors identified

were the admission and capacity planning.

2. To make an analysis of the capacity using the demand-supply model for the analysis of

hospital processes described in Vissers and Beech (2005). This approach facilitates the

identification of the level of mismatch between demand and supply, providing some

insights into areas of improvement. One conclusion is that it is feasible to implement the

redesigned ODT process in the clinic for patients referred for Photodynamic therapy

(PDT) and Excision, considering that the current capacity level is sufficient to fulfill the

current demand. Also, the significant factors for this implementation are: the admission

rule, the percentage of appointment slots held open for PDT - ODT patients, and the

allocation of capacity throughout the week for Excision-ODT patients. However, in this

analysis the inflow of patients and the time durations for each activity were assumed to

be deterministic, which does not correspond to the real situation in the clinic.

The aim of this study is twofold, both qualitative and quantitative:

(1) Qualitative: to identify the factors hindering the possibility to implement the redesigned,

one-day treatment (ODT) process

(2) Quantitative: to propose an alternative setting that supports the implementation of the

redesigned ODT process, and to measure the advantages of the new implementation in terms

of average throughput time

iv

3. Develop a simulation model to represent the actual process followed by patients at the

clinic and the proposed redesign. Simulation is one of the best alternatives when

comparing different scenarios by incorporating variability to the activities durations and

demand (Lee and Asllani, 2001).

4. Define scenarios to be evaluated, combining the different factors identified as significant

in the previous analysis of capacity conducted.

5. Analyze the results gathered from different alternative scenarios to conclude about the

feasibility of implementing the redesigned ODT process and the best setting from the

scenarios proposed.

Conclusions

Finally, this methodology conducted to answer the research questions stated in this project:

• Research question #1: What changes should be made in the dermatology oncology

outpatient clinic to treat the new patients diagnosed with BCC on the same day of the

diagnosis? What conditions should be fulfilled?

In order to implement the redesigned ODT process, the clinic should align their planning

for treatment with the admission of patients for consult meeting. This alignment is necessary,

considering that new patients who are arriving every day for consultation could decide whether

they receive their treatment on the same day of the diagnosis or not, and the clinic should have

the resources available to fulfill that demand.

Another condition that should be fulfilled is the availability of the results from the

pathology department within the same day to complete the process. This could be considered as

the most critical factor to implement the redesigned ODT process with the drawback that it is an

external resource and out of the scope of this study. This suggests the need to discuss the

redesign with the chief of this external party, stressing the advantages of this implementation for

the patients.

• Research question #2: How could the conditions required to implement the redesigned

ODT process be fulfilled?

To fulfill the condition of an alignment between the demand and supply, the results

showed that one significant factor is the percentage of open appointment slots held open to treat

ODT-patients. This percentage is estimated identifying the expected demand of services from

ODT-patients and the average percentage of slots held open for treatment, both for Excision and

PDT. This percentage changes with changes in the demand, considering that the resources

available remain the same. For the current demand, 27.5% of the slots should be held open,

which can be translated to 2 appointment slots for PDT and 3 for Excision every day.

To extend the option to choose for treatment on the same day of diagnosis, another factor

that should be considered is the admission rule. The current admission rule is that every doctor

receives a maximum of five new patients for consultation during one block (half a day: morning

or afternoon). However, this rule only aims to balance the workload between doctors but is not

patient-centered. The proposed rule includes all the appointment slots for new patients during the

v

morning. This aims to extend the option to choose the redesigned ODT process to all new

patients, under the actual assumption that the sample biopsy should be available before 11:30 for

the evaluation at the pathology department and further answer in less than two hours.

• Research question #3: What are the quantitative advantages or disadvantages gained

from the implementation of the redesigned ODT process?

The advantages of implementing the redesigned ODT process can be measured in terms

of the time performance. The average throughput time for new patients could be reduced to 90%

for Excision and 95% for PDT, compared to the current situation. This redesign also adds

flexibility to the process, which allows patients to decide whether they want to be treated on the

same day or follow the regular process. To implement the redesigned ODT process is mandatory

to receive the results from pathology in less than two hours, which nowadays takes

approximately two weeks. This improvement could increase the cost of this service for the clinic.

• Research question #4: What is the effect of the implementation of the redesigned ODT

process on the throughput time for other patient groups (SCC, melanoma, others),

considering that the doctor, nurses and operating rooms are shared by all BCC-patients?

The effect of implementing the redesigned ODT process for new patients also reduces the

average throughput time for control patients treated with Excision with approximately two

weeks. The effect for control patients treated with PDT is not affected by the actual admission

rule in which only 27.5 % of the appointment slots are held open for ODT-patients, but it is

significantly affected when this percentage is increased to 50%. The analysis is performed

independently for patients treated by Excision and PDT because their resources are independent.

The only resources shared are those between Excision and MOHs. The average throughput time

for patients treated with MOHs does not show a significant difference between the current

situation and the proposed scenario, measured in approximately 1.5 days. This difference is not

significant considering that the current throughput time is greater than two months.

vi

Preface

This report is a master thesis for the study of Operation Management and Logistics in the Faculty

of Industrial Engineering & Innovation Sciences at the Eindhoven University of Technology.

This master thesis has been conducted in the Dermatology oncology outpatient clinic of the

Catharina Hospital in Eindhoven. I would like to thank several people who have aided

significantly in making this master thesis possible.

I would like to thank Dr. Gertruud Krekels at the dermatology oncology outpatient clinic for her

support throughout the project. I would like to thank specialists, doctors, nurses and assistants

that helped me to get to know the process, for their openness and willingness to provide the

information.

Also, I am greatly indebted to my supervisors, Dr. Nico Dellaert and Dr. Monique Jansen-

Vullers, for their useful criticism and guidance throughout this project. Next, I would like to

thank Dr. Hajo Reijers for his noble contribution to improve this report.

Finally, I would like to thank God for all the blessings received through my family and friends. I

am thankful for the opportunity to share this journey with all of you. Finally, I would like to

dedicate this thesis to my dearly loved husband, Anthony, who has always supported my dreams.

Heidi Romero

December, 2009

vii

Table of Contents

1 INTRODUCTION ......................................................................................................................................... 1

1.1 Research Environment.......................................................................................................... 1

1.2 Problem Statement ............................................................................................................... 2

1.2.1 Current situation .................................................................................................................. 2

1.2.2 Redesigned One-day-treatment (ODT) process ..................................................................... 2

1.3 Research questions and project goals .................................................................................... 3

1.4 Report Outline ...................................................................................................................... 4

2 LITERATURE REVIEW .............................................................................................................................. 5

2.1 Introduction .......................................................................................................................... 5

2.2 Study Synthesis .................................................................................................................... 6

2.2.1 Admission Planning .............................................................................................................. 6

2.2.2 Capacity Planning ................................................................................................................ 8

2.2.3 Integrated Approach........................................................................................................... 12

2.3 Concluding from the literature review................................................................................. 14

3 CAPACITY ANALYSIS ............................................................................................................................ 16

3.1 Introduction ........................................................................................................................ 16

3.2 Demand-supply model for clinical processes ...................................................................... 16

3.2.1 Inflow of patients ................................................................................................................ 16

3.2.2 Treatment Profiles .............................................................................................................. 17

3.2.3 Process description and resources requirement per patient group ...................................... 17

3.2.4 Performance estimation for actual process ......................................................................... 20

3.3 Admission and capacity planning ....................................................................................... 23

3.3.1 Planning for PDT ............................................................................................................... 23

3.3.2 Planning for Excision ......................................................................................................... 25

3.4 Concluding from the process analysis ................................................................................. 27

4 MODEL DEVELOPMENT ........................................................................................................................ 29

4.1 Simulation model description ............................................................................................. 29

4.1.1 Input Data .......................................................................................................................... 29

4.1.2 Model flow logic ................................................................................................................. 32

4.1.3 Performance measures ....................................................................................................... 32

4.1.4 Verification and Validation ................................................................................................ 32

4.2 Modeling the redesigned one-day treatment (ODT) process ................................................ 33

4.2.1 Model flow logic ................................................................................................................. 33

4.2.2 Verification and Validation ................................................................................................ 33

4.3 Concluding from the modeling ........................................................................................... 34

viii

5 SCENARIOS ............................................................................................................................................... 35

5.1 Introduction ........................................................................................................................ 35

5.2 Selection of factors ............................................................................................................. 35

5.2.1 Admission rule ................................................................................................................... 35

5.2.2 Percentage of open slots available for PDT and Excision ................................................... 36

5.2.3 Resources allocation for Excision ....................................................................................... 36

5.3 Definition of scenarios ....................................................................................................... 37

5.3.1 Scenario # 1 ....................................................................................................................... 38

5.3.2 Scenario #2 ........................................................................................................................ 38

5.3.3 Scenario #3 ........................................................................................................................ 38

5.3.4 Scenario #4 ........................................................................................................................ 39

5.3.5 Scenario #5: ....................................................................................................................... 39

5.3.6 Scenario #6: ....................................................................................................................... 39

5.3.7 Scenario #7: ....................................................................................................................... 39

5.3.8 Scenario #8: ....................................................................................................................... 39

5.4 Concluding from scenarios ................................................................................................. 39

6 EXPERIMENTAL RESULTS .................................................................................................................... 41

6.1 Introduction ........................................................................................................................ 41

6.2 Results ............................................................................................................................... 41

6.2.1 Throughput time for patients treated by Excision ................................................................ 41

6.2.2 Throughput time for patients treated by PDT ...................................................................... 43

6.3 Analysis ............................................................................................................................. 44

6.3.1 Comparative analysis of scenarios for Excision .................................................................. 45

6.3.2 Comparative analysis of scenarios for PDT ........................................................................ 46

6.3.3 Sensitivity analysis ............................................................................................................. 46

6.4 Concluding from experiments ............................................................................................. 47

7 CONCLUSIONS AND RECOMMENDATIONS ..................................................................................... 47

7.1 Conclusions ........................................................................................................................ 48

7.2 Limitations and further research ......................................................................................... 49

REFERENCES ................................................................................................................................................... 51

LIST OF ABBREVIATIONS ............................................................................................................................ 53

APPENDICES .................................................................................................................................................... 54

Appendix A - Organizational Chart of Catharina Hospital.................................................................................. 54

Appendix B- Master Scheduling for doctors and Specialists ................................................................................ 55

Appendix C- Elaboration of the simulation model in Arena ................................................................................. 58

Appendix E- Modeling the redesigned ODT process ............................................................................................ 78

Appendix G- Defining the warm-up period .......................................................................................................... 80

Appendix H - Admission rule for actual and proposed ........................................................................................ 81

Appendix I – Resources allocation proposed by OpQuest for scenarios 2, 4, 6 and 8 ......................................... 83

ix

Table of Figures

Figure 1-Devil’s quadrangle for the redesigned ODT process ................................................................. 3

Figure 2-Overview of literature search and selection ............................................................................... 6

Figure 3-A three-phase hierarchical approach for operating theater schedules (Testi et al., 2007) ........ 12

Figure 4-A scheduling problem for the regional health center (Lee and Asllani, 2001) .......................... 13

Figure 5- Flow chart of activities followed by new patients diagnosed with BCC in the dermatology

oncology outpatient treatment ......................................................................................................... 17

Figure 6- Scenarios scheme, derived from the combination of three factors ........................................... 38

Figure 7- Confidence interval for the throughput time for new patients treated with Excision ................ 41

Figure 8- Throughput time for new and control patients treated with Excision ....................................... 42

Figure 9- Confidence interval for the throughput time for new patients treated with PDT ...................... 43

Figure 10- Throughput time for new and control patients treated with PDT ........................................... 44

Figure 11-Arena flow chart for patients diagnosed with BCC at the oncology clinic .............................. 59

Figure 12-Submodel for making an appointment for BCC patient’s treatment ........................................ 61

Figure 13-Submodel for the Photo Dynamic Therapy (PDT) .................................................................. 62

Figure 14-Submodel for the MOHs Procedure ....................................................................................... 63

Figure 15-Submodel for the Excision ..................................................................................................... 63

Figure 16- Process to make an appointment for the consultation of a new patient part 1 ........................ 69

Figure 17- Process to make an appointment for the consultation of a new patient part 2 ........................ 70

Figure 18- Process to make an appointment for the consultation of patients- part 3 ............................... 71

Figure 19- Process to make an appointment for the consultation of a control patient part 1 ................... 72

Figure 20- Process to make an appointment for the consultation of a control patient part 2 ................... 73

Figure 21- Process to make an appointment for the Photo Dynamic Therapy treatment part 1 ............... 74

Figure 22- Process to make an appointment for the Photo Dynamic Therapy treatment part 2 ............... 75

Figure 23-Changes in pathological test for the redesigned ODT process ............................................... 78

Figure 24- Changes in submodel for making an appointment for BCC patient’s treatment for the

redesigned ODT process ................................................................................................................. 78

Figure 25- Sensitivity analysis of the probability distribution for the arrival pattern .............................. 79

Figure 26- Average throughput time for BCC patients treated with Excision and PDT ........................... 80

x

Table of Tables

Table 1-Overview of the resources available at the clinic ....................................................................... 18

Table 2-Resource use per activity .......................................................................................................... 19

Table 3-Resource use per patient group in the actual process ................................................................ 20

Table 4-Match between weekly demand and supply per resource type in the clinic ................................. 20

Table 5- Daily resource use per patient group with the redesigned ODT process ................................... 22

Table 6- Expected daily demand of resources PDT-patients by the redesigned ODT process .................. 24

Table 7- Estimated percentage of appointment slots open for PDT-patients by the redesigned ODT

process ............................................................................................................................................ 24

Table 8- Estimated resources use by patients treated with MOHs and Excision ...................................... 26

Table 9-Match between daily demand and supply per resource type for MOHs and Excision ................. 27

Table 10-Parameter for the Poisson distribution .................................................................................... 30

Table 11 – Output generated in Excel for the model verification ............................................................ 33

Table 12 – Current allocation of resources for MOHs and Excision ....................................................... 36

Table 13- Weekly schedule of doctors for consultation ........................................................................... 55

Table 14- Weekly schedule of specialists for consultation ...................................................................... 55

Table 15 - Weekly schedule for MOHs and Excision .............................................................................. 56

Table 16- Weekly schedule of doctors for treatment ............................................................................... 56

Table 17- Weekly schedule of specialists for treatment ........................................................................... 57

Table 18-Description of variables .......................................................................................................... 64

Table 19-Description of Attributes ......................................................................................................... 65

Table 20 – Current allocation of appointment slots for consult meetings during the morning ................. 81

Table 21- Current allocation of appointment slots for consult meetings during the afternoon ................. 81

Table 22 – Proposed allocation of appointment slots for consult meetings during the morning ............... 82

Table 23 – Proposed allocation of appointment slots for consult meeting during the afternoon .............. 82

Table 24 – Optimal allocation of resources for MOHs and Excision for scenario 2 ................................ 83




1

1 INTRODUCTION Hospitals and other health care institutions provide human and capital resources to

maintain and improve their patients’ life. However, there is a trade-off between the involved

service level and costs, which has motivated health care institutions to find creative ways to

reduce costs with a limited impact on their service level. Although hospitals are a special type of

service organization, they can borrow some managerial practices from other industries more

operationally effective with similar challenges.

This research will present the application of operations management practices in a

dermatology oncology outpatient clinic at the Catharina Hospital, which is a specialized center

for the treatment for skin cancer, located in Eindhoven. The operations management functions in

health organizations include the planning, coordination and control of their resources in order to

improve quality and reduce costs (Langabeer, 2008). This study will focus on the planning

function, specifically analyzing the admission and capacity planning. The admission planning

includes balancing the workload of admissions considering the resource requirements for

different patient categories. While the capacity planning, includes several decisions including

optimizing the utilization of a critical resource, balancing the capacities of different resources

required simultaneously and the allocation of shared resources.

1.1 Research Environment

This research is conducted in the dermatology oncology outpatient clinic at the Catharina

hospital, located in Eindhoven. This is one of the top specialized centers in the Netherlands for

skin cancer treatment. They receive patients with different types of skin cancer: malignant

melanoma, squamous cell carcinoma (SCC) and basal cell carcinoma (BCC). The basal cell

carcinoma is considered the most common form of non melanoma skin cancer around the world

and the most reported malignancies in Caucasian populations, with an incidence of

approximately 30,000 cases diagnosed per year in the Netherlands (Essers et al. 2006). These

tumors destroy the tissue extensively and account for a considerable healthcare expenditures

worldwide (Thissen et al. 1998).

There are four different treatment interventions for patients diagnosed with basal cell

carcinoma (BCC) offered in the dermatology oncology outpatient clinic in study, depending on

the type of cancer and location. The treatments offered are described as follows:

1) Self-treatment: for superficial basal cell carcinoma. Patients receive the instructions for

the application of a special cream at home.

2) Photodynamic therapy (PDT): is another option for the treatment of superficial basal

cell carcinoma. For this treatment is necessary to apply a special cream on the affected

area for a further exposure to infrared light to reduce the cancer spot.

3) Excision: used to remove solid spots at different locations other than the face. It is even

possible to perform excisions in the face only if the cancer spot is small or does not have

a significantly negative effect in the esthetic.

4) MOHs: is a revolutionary procedure used to excise cancer spots with minimum skin

damage and with the highest guarantee (almost 99%) at the end of the procedure that the

excise spot is cancer-free. It is recommended for spots located in the face or for larger

cancer areas with anesthesia (Mikhail, 1991).

2

1.2 Problem Statement

1.2.1 Current situation

During the last years, the arrival of patients diagnosed with basal cell carcinoma to the

dermatology oncology outpatient clinic has been growing in approximately 5% per year. This

situation supports worldwide statistics presented by Essers et al. (2006), indicating a growth in

the occurrence of this specific type of skin cancer.

However, the resources available at the clinic to provide the treatment have not increased

in the same proportion. Some critical resources, such as the specialists, doctors and nurses, are

shared by all the patients following different care paths inside the clinic. For instance, the doctors

and the specialists are scheduled to perform different activities during the week, including

consultation time or supervision in case of the specialists, time for excisions, for MOHs and even

some hours for research.

The clinic has an agreement with the hospital to receive a minimum amount of new

patients per year. This amount started with 6,500 new patients in 2005, and has increased by

1,000 new patients every year. The planner used this amounts to allocate the capacity required

every year to receive those new patients and continue treating the control ones. A “control

patient” as called during this report, is a patient who was diagnosed and treated for skin cancer

and requires further consultations in the clinic to evaluate the condition of the cancer. Basically

the doctors need to check the condition of the cancer. Those checks are performed approximately

every six months during the first two years after the treatment. If the patient is recurrent and

requires another treatment this cycle starts all over again.

Considering the current situation, with a growing demand, fixed capacity level and shared

resources, less capacity is available every year for patient’s treatment. This situation has affected

the waiting time required for a new patient to start the treatment after the diagnosis. Nowadays,

this time varies significantly among the different types of treatments. It is approximately two

weeks for the excision and the PDT, but up to three months for the MOHs procedure. The

administration of the clinic has the vision of offering the option to BCC-patients to have their

consultation and treatment during the same day. It suggests an improvement in the service level

from the patient perspective with a significant reduction on their average throughput time. This

situation encouraged the clinic to look for some alternative options to optimize their operations

in order to improve their service, reducing patient’s waiting time.

1.2.2 Redesigned One-day-treatment (ODT) process

A colleague student from the Information System Group at the Faculty of Industrial

Engineering & Innovation Sciences analyzed their treatment processes and developed some

redesign scenarios, one of them suggesting that the consult meeting, diagnosis and treatment

could all be performed in one day (Groot, 2009), named in this report as the redesigned one-day-

treatment (ODT) process. This redesign was considered very promising for the clinic because it

is aligned with their future vision of the clinic.

A qualitative evaluation of this redesign was performed using the devil’s quadrangle

(Reijers and Mansar, 2005). The devil’s quadrangle is used as a framework in which the effect of

redesigns is evaluated using four different dimensions: quality, time, cost and flexibility. The

idea is to describe the trade-off underlying every redesign measure, and to identify how each

3

criterion from the devil’s quadrangle can be made operational. It was considered that the quality,

flexibility and time performance dimensions are enhanced with the redesigned ODT process (See

Figure 1). Groot (2009) used a framework proposed by the Dutch Council of Public Health

(NRV,1990) to identify the quality aspects that could be improved in healthcare organizations

and a model developed by Asselman (2008) for the cost aspects. Using those frameworks, it is

possible to clearly define which aspects of each dimension could be enhanced by the

implementation of the redesigned ODT process.

Figure 1-Devil’s quadrangle for the redesigned ODT process

The quality of care is improved by offering a

process which is more patient-centered. With the

redesigned ODT process, patients do not need to wait

for weeks before been informed about their condition

and further treatment. The most obvious advantage of

this redesign is the improvement in the time

dimension. By definition, it implies that the actual

throughput time for patients is one day. The

throughput time is defined as the time between

patients’ arrival at the clinic for their consult meeting

until finish their treatment. In this definition is not

considered the time that patients need to wait

between their call for an appointment until their

consultation meeting.

The cost dimension could be negatively affected if the implementation of this redesign

requires extra capacity available for the treatment. However, to implement this redesign is

required to have the results from pathology on the same day, which could increase the provision

of this service. Finally, the redesigned ODT process provides a type of flexibility, called by

Schonenber et al. (2007) as flexibility by design. This type of flexibility is made operational in

this case offering to patients the option to decide if they want to be treated during the same day

or make an appointment in the further weeks. In the actual process, patients do not have the

option to choose.

1.3 Research questions and project goals

Besides the qualitative evaluation, the chief of the clinic is interested in evaluating the

feasibility and quantitative effect of the implementation of the proposed redesign. This situation

raised some interesting research questions:








Quality

Time

Flexibility

Cost

4




The aim of this study is twofold, both qualitative and quantitative. The qualitative part

intends to answer the first research question that requests the identification of the factors

hindering the possibility to implement the new process. While the second until fourth research

questions have a quantitative nature. They are directed to propose an alternative setting that

supports the implementation of the redesigned one-day treatment (ODT) process, and to measure

the advantages of the new implementation in terms of resources utilization and costs. The mixed

nature of this project suggests the following goals:

1. Analyze the actual process from a logistic perspective and identify the factors that hinder

the possibilities of treatment on the same day of the diagnosis.

2. Propose alternative designs that allow the implementation of the new process.

3. Compare the performance of the actual process to the new alternatives in terms of the

resource utilization and total costs.

4. Identify how the implementation of the new alternatives affects the average waiting time

of non new BCC patients receiving services at the clinic.

The scope of this study is limited to the dermatology oncology outpatient clinic at the

Catharina Hospital. The conclusions will be specific to the hospital in study, with the possibility

to be extended to another outpatient clinic with similar characteristics. The analysis includes the

process followed by new patients arriving at the clinic for consultation and diagnosed and treated

with basal cell carcinoma. The effect of the implementation of one-day treatment approach will

include new and control patients. Therefore, the effect in the waiting time for control patients

will be analyzed as a side effect of our proposal, as indicated in the last goal stated.

1.4 Report Outline

The remainder of this report is organized as follows. Chapter 1 describes the organizational

context in which this research is developed and the problem statement, emphasizing the gaps that

can be closed by the current research and the specific project goals and scope. A literature review

is presented in chapter 2 to get some insights into the approaches commonly used by authors to

evaluate a process redesign and factors that must be consider for a successful implementation. A

performance analysis of the actual process is developed in Chapter 3, to discover the factors that

influence the implementation of the redesigned ODT process. An elaboration of the model

developed to represent the actual system and the redesigned ODT process is presented in Chapter

4. Moreover, Chapter 5 presents different alternative scenarios that could increase its successful

implementation. Those scenarios require to be evaluated to quantify the impact of their changes.

Chapter 6 shows the results gathered from implementing the changes from the scenarios

proposed and an analysis of these results. Finally, Chapter 7 presents the conclusions and

recommendations for future research.

2 LITERATURE REVIEW The aim of this literature review is twofold: First, to identify the different factors that needed

to be addressed to implement the redesigned ODT process; and secondly, to get insights into the

different approaches available to answer the research questions defined in the current study.

2.1 Introduction The logistic perspective for production management of processes in a healthcare

organization has to be based on the understanding of the characteristics of hospital care processes

and their interaction with resources (Vries et al., 1999). Nowadays some of the critical resources

are scarce, making their allocation to the different processes an important aspect of health care.

However, the allocation of the different resources is not a simple task because it should consider

the interaction between the different departments in the hospital, shared and critical resources. It

also requires a definition of priorities between the different tasks performed by them.

It is suggested that the same analysis needs to be followed before implementing any

process redesign. Villa et al. (2009) analyzed how the success of processes redesign to improve

the patients flow heavily depends on the presence of a well-designed capacity planning system.

Some important aspects considered in their study for the capacity planning are: (1) the

synchronization of the operating rooms scheduling with the other services; (2) centralization of

the scheduling process for shared resources; and (3) enhancement of the coordination between

different hospital settings, allowing a local and system-wide optimization.

Furthermore, the same problem has been approached by different researchers considering

the waiting lists instead of waiting time. For instance, Vissers et al. (2001) analyzed how waiting

lists cannot be simply approached as an efficiency problem and stated that “the waiting list is a

result of four mechanisms, i.e. need assessment, demand management, resources allocation and

production management”. The waiting lists should be approached as an effect of the mismatch

between demand and supply of care. At the hospital level, the development of waiting lists is

caused by an insufficient allocation of resources to match the flow of patients per specialty.

Those results emphasize that the capacity planning is an important factor to be analyzed for the

implementation of a process redesign.

With this information at hand, a literature review was conducted to investigate the decision

areas related to the admission and capacity planning that should be addressed if a reduction of

waiting time is to be achieved. The results are shown in Figure 2, including a total of 97 articles

fulfilling the relevance criterion from which only 44 approved the quality criterion. Finally, the

articles collected from the different search engines were placed together and after eliminating the

duplicates, 21 articles were selected to develop the literature review and synthesis. The selecting

procedure is summarized in Figure 2.

6

Figure 2-Overview of literature search and selection

2.2 Study Synthesis A critical review of the literature selected in the previous sub-section will help to develop a

thorough understanding of the different approaches presented to solve problems related to

admission and capacity planning in healthcare organizations. The total body of literature has

been organized in three different groups: capacity planning, admission planning and integrated

approaches. This section intends to highlight the different aspects considered in the literature

related to the topic at hand, and to provide fresh insights in the best approaches to solve them.

2.2.1 Admission Planning The admission planning consists in scheduling the admission of patients according to

medical priorities and their resources requirements. It mainly focused in a reduction of the

patients’ waiting time and resources overtime and idle time. However, the decisions in the

admission planning can impact different aspects of the patient waiting time. Basically, the total

patient waiting time can be decomposed in different parts: (1) the time between the patient call

and the scheduled date of the appointment; and (2) during the appointment date, there is a

difference between the scheduled moment of the appointment and the real moment that the

appointment starts. It is necessary to clearly distinguish between those types of waiting time

because they are approached differently depending on the type of admission system under study.

For instance, a scheduled admission system is more focused on reducing the time between the

moment of the appointment and the real time of the appointment, increasing the resources

utilization, but increasing the time that the patient needs to wait for the appointment. In

healthcare, this is a very common approach due to the scarcity of resources and the willingness

of patients to wait for the service. Nevertheless, this situation has been changing suggesting more

improvements in the area.

7

The admission planning also involves some important decisions like the number of

patients admitted for a specialty each day and the patient mix. Vissers and Beech (2005)

distinguished three different types of admissions: as an outpatient referred by the family doctor;

emergency patient, requiring immediate assistant by a specialist; and finally, as an inpatient,

which is subsequently subdivided in scheduled and unscheduled. A scheduled inpatient

admission includes an assignment of an appointment date for the procedure, while unscheduled

admissions correspond to emergency admission of patients after a medical decision by a

specialist at the outpatient department. For this study, the main interest is to focus on the

outpatient admissions, considering the characteristics of the clinic under study. It is important to

make that distinction because nowadays those patients are demanding shorter waiting times, the

competition is escalating between providers and it becomes a challenge especially for outpatient

service providers.

Other aspects of the admission planning have been previously analyzed in the literature in

order to identify their impact on the patients’ average waiting time. For instance, Conforti et al.

(2009) evaluate the impact of a non-block scheduling strategy for radiotherapy treatments. With

a block strategy, the workday is divided in a fixed number of slots with the same time duration,

while the non-block strategy assigned a different treatment time for each patient. The authors

confirmed in an experimental setting with six different scenarios that the implementation of a

non-block scheduling strategy provides a reduction in the average waiting time for patients and

increase the number of new patients scheduled.

The scheduling rule and placement of open or urgent appointment slots in a multi-period

environment were investigated by Klassen and Rohleder (2004). First, they analyzed four

different scheduling rules: (1) FCFA, First-call first-appointment used as the base case;

(2) LVBEG, low variance patient at the beginning of the session and high variance at the end;

(3) B2, place two patients in the first appointment slot and spread the rest evenly over the period;

and (4) B2LVBEG, which is a combination of the last two appointment rules. The LVBEG was

found as the best appointment rule in a multi-period environment and in a single period, in terms

of a reduction in the average patient waiting time. A patient is considered as “low variance”

when the expected time that they are likely to take is well known, compared to the “high

variance” with a quite unsure duration time. The authors assigned the first half of appointment

slots in any period (morning or afternoon) for low variance patients for the experiment. Optimal

outpatient schedules were developed by Kaandorp and Koole (2007) for a finite number of

possible arrival epochs. They demonstrate that the Bailey-Welch rule (same concept as B2 rule)

provides results near optimal.

The effect in patients’ waiting time due to the placement of open appointment slots for

emergencies is another decision explored by Klassen and Rohleder (2004). They found that

spreading the urgent slots during the day or at least during the afternoon provide better results,

even though it was found that this placement did not influence significantly the average waiting

time. This suggests that open appointment slots can be placed based on each provider’s

preference. However, not only the placement but also the percentage of open slots has been

evaluated by the researchers. Qu et al. (2007) developed a quantitative approach to define the

optimal percentage of open appointments available each day to match the demand with the

capacity. They analyzed how the open access scheduling or same-day scheduling significantly

8

improve the performance of outpatient clinics. In this schedule some appointments are

prescheduled and others are held open to be filled from 12 until 72 hours before. The results

illustrated that when the demand is much higher than the installed capacity, all appointments

should be held open if there is no medical need for pre-scheduling. This will improve the

resources utilization and patients’ satisfaction, reducing costs. Su and Shih (2003) explored

different registration strategies for the allocation of walk-in and scheduled patients in outpatient

clinics. They found that scheduling the appointments with flexible time interval provides the

least throughput time for scheduled patients, while the alternative sequence (odd numbers for

walk-in patients and even numbers for scheduled patients) provide the least throughput time for

walk-in patients.

The approaches identified in the literature for the problems in admission planning can be

divided in two groups: studies focus on the evaluation of schedules (Klassen and Rohleder,

2004) and others that develop algorithms to find good schedules (Kaandorp and Koole, 2007;

Conforti et al., 2009). Operations research methods, such as queuing theory, stochastic

optimization and simulation, are applied to the analysis and optimization of the appointment

scheduling systems. Vissers and Beech (2005) summarized the variety of approaches that have

been used to investigate the scheduling of appointments in outpatient clinics in four groups:

(1) bottleneck analysis, comparing service capacity and demand at the bottleneck; (2) steady-

state queuing models, to evaluate the effect of changes in the appointment; (3) time dependent

queuing models for simple systems; and finally (4) simulation models, to improve the

understanding of the impact of some changes in more complex systems.

To conclude, the literature presents a collection of studies in admission planning for

patients in healthcare organizations to reduce the patients’ average waiting time while

maintaining appropriate levels of resource utilization and costs. The admission planning involves

several decisions as shown in this section, including the selection of a scheduling strategy, the

appointment rule, the percentage of appointment slots held open, and their allocation. The next

section will summarize the main findings in the literature related to capacity planning in

healthcare organizations.

2.2.2 Capacity Planning

A large amount of work has already been done in this research area. The capacity

planning involves several decisions including optimizing the utilization of a critical resource,

balancing the capacities of different resources required simultaneously and the allocation of

shared resources. The type of decisions can vary from the assignment of a date for a specific

treatment, the time, the operating room were the procedure will be performed or simply the

allocation of capacity.

A literature review in operating room planning and scheduling was presented by Cardoen

et al. (2009). They evaluated 247 manuscripts including articles published in scientific journals,

proceedings and Ph.D. dissertations. They proposed a structured way to review the literature

selected using descriptive fields to facilitate the comparison in multiple facets between studies

and the link between research contributions. Therefore, this section will use this taxonomy to

identify the research contributions in capacity planning in healthcare organizations. The

9

descriptive fields included are: patient characteristics, performance measures, decision

delineation, research methodology, uncertainty and applicability of research.

- Patient characteristics: Patients are mostly classified in the literature related to capacity

planning in two major groups: elective and non-elective patients. Another way to refer to non-

elective patients is emergency patients. The emergency patients must be served immediately

while elective patients can be planned for future dates (Lamiri et al., 2006). This classification

helps in the prioritization between patients, considering their medical condition. Most of the

studies reviewed only consider the elective or planned patients and manage the non-elective

patients letting some open slots only available for emergencies. The definition of the amount of

slots open for emergencies was discussed previously in the admission planning section.

Another patient’s classification that can be distinguished in the studies is in inpatient and

outpatient. An inpatient is referred to a hospitalized patient that needs to stay overnight in the

hospital, while outpatients enter and leave the hospital on the same day. In some cases both

patient types: inpatient and outpatient are explicitly considered in their analysis, like in Pham and

Klinkert (2008). It is very important to define the types of patients for which the scheduling

procedure is developed because it provides some information on the uncertainty involved in the

arrival and duration of patients, and their further demand of resources (Cardoen et al., 2009).

- Performance measures: Some of the most important performance measures used in the

literature to define the objective function are the waiting time, throughput, resource utilization

and financial measures. Their selection describes the perspective used to treat the problem

approached. For instance, the average waiting time for a patient represents one of the most

commonly used performance measures identified by Cardoen et al. (2009) within the 247

manuscripts reviewed in their study. Nevertheless, when the resources involved in the decision

are very expensive or scarce, like the time of a specialist or operating room, the researches intend

to balance the decisions minimizing the waiting time for patients together with the waiting time

for these resources (Chern et al., 2008). Directly related to the waiting time reduction is the

throughput. The throughput indicates the number of patients treated. When the amount of

patients treated increases, it triggers a decrease in waiting time assuming that the amount of

resources available and the demand remain the same.

Another widely used performance measure is the resource utilization. The resources

utilization is focused in the resources workload. The maximization of the resource utilization

should be selected when the coordination of several resources is required for the patient’s

treatment. This coordination could lead to the underutilization of some of those resources. In this

case the optimization should take place maximizing the utilization of the most critical resource in

the process, which is typically the operating room. One example of this application is presented

by Fei et al. (2009) who developed a mathematical model to maximize the utilization of the

operating rooms and to minimize overtime. It is important to emphasize that the maximization of

the resource utilization does not necessarily imply a reduction in overtime. Some studies assume

a utilization of 100% while others define target utilization, and intend to reduce the difference

between the utilization reached and this target.

The financial aspect is implicit in almost all the studies, but rarely discussed in practical

applications, considering the confidentiality of information related to costs for any organization.

The maximization of the resource utilization implies a cost reduction due to the fact that there are

10

some fixed costs involved in the process, while overutilization can lead to detrimental results in

terms of costs. Lamiri et al. (2008) developed a model for elective surgery planning in operating

rooms minimizing the costs related to the overutilization of operating rooms and the costs related

to perform the elective surgery.

- Decision delineation: A wide variety of decisions are involved in the capacity planning and

scheduling of resources, such as the assignment of a date, a room , a time or the allocation of

resources. Cardoen et al. (2009) categorized their studies based on three different decision levels

considering whether the decision applies to a medical discipline, a surgeon or a specific patient

type.

The discipline level considers decisions that are taken for a department or a specific

specialty, while at the surgeon level the decision should be focused on planning an agenda for

each surgeon indicating the date, room and time that the surgery or treatment in general have to

be performed. An example of the discipline level is the adaptive scheduling approach for

resources allocation for tomography scans at the radiology department, presented by Vermeulen

et al. (2009). In this case the decisions are made specifically for the resources at the radiology

department. Finally, at the patient level decisions are made for specific patients or patient types.

Most of the contributions in this research area apply to the patient level. The main reason is the

lack of flexibility from the specialists to accept changes in their rosters. It is expected an increase

in the contribution at the other levels in the near future due to the introduction of more operation

research scheduling techniques in healthcare organizations (Cardoen et al., 2009).

Moreover, a framework presented by Vissers and Beech (2005) presents five different

levels of planning, each one with a different decision focus. The strategic planning evaluates the

long-term resource requirements, level the annual patient’s volume and define a target service

and efficiency levels. The second level is the patient volume planning and control. In this level

the decisions are focused on the amount of resources available on the annual level to specialties

and patient groups. The “resource planning and control” is the third level, in which decisions

involved specialists’ time and detailed number of patients per period together with a time-phased

allocation of resources. The next level is the patient groups planning and control, which

considers the guidelines to plan patients based on patient’s group. And finally, the lowest level is

the patient planning and control, in which individual patients are assigned to resources on a daily

basis. This conceptual framework can be used to position contributions to different aspects of

capacity planning in healthcare organizations.

- Research methodology: Operational research and management techniques have been

applied to the capacity planning in healthcare organizations, combining each type of analysis

with the most appropriated evaluation technique. The most common types of analysis found in

the literature were the optimization and scenarios comparison. In mathematical programming

approaches, scheduling problems are formulated using linear programs, linear integer programs

or just general mathematical programs. An example of the optimization approach is presented by

Pham and Klinkert (2008). They developed a surgical case scheduling approach called multi-

mode blocking job shop (MMBJS) as an extension of the job shop scheduling problem, using a

mixed integer linear programming problem. The MMBJS allocates resources to each individual

case and define the time to perform the surgery. The optimal solution was pursued using a

practical-sized instance. Moreover, Vissers et al. (2005) optimized the scheduling of operating

rooms for cardiothoracic surgeries considering a patient mix. The problem was modeled using a

11

mixed integer linear programming and solved using the branch and bound algorithm. The

branch and bound method is the best known exact algorithm for linear integer programs, but

exploring all the trees can be very time consuming and heuristics can help to finish the search.

Nevertheless, when the number of variables and constraints increases it is more difficult

and sometimes impossible to find a feasible solution for mixed integer programming or binary

integer programming problems. Also, search algorithms become very inefficient requiring an

excessive computational time. Hence, heuristics are the method of choice to deal with complex

real world objectives and constraints that do not solve easily with a mathematical programming

formulation (Ernst et al., 2004). For instance, a heuristic algorithm HESA was designed to solve

the health examination scheduling problem effectively and efficiently. This problem was

formulated using a binary integer programming which is commonly used for those scheduling

problems, but with the disadvantage that become unsolvable for large problems. Another

heuristic for a scheduling problem was developed by Chien et al. (2008). They presented one

case in which a genetic algorithm was designed to solve the problem of scheduling therapies for

patient’s rehabilitation. The problem was structured as a mixed integer programming model

pursuing a reduction in the patients’ waiting time and maximization of resource utilization. Since

the problem is NP-hard, they proposed a heuristic method (genetic algorithm) which has

demonstrated to provide satisfactory results for hard combinatorial optimization problems.

Simulation approaches seem to be very suitable for applying a scenario analysis,

specifically when the problem involves a lot of stochastic variables and requires some modeling

flexibility to allow a sufficient degree of detail. Some approaches included a mix of heuristics

and simulation in an attempt to deal with complex problems. An example of this case was

presented by Oddoye et al. (2009). They combine goal programming and simulation to explore

different scenarios for an efficient planning of resources. Their approach offers the strengths of

simulation (flexibility in capturing and modeling the patients flow) and goal programming

(meeting several targets over a range of conflicting criteria) to facilitate the decision making to

the hospital management. Finally, this section described how the problems related to capacity

planning in healthcare organizations can be translated using mathematical programming or

simulation techniques based on the type of analysis required. The mathematical modeling has

demonstrated to be more suitable for optimization while simulation suits better for scenario

analysis.

- Uncertainty: This section analyzes what aspects of the uncertainty involved in the processes

are incorporated in the analysis and modeling of the problems related to capacity planning. Two

types of uncertainty are mainly addressed in the literature, one related to the arrival pattern and

another to the duration of the different activities. Lamiri et al. (2008) described a stochastic

model for operating room planning with elective and emergency demand for surgery. They

incorporated the uncertainty due to the emergency demand using a stochastic arrival of

emergency cases for surgery. However, several studies analyzed ignore the uncertainty in

activities duration and assume deterministic values (Vissers and Beech, 2005). The main reason

to ignore this variability is due to lack of information available, demanding a lot of time and

expertise for an appropriate data collection.

- Applicability of research: An important characteristic of the studies found in the literature is

to illustrate the applicability of their research. They could be tested using real data or generating

some data for experimental setting. Cardoen et al. (2009) emphasize how the lack of

12

implementation in healthcare seems to have improved considerably. But the implementation

process is hardly described in detail by the authors.

2.2.3 Integrated Approach

In the previous sections, research from several authors has been discussed and analyzed

indicating its main characteristics and contributions. Authors evaluate a problem and capture the

most significant aspects of it in order to define the best approach. In some cases they need to take

into account more than one decision level at a time to provide a solution that involves different

perspectives of the problem.

For instance, Testi et al., (2007) developed a three phase hierarchical approach for the

weekly scheduling of operating rooms presented in Figure 3. In the first phase, called the session

planning (SP) is determined the number of sessions to be scheduled weekly for each ward. Phase

two is the master surgical schedule (MSS). This phase defines the assignment between ward,

surgery room and day of the week, using the solution from the first phase as constraints in the

second model. The third phase used a discrete-event simulation model to assign patients into the

assigned operating room theaters. The first two phases were solved by integer programming and

are situated in the discipline level, while the third phase was formulated in terms of individual

patients. The aim of their study is to provide an integrated way of planning surgical activities to

improve the efficiency of the operating room and reduce the waiting list, while improving the

department organization.

Figure 3-A three-phase hierarchical approach for operating theater schedules (Testi et al., 2007)

Furthermore, another way used by authors to integrate different decisions is providing a

decision support tools for managers. Those tools allow them to evaluate the impact of their

decisions in terms of capacity planning. They provide the advantages of empowering decision

makers at the operational level and add flexibility to system designs and implementation. One

decision support tool was developed by Bowers et al. (2005). They provided a simulation model

for a diagnosis and treatment center to managers. This decision support tool allows managers to

appreciate the consequences of their proposals and the impact of their requirements in terms of

the demand of resources involved in the provision of service. This tool provides a significant

input for the capacity planning for a diagnosis and treatment center.

13

Lee and Asllani (2001) provided guidelines to design decision support systems. They

distinguished three different dimensions to analyze the scheduling problem: entity type

(outpatient, inpatient), business unit (department with or without appointment) and scheduling

environment (stochastic or deterministic). Their approach provides the necessary interface in a

dynamic environment and finds the best scheduling technique in a given point in time. The idea

is to reduce the problem’s complexity, structuring the problem in different layers or classes. The

first layer provides input to the next one and the later solves the problem in a static environment.

Different healthcare organizations may have different classes, but the first step is to

define them for the problem at hand and then find the most appropriate solution methodologies

to be applied. For instance, Figure 4 suggests scheduling system for the regional health care

center described in Lee and Asllani (2001). It shows that the set of scheduling problems can be

grouped in two main classes: stochastic and deterministic, leading for each one of them to

different methodologies to be followed. Basically, they provided a framework for the

development of decision support systems for the scheduling of resources in healthcare

organizations.

Figure 4-A scheduling problem for the regional health center (Lee and Asllani, 2001)

Another framework for modeling hospital resources was developed by Harper (2002).

Similar to the previously described by Lee and Asllani (2001), he proposed a simulation

technique for managing complex stochastic systems. The idea is that the common deterministic

approaches for planning and managing the system are expected to be inadequate in those cases.

One advantage of this framework is the inclusion of a patient classification to capture variability

within patients.

14

Finally, a better integration of different decision levels enhances the understanding of the

systems interactions and considers the complexity of the problems at hand. In this subsection

different types of approaches were included: (1) a hierarchical approach in which different

decision levels were consider integrating the decisions for the planning of the resources involved

in surgical activities, and (2) decision support tools, helping managers to evaluate the impact of

their decisions changing different factors. In some cases, the decision support tools were

described as general frameworks to provide guidelines for a flexible application to different

problem settings.

2.3 Concluding from the literature review

This literature review started analyzing from a logistic perspective, the factors that

influence the success of implementing a process redesign. The most significant factors that

needed to be addressed are the admission and capacity planning. Basically, to apply the

redesigned ODT process it is necessary to reduce the actual patient waiting time maintaining the

current capacity level. This redesigned process will be offered to a specific type of patients, but

the resources are shared by all the patients at the clinic. Therefore, it is necessary to analyze the

best allocation of capacity to the different patient types to be able to implement the redesigned

ODT process. With this information, a research synthesis was conducted classifying the literature

in three different areas: admission planning, capacity planning and integrated approaches.

The admission planning included several decisions considering the selection of a

scheduling strategy, the appointment rule, the percentage of appointment slots held open and

their allocation. This literature presents a collection of studies in admission planning for patients

in healthcare organizations to reduce the patients’ average waiting time while maintaining

appropriate levels of resource utilization and costs. Furthermore, the researches in capacity

planning were analyzed using the taxonomy proposed by Cardoen et al. (2009). They proposed a

structured way to review the literature selected using descriptive fields to facilitate the

comparison in multiple facets between studies and the link between research contributions. The

descriptive fields included are: patient characteristics, performance measures, decision

delineation, research methodology, uncertainty and applicability of research.

The integrated approaches included the following: (1) a hierarchical approach in which

different decision levels considering the decisions for the planning of the resources involved in

surgical activities, and (2) decision support tools, helping managers to evaluate the impact of

their decisions changing different factors. In some cases presented as general frameworks to

provide guidelines for a flexible application to different problem settings. It was analyzed how

those integrated approaches improves the understanding of the systems interactions and the

problem complexity.

The goal of the current research is to evaluate different scenarios to quantify the effect of

implementing the redesigned ODT process for new patients with basal cell carcinoma. The most

common approach to analyze different scenarios is simulation based on the literature, in order to

understand the impact of each decision in complex systems specially when managing stochastic

processing times and demand (Lee and Asllani, 2001). Simulation is a cost-effective way to

evaluate different scenarios without requiring any changes in the current system, which would

require a higher investment in time and money and even sometimes discomfort for the patients.

15

The complexity found in the actual process, such as a stochastic arrival pattern, different

patients routing and scheduling, makes simulation an attractive tool to be used (Vissers and

Beech, 2005). In this case it is very critical for the analysis to incorporate the variability in the

patients’ arrival to identify the impact in terms of resources utilization in case of implementing

the redesigned ODT process. Also the variability in the activities duration can influence our

conclusions related to the feasibility of this implementation.

To conclude, the literature review provides some insights into different factors considered

in the analysis of processes from a logistic perspective. Using this information, next chapter

analyzes the current situation using an approach proposed in the literature to identify the

feasibility of the implementation of the redesigned ODT process, and to evaluate the impact of

the factors suggested to build different scenarios.

16

3 CAPACITY ANALYSIS

3.1 Introduction

The literature review presented in the previous chapter contributed with the identification of

significant factors when performing an improvement or redesign in hospital processes. The

logistic approaches presented consider the different activities linked to the use of resources in the

process. This chapter implements the approach described by Vissers and Beech (2005) using the

demand-supply model for the analysis of hospital processes, to understand the process chain

followed by patients arriving at the dermatology oncology outpatient clinic and their interaction

with the critical resources.

This approach intends to identify the match between demand and supply in the clinic,

allowing the estimation of the resources utilization. The methodology proposed by Vissers and

Beech (2005) includes the following steps:

• First, describing the inflow of patients over the different patient types distinguished

• Secondly, identifying the different treatment profiles or types of treatments available for

each patient group

• Third, describe the process relating the different patient groups (a combination of the

patient types and the types of treatments) with their resources use.

• Finally, evaluate the performance of the process in terms of the resources utilization.

Using this approach it is possible to evaluate the feasibility of implementing the redesigned

ODT process in terms of the current capacity level and even propose the scenarios to improve the

chances of success for the redesigned ODT process.

3.2 Demand-supply model for clinical processes

3.2.1 Inflow of patients

The current inflow of new patients arriving at the clinic is on average 10,500 patients per

year, which is estimated to be approximately 250 patients every week. The clinic has been

operating during five years starting with an estimated inflow of patients of 6,500 patients during

the first year until 10,500 in the last year (2009) with an estimated increase of 1,000 patients

every year. These numbers were provided by the clinic considering the agreements between the

hospital and the clinic with respect to the expected amount of new patients that they should

receive every year.

Moreover, the total inflow of outpatients to the clinic is not only composed by new patients

but also by control patients. The control patients are those patients that have been treated at least

one time in the clinic and continue to make follow up visits every 6 months during the next two

years after their treatment. However, it is very plausible that patients treated for skin cancer will

be recurrent, which means that the probability to have a new tumor is very high. This will

influence our patient’s inflow because most of the patients will continue in the process for

longer. Considering this information and the fact that almost 48% of the total amounts of patients

evaluated are diagnosed with some type of skin cancer, the amount of control patients arriving to

the process nowadays can be estimated as 516 every week.

17

3.2.2 Treatment Profiles

The treatment profiles are the different types of treatments available for the new and

control patients in the clinic. They define the different paths followed by patients in the clinic. In

this particular case approximately 40% of the patients after their consultation are diagnosed

cancer free. The next 60% is referred for further medical tests. The type of diagnosis expected

for a patient at the clinic includes: (1) cancer-free, (2) basal cell carcinoma, (3) melanoma or (4)

squamous cell carcinoma. After the biopsy, almost 19% of the patients referred for medical tests

are included in the category of non BCC patients: cancer-free, melanoma or SCC.

The current study only includes the analysis of patients diagnosed with basal cell

carcinoma (BCC) as defined in the project scope. Therefore, the treatment profiles considered in

this analysis involve: self-treatment, PDT, MOHs and Excision. After the biopsy, approximately

44% of the patients are referred for self-treatment, 14% for PDT, 9% for MOHs and the

remaining 14% for Excision. These values were gathered from a database provided by the

hospital containing a collection of all the cases treated since 2005 until 2009 in the clinic.

Finally, there are five different paths that could be followed by a patient (new and control) after

their consultation in the clinic: (1) self-treatment; (2) PDT; (3) MOHs; (4) Excision; (5) Others:

melanoma, SCC or cancer free patients.

3.2.3 Process description and resources requirement per patient group

The general process followed by a patient at the dermatology oncology outpatient clinic,

looking from an aggregate level of patient flows and resources, starts when a patient calls for an

appointment. The secretary assigns the patient to the first available slot for the consultation.

Patients are referred by their general doctor, dermatologist or also self-referring. During the

appointment, the doctor makes a general check and gives a first diagnosis. The expected

diagnosis for a patient is one of the followings: (1) cancer-free, (2) basal cell carcinoma, (3)

melanoma or (4) squamous cell carcinoma, described in Figure 5.

Figure 5- Flow chart of activities followed by new patients diagnosed with BCC in the dermatology

oncology outpatient treatment

Patient arrival:

Call or referal

Expected skin

cancer patient

End process

Receive info about

definitive

diagnosis and

further treatment

Confirm

Diagnosis?

Melanoma

Squamous Cell

carcinoma

Basal Cell

carcinomaConsult Meeting

Take photo and

biopsy for

pathological check

Further Treatment

Self-treatment

Type of

Treatment?

Excision

Mohs procedure

Photo Dynamic

Therapy (PDT)

Post-treatment

care and control

checks

Some additional tests are required to confirm the diagnosis and to define the appropriate

treatment. The doctor asks the patient to make a photo of the cancer spot (in another area inside

of the hospital) and afterwards to go back to the dermatology oncology area to take a biopsy. A

18

nurse takes the sample from the biopsy and sends it to the pathology department, which is in

charge of the sample analysis to confirm or rejected the diagnosis. The doctor receives the

patient file again with the photo and results from pathology. With this information at hand, the

doctor decides the appropriate type of treatment for the patient and calls the patient to inform

about the final diagnosis and recommended treatment. Furthermore, the secretary calls the

patient and arranges an appointment for the treatment. The patient receives the treatment which

is followed by further control checks in the clinic to verify the condition of the cancer.

Different resources are involved in the process of diagnosis and treatment for patients

with BCC, as described in Table 1. The table presents the main resources directly responsible for

specific activities in the process. The second column classifies those resources considering the

kind of activities that they can perform. For instance, the assistants include personnel who can

perform different functions to support or assist doctors in their activities. They are integrated by:

(1) the secretary who plan the appointment; (2) receive the patient at the front desk; (3) the nurse

who assists the doctor during the excision or MOHs; (4) others who take the biopsy; (5) treat the

patient using the PDT. Those resources are shared by all the processes in the clinic, so to

estimate the amount of time available is necessary to consider the time that they are actually

scheduled for the specific activities involved in this process.

Table 1-Overview of the resources available at the clinic

Resource

Description

Classification Amount

available

Activities performed Time available per week

Specialists • 3 oncologists

• 4 dermatologists

7 • Supervision

• Consultation

• MOHs

• Excision

3 days a week per specialist,

including one day for

research

Doctors • AIO*

• ANIO**

8 • Consultation

• Excision

• MOHs

32 to 36 hours a week

plus 4 to 8 hours for

research

Assistants

• Secretaries

• Nurses Biopsy

• Nurse PDT

• Nurses Excision

25

(only part

time)

• Plan appointment

• Make appointments

• Receive patients at

front desk

• Biopsy

• Perform PDT

• Assist in MOHs

and Excisions

They work part time and

can be classified in three

main functions:

1. NP: nurse for MOHs,

PDT, excision, plan

MOHs

2. NU: biopsy and regular

nurse assist.

3. ST: secretarial

functions, front desk,

telephone, plan

appointments and

organize files.

Operating

Rooms • OK, internal

• KCI, external

2

3 • MOHs

• Excisions

OK are 7 days a week,

while KCI is 12 hours a

week

* AIO is “arts in opleiding” doctors studying for a specialization, requiring 8 hours a week for research

** ANIOS is “arts niet in opleiding specialisme” doctors studying for a specialization, with 4 hours for research

19

The analysis of resource usage in the process for new patient groups is shown in Table 2. It

includes a list of the different activities that should be performed in the process, the expected

duration of those activities and the time demanded per resource type. The resources considered

in the analysis are operating rooms, specialist, doctors and nurses. In some cases, the duration of

the activities differs between new and control patients. The distinction between a new and a

control patient is only made for the activities related to the diagnosis, because for the treatment

they demand the same amount of time from the different resources.

Table 2-Resource use per activity

Activity description

Estimated duration

(minutes) Resource

Resource use

(minutes)

Consult

Consultation meeting for new patient 15 Doctor 15

Specialist 5

Consultation meeting for control patient 5 Doctor 5

Take sample (Biopsy) 10 Biopsy nurse 10

Inform patient diagnosis and future

treatment 5 Doctor 5

Treatment: PDT treatment

Check file and ointment application 10 Nurse, PDT 10

Infrared light exposition

10 (after 3 hours) or

30 (after 2 hours) Nurse, PDT 20

Treatment: MOHs surgery

Prepare patient and perform surgery 35 Operation Room (OR) 35

Doctor 35

Nurse, Excision 35

Specialist 20

Analyze samples 7.5 Doctor 7.5

Specialist 7.5

Close wound and prepare report 15 Operation Room (OR) 15

Doctor 15

Nurse, Excision 15

Specialist 10

Treatment: Excision

Perform surgery and close wound 30 Operation Room (OR) 30

(the time change between patients: 15, 20, 30,40 minutes until 1

hour) Doctor 30

Nurse, Excision 30

Specialist 10

It is important to explain that for the PDT treatment, the time spent on the application of

ointment is on average 10 minutes and then patients need to wait during 2 or 3 hours for the

20

application of the infrared light which requires 30 or 10 minutes, respectively. Finally, the nurse

schedules the next appointment for a follow up visit.

3.2.4 Performance estimation for actual process

Having defined each step in the clinical process, it is possible to summarize the resource

usage in the process for patients looking for: (1) an appointment for consultation, (2) a PDT

treatment, (3) Excision or (4) MOHs, as shown in Table 3. It also shows the estimated weekly

demand or arrival of patients per group type.

Table 3-Resource use per patient group in the actual process

The information regarding the demand of each type of resources per patient group and the

weekly demand is used to estimate the resources needed during the week and to compare it with

the level of resources available for this process. Using the data from Table 3 and gathering

information from the clinic about the current capacity level, it is possible to link the demand and

supply of the most critical resources involved in the process, as shown in Table 4.

Table 4-Match between weekly demand and supply per resource type in the clinic

Resource

Weekly Demand

(per hour)

Weekly Supply

(per hour) Utilization (%)

1. Operating Room 67.0 120 55.83%

2. Specialists 62.6 104 60.19%

3. Doctors 199.9 216 92.55%

4. Nurse Excision 67.0 112 59.82%

5. Nurse PDT 32.0 40 80.00%

6. Nurse Biopsy 76.7 80 95.84%

The availability of resources per week is estimated using the current allocation of resources for

consultation and treatment during the week. The master schedule of doctors and specialists for

consult meeting and treatment are shown in Appendix B. It can be derived from Table 4, that the

current capacity level is sufficient to respond to the demand on an aggregated level. However, in

Patient's group per

unit

per

week

per

unit

per

week

per

unit

per

week

per

unit

per

week

per

unit

per

week

per

unit

per

week1. New patient cancer free 100 0 0 5 500 20 2000 0 0 0 0 0 0

2. New patient self-treatment and

non BCC 94 0 0 5 470 20 1880 0 0 0 0 10 940

3. New patient for PDT 21 0 0 5 105 20 420 30 630 0 0 10 210

4. New patient BCC refered for

MOHs14 50 700 32.5 455 67.5 945 0 0 50 700 10 140

5. New patient BCC refered for

Excision 21 30 630 15 315 0 0 0 0 30 630 10 210

6. Control patient cancer free 206 0 0 0 0 5 1030 0 0 0 0 0 0

7. Control patient self-treatment

and non BCC 196 0 0 0 0 10 1960 0 0 0 0 10 1960

8. Control patient for PDT 43 0 0 5 215 10 430 30 1290 0 0 10 430

9. Control patient BCC refered for

MOHs 28 50 1400 37.5 1050 57.5 1610 0 0 50 1400 10 280

10. Control patient BCC refered

for Excision 43 30 1290 15 645 40 1720 0 0 30 1290 10 430

Total (in minutes): 4020 3755 11995 1920 4020 4600

Total (in hours): 67.0 62.6 199.9 32.0 67.0 76.7

Estimated

weekly

demand

Nurse, PDT Nurse Excision Nurse BiopsyOperating

Room (OR)Specialists Doctors

21

order to implement the redesigned ODT process, it is necessary to evaluate the level of matching

between demand and supply on a daily basis. This evaluation is described in the next subsection

and provides insights into the amount of capacity expected to be reserved for the patients flow

and the areas of improvement for the definition of different scenarios.

- Performance estimation with the redesigned ODT process:

The implementation of the redesigned ODT process requires an analysis of the capacity

available compared to the demand on a daily basis. Therefore, it is necessary to identify the

difference in the demand imposed by the implementation of the redesigned ODT process. This

difference has roots in the demand of resources for treatment combined with the consultation of

patients. Until now, the demand of appointment slots for consultation has been managed

independently from the demand of appointment slots for treatment. They use independent

waiting lists for the consultation of patients and for the different treatments. Nevertheless, the

demand of resources for treatment for patients following the redesigned ODT process is guided

by the amount of patients arriving for consultation.

The redesigned ODT process imposes the need to align both services: consultation and

treatment, to be offered on the same day. This alignment guarantees that every new patient

arriving at the clinic for consultation following the redesigned ODT process will have the

resources available for treatment during the same day. Nowadays, a master schedule is prepared

in the clinic to present a weekly plan of activities for the specialists, doctors and nurses. This

schedule intends to coordinate the different activities at the clinic and at the same time reserve a

specific amount of capacity every day for the different patient groups. This master schedule is

revised every six months with small changes based on the availability of resources at that time.

Using these schedules-- one for operating rooms, specialists, doctors and another for nurses-- we

defined the current capacity level available every day (each day-part: morning or afternoon) for

treatment. This information is used to compare it with the current demand of resources per

patient group on a daily basis for the different patient groups. The patient groups need to be

defined, following the same steps proposed by Vissers and Beech (2005) using the demand-

supply model for the analysis of hospital processes.

The first step is to distinguish patient types: regular or ODT patients. For the treatment

there is no need to differentiate between new and control patients because they follow the same

path in the process and demand the same amount of resources.

The second step is to identify the treatment profiles, which are the same as in the actual

process: Self-treatment, PDT, MOHs or Excision. In this study only the evaluation is included of

patients referred for MOHs surgery following the actual process and not the redesigned ODT

process. This will guide to the definition of the patient’s groups available for treatment:

[1] Patient PDT-regular flow

[2] Patient PDT - ODT

[3] Patient MOHS

[4] Patient Excision-regular flow

[5] Patient Excision-ODT

22

The estimation of the demand requirements per patient group is the next step needed to

define the total amount of resources demanded each day to estimate the resources utilization. The

daily resource use for patient groups is shown in Table 5, considering the arrival of patients

following the regular and the redesigned ODT process.

Table 5- Daily resource use per patient group with the redesigned ODT process

Patient's group Resource use

Average time

(in minutes)

Estimated

daily demand

Estimated

weekly demand

1. Patient PDT - regular flow Nurse, PDT 30 13 64

2. Patient PDT-ODT Nurse, PDT 30

3. Patient MOHs Operation Room (OR) 50

8 41 Specialist 37.5

Doctor 57.5

Nurse, Excision 50

4. Patient Excision-regular

flow Operation Room (OR) 30

13 64

Specialist 10

Doctor 30

Nurse, Excision 30

5. Patient Excision-ODT Operation Room (OR) 30

Specialist 10

Doctor 30

Nurse, Excision 30

*Values estimated multiplying the total amount of new and control patients expected per week, to the %

corresponding to each treatment type (8.4% for PDT, 5.4% for MOHs and 8.4% for Excision). This data was

gathered from data base collecting all cases including 2005 until 2009

The step that follows is the estimation of the amount of resources needed to fulfill the

daily demand of patients arriving at the clinic. However, to estimate the daily demand of

resources we need to identify the percentage of patients expected to be treated using PDT

following the regular flow and the percentage following the redesigned ODT process. The same

needs to take place for Excision. For instance, every day it is estimated that an average of 13

patients demand treatment using PDT as shown in Table 5, but how many of them should be

treated in one day and how many will follow the regular process? Even though the resources use

is the same for all the patients treated using PDT (regular or ODT), their daily demand differs

and is affected by different factors.

Patients treated with PDT following the regular process have a previous assignment of an

appointment date for their procedure. Those patients are assigned to one of the appointment slots

available during the week based on the master schedule, so they have a scheduled demand.

However, the demand of patients for PDT following the redesigned ODT process cannot be

scheduled or well known in advanced because it depends on the amount of patients arriving for

consult during this day and diagnosed with BCC refereed for treatment. The unscheduled

admission of patients requires the placement of open appointment slots for their treatment every

day. Then the question arises: how much capacity should be held open for ODT patients? This

23

decision will affect the demand of resources during the week. This question suggests that one

factor to be considered in the analysis for the implementation of the redesigned ODT process is

the percentage of capacity that should be reserved for patients following the redesigned ODT

process refereed for PDT and Excision. The next subsection will analyze the effect of the

admission planning in the implementation of this redesign.

3.3 Admission and capacity planning

The admission planning has been studied extensively in the literature as shown in the

previous chapter. It was analyzed how different decisions in the admission planning impact the

performance of the system in terms of waiting time of patients and resources utilization. Some of

the most important decisions identified were the selection of a scheduling strategy, the definition

of an appropriate appointment rule, the percentage of appointment slots held open and their

allocation. In the previous section, the question was raised on the percentage of appointment

slots that should be held open for patients treated by the redesigned ODT process. This section

intends to evaluate the feasibility of implementing the redesigned ODT process at the clinic

using the current admission planning and to identify alternative options to be used for the

definition of the scenarios.

The resource use per patient group shows that the resources needed for the PDT and

Excision are independent. A specialist, a doctor and a nurse Excision is required for the Excision

while only the nurse PDT is required for the PDT. Therefore, the analysis of their capacity for

PDT is presented independently from the analysis for Excision and MOHs.

3.3.1 Planning for PDT

The current admissions for PDT are scheduled in advance. The average waiting time for

PDT treatment is approximately 2 weeks. There is one nurse specifically assigned for PDT every

day in the clinic, and the maximum amount of patients planned per day is 8 patients. With this

information at hand, it is clear that implementing the redesigned ODT process could lead to a

capacity loss due to appointment slots held open. Therefore, it is necessary to estimate the

expected capacity loss due to the implementation of the redesigned ODT process.

The first step is to estimate the daily demand of resources (PDT nurse) for patients using PDT

and following the redesigned ODT process. This demand is defined by new patients arriving to

consultation every day. It is estimated that approximately 8.4% of the patients belongs to the

patient group diagnosed with BCC and refereed to be treated using PDT. Therefore, with the

information about the amount of new patients arriving per day we can estimate their daily

demand. In this analysis is required to consider one restriction imposed in the system, which is

the fact that only patients arriving at the clinic for diagnosis during the morning can receive the

results from the biopsy and are able to be treated during the same day. Due to this restriction we

can only include new patients arriving during the morning in the estimated demand. The

expected daily demand for PDT- ODT patients is shown in Table 6, expressed in amount of

patients and in minutes demanded of PDT nurse.

24

Table 6- Expected daily demand of resources PDT-patients by the redesigned ODT process

Day Admission new

patients

Number of slots

open for PDT

Number of slots open

for PDT (rounded)

Resource use

(in minutes)

Monday 25 2.1 2 60

Tuesday 25 2.1 2 60

Wednesday 25 2.1 2 60

Thursday 15 1.26 1 30

Friday 40 3.36 4 120

Saturday 0 0 0 0

Sunday 0 0 0 0

Total 10.92 11 330

The next step consists of estimating the current capacity level available for the PDT every

day. There is one nurse every day available for treatment, but they only schedule a maximum of

8 patients every day. This amount of patients will be used to define the target utilization level for

the nurse PDT. Using this information it is possible to identify the percentage of time demanded

for ODT-patients every day and to define the percentage of appointment slots that must be held

open every day for this implementation as shown in Table 7.

Table 7- Estimated percentage of appointment slots open for PDT-patients by the redesigned ODT

process

Day Resource use

(in minutes)

Current

availability

Demanded open

slots (%)

Monday 60 240 0.25

Tuesday 60 240 0.25

Wednesday 60 240 0.25

Thursday 30 240 0.125

Friday 120 240 0.5

Saturday 0 0 0

Sunday 0 0 0

From Table 7 we can conclude that the maximum percentage of appointments held open

in the current situation should not exceed 50%, because it will represent a loss in capacity that

will not be utilized. Another insight provided by this analysis is that the expected percentage of

appointments that must be held open per day is 27.5% which is the average of the % of open

slots demanded per day shown in Table 7. This percentage represents 2 appointment slots held

open every day (0.275*8=2.2). However, it is important to consider that the percentage of

appointment slots held open depends on the daily demand. The demand used for the previous

analysis does not include the total amount of patients arriving to the clinic during the week, but

only patients scheduled for a consultation during the morning.

Therefore, how can we extend the redesigned ODT process to all new patients treated by

PDT? One option is to allocate all the appointment slots for new patients during the morning.

25

This requires a modification in the current admission rule in which every doctor receives five

new patients per block. However, this change will duplicate the amount of patients demanding

the redesigned ODT process for treatment every day, maintaining the same amount of

appointment slots available. Therefore, four appointment slots must be held open instead of two

considering the new demand. The same analysis is exposed as follows for the patients referred

for Excision by the redesigned ODT process.

3.3.2 Planning for Excision

The admissions planning for the Excision is also scheduled in advanced and the recent

average waiting time for Excision is approximately 40 days. The resources assigned for the

Excision are shared with the MOHs, and includ a specialist, doctor and a Excision-nurse as

shown in Table 5. Similar to the previous analysis, the current capacity level should be compared

to the demand. In this case the daily demand is calculated adding the resource use for the MOHs,

for the scheduled patients for Excision and the expected demand for patients following the

redesigned ODT process, as shown in Table 8. The expected time demand of patients for ODT is

calculated in the same way as the estimation for PDT patients by the redesigned ODT process,

using the new patient’s arrival based on their planned admission for consultation. The percentage

of patients estimated to be diagnosed with BCC and referred to be treated using Excision is

approximately 8.4% from the total population arriving. This percentage was gathered from a

database provided by the clinic collecting all the cases treated from 2005 until 2009.

Nevertheless, it could be argued that by adding the demand of scheduled patients with all

the expected patients for Excision we are duplicating the population of patients looking for the

Excision. Actually, the approach here is to evaluate if with the current demand, the clinic could

add the new patients arriving for treatment (ODT-patients) to the original flow of patients. If not,

then we need to follow the same approach used for the PDT, in which it is estimated the

percentage of appointment slots that must be held open for the patients by ODT.

The demand shown in Table 8 is estimated per resource type. The demand of the

scheduled patients was defined using the master schedule for treatment prepared at the clinic

every six months. This schedule includes the operating rooms, specialists and doctors and the

treatment type (MOHs or Excision). The estimation of the resource use per patient (from Table 5)

is multiplied by the daily demand to define the resource use in minutes every day for operating

rooms, doctors, specialists and nurses. Nowadays, the clinic has some restrictions in terms of the

maximum amount of MOHs procedures that they can perform per day, limited to 4. The capacity

reserved for the treatment exceeds the time demanded by these patients, suggesting the

possibility to improve the resources utilization including the new patients treated by Excision

with the redesigned ODT process. The next step is to compare the daily demand with the supply

per resource type (See Table 9 ).

26

Table 8- Estimated resources use by patients treated with MOHs and Excision

MOHs

Day per pat per day per pat per day per pat per day per pat per day

Monday 4 50 200 37.5 150 57.5 230 50 200

Tuesday 4 50 200 37.5 150 57.5 230 50 200

Wednesday 0 50 0 37.5 0 57.5 0 50 0

Thursday 4 50 200 37.5 150 57.5 230 50 200

Friday 0 50 0 37.5 0 57.5 0 50 0

Saturday 0 0 0 0 0 0 0 0 0

Sunday 0 0 0 0 0 0 0 0 0

Total (in minutes): 600 450 690 600

Total (in hours): 10.0 7.5 11.5 10.0

Scheduled Excision


Monday 0 30 0 10 0 30 0 30 0

Tuesday 36 30 1080 10 360 30 1080 30 1080

Wednesday 0 30 0 10 0 30 0 30 0

Thursday 0 30 0 10 0 30 0 30 0

Friday 12 30 360 10 120 30 360 30 360

Saturday 0 0 0 0 0 0 0 0 0

Sunday 0 0 0 0 0 0 0 0 0

Total (in minutes): 1440 480 1440 1440

Total (in hours): 24.0 8.0 24.0 24.0

ODT-Excision


Monday 4 30 120 10 40 30 120 30 120

Tuesday 4 30 120 10 40 30 120 30 120

Wednesday 4 30 120 10 40 30 120 30 120

Thursday 3 30 90 10 30 30 90 30 90

Friday 7 30 210 10 70 30 210 30 210

Saturday 0 0 0 0 0 0 0 0 0

Sunday 0 0 0 0 0 0 0 0 0

Total (in minutes): 660 220 660 660

Total (in hours): 11.0 3.7 11.0 11.0

Total demand

Day

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Sunday

Total (in minutes):

0 0 0 0

2700 1150 2790 2700

570 190 570 570

0 0 0 0

120 40 120 120

290 180 320 290

320 190 350 320

1400 550 1430 1400

Patient's

arrival

Operating Room

(OR)

Specialists Doctors Nurse Excision

Operating Room Specialists Doctors Nurse Excision


Patient's

arrival

Operating Room

(OR)


Patient's

arrival

Operating Room

(OR)

27

Table 9-Match between daily demand and supply per resource type for MOHs and Excision

The comparison between the demand of resources per day and their availability provides

some insights into the factors that could diminish the implementation of the redesigned ODT

process. It can be observed in the bottom of Table 9, that the total amount of resources per week

is sufficient to fulfill the current demand and the demand of new patients for Excision by the

redesigned ODT process. However, when the comparison is done on a daily basis it can be

observed a very variable level of utilization for all the different resources, even in some cases

unable to fulfill the expected demand. This analysis is only performed using the current capacity

level at the clinic. The variability due to some peak demand during a day in the weeks is

assumed to be managed by treating the patient during the next day available without considering

overtime or additional capacity. Under this assumption, results suggest that in order to

implement the redesigned ODT process it is necessary to change the allocation of resources

within the different days of the week. Also, the availability of two internal operating rooms

increases the flexibility and provides room for improvements.

3.4 Concluding from the process analysis

The analysis of the actual process for patients diagnosed with BCC at the dermatology

oncology outpatient clinic in the Catharina hospital provided some insights into the feasibility of

implementing the redesigned ODT process for patients referred for PDT and Excision. The

approach followed in this chapter used the demand-supply model for the analysis of hospital

processes described in Vissers and Beech (2005). This approach proposed to evaluate the match

between demand and supply in the clinic.

The idea is to identify different patient groups combining the patient’s types and

treatments profiles to estimate their individual demand of resources. Using this information and

the inflow of patients for the different groups, it is possible to estimate the total demand of

resources for a specific period, starting from one week until one day. This demand is compared

with the current capacity level at the clinic. Using this information we can estimate the

percentage of resources utilization and evaluate the feasibility of this implementation.

It was identified that the feasibility of the implementation is different for patients treated

with Excision and patients with PDT. For the patients treated with PDT, it is necessary to define

a percentage of open appointment slots available every day for the treatment of new patients with

the redesigned ODT process, because nowadays the resources available for PDT is limited to one

nurse per day, which is sufficient to fulfill the current demand but leave no room for the new

arrivals. The percentage of appointment slots that must be held open every day is on average

27.5% to fulfill the arrival of patients for PDT by the redesigned ODT process.

Day demand supply % util. demand supply % util. demand supply % util. demand supply % util.

Monday 320 960 0.333 190 960 0.198 350 960 0.365 320 960 0.333

Tuesday 1400 2400 0.583 550 1440 0.382 1430 1920 0.745 1400 1440 0.972

Wednesday 120 960 0.125 40 480 0.083 120 0 120 480 0.250

Thursday 290 960 0.302 180 960 0.188 320 480 0.667 290 480 0.604

Friday 570 1440 0.396 190 960 0.198 570 480 1.188 570 720 0.792

Saturday 0 0 0 0 0 0 0 0 0 0 0

Sunday 0 0 0 0 0 0 0 0 0 0 0

Total (in minutes): 2700 6720 0.4018 1150 4800 0.240 2790 3840 0.7266 2700 4080 0.662

Operating Room (OR) Specialists Doctors Nurse Excision

28

Nevertheless, the situation for patients treated with Excision is different. They have

plenty capacity available to fulfill the weekly demand of patients arriving for Excision by the

redesigned ODT process, but this capacity needs to be allocated differently among days, because

now there is a significant variability between the resources utilization between days in the week

and even in some cases the resources are not sufficient to fulfill the daily demand. Doctors seem

to be the most critical resource for this reallocation.

One conclusion is that it is feasible to implement the redesigned ODT process in the

clinic for patients referred for PDT and Excision, considering that the current capacity level is

sufficient to fulfill the current demand. Also, another conclusion is that significant factors for this

implementation are: the admission rule, the percentage of appointment slots held open for the

PDT - ODT patients, and the allocation of capacity throughout the week for Excision-ODT

patients.

The analysis performed in this section is considered as a first step to get some insights

into the problem and the alternative ways to improve the current situation. However, in this

analysis the inflow of patients and the time durations for each activity were assumed to be

deterministic which does not correspond to the real situation in the clinic. It is well known that

the time of a surgery or consultation changes significantly among patients. Even more important

is it that the arrival of patients with a specific type of diagnosis also changes from one day to

another and this impacts the utilization of the resources at the clinic, assuming that we are

proposing to let some appointment slots open for patients that could arrive every day. In order to

improve the effect of changes in the actual system it is proposed to use a simulation model (Lee

and Asllani, 2001), which allows to introduce the variability expected in the patient’s arrival and

the processing times duration.

The next chapter presents a development of a simulation model for the actual process and

for the redesigned ODT process. The simulation model described in Chapter 4 will be used for a

quantitative evaluation of the scenarios proposed in Chapter 5.

29

4 MODEL DEVELOPMENT This section is devoted to the development of the model to simulate the current and the

redesigned ODT process followed by patients diagnosed with BCC within the dermatology

oncology outpatient clinic at the Catharina hospital.

4.1 Simulation model description

A simulation package called Arena 7.0 from Rockwell Software is used for the

development of this model, considering that it provides tools to support the analysis needed. This

section includes the main characteristics related to the data collection, a detailed description of

the processes characteristics and how they were translated in the model. Details about the model

elaboration are included in Appendix C.

4.1.1 Input Data

The absence of stored event logs to discover the process flow, suggested the need to

collect the data manually from the people involved in the process (Sharp and McDermott, 2001).

The first step was to interview experts to identify critical resources in the process, including the

most experienced personnel for each activity. The objective of those interviews was to identify

the main characteristics of the process, their activities and all resources required.

The data regarding the duration of process activities was not available and neither the

arrival pattern of patients per day. Therefore, an estimation of those durations was provided by

experts, to define the input probability functions. For the arrival pattern, it was considered the

expected amount of patients to be treated every year, as discussed in more detail in this section.

Moreover, stored information related to the patients treated in the clinic between 2005 and 2009

was used to define the percentage of patients per type of treatment. The main input data required

to build the model can be grouped as follows:

- Arrivals:

Every year the clinic agrees with the hospital to receive a minimum amount of new

patients during the year. For their planning, they consider this amount of patients and assume 40

weeks per year, subtracting the different holidays of their personnel during the year. The amount

of new patients arriving every year has been increasing from approximately 6,500 patients during

the first year (2005) until an actual expected amount of 10,500 for the current year. Nowadays,

the number of patients calling for an appointment every day is not known because the hospital

always keep a waiting list that acts as a buffer reducing the variability present in this arrival

process. However, it is very important to consider the variability inherent in the daily arrival of

patients, especially to measure the system performance with the implementation of the

redesigned ODT process.

The exponential distribution seems to be appropriate to model the inter-arrival time of

calls from new patients, considering that it is suitable to describe the times between events that

occurs independently and continuously. In this case the arrival of a call does not depend on the

time of the previous call showing the independence of those events, and the variable time is

continuous. This distribution also has the advantage that it introduces a significant variability in

the arrival process. It is also specified in the model that patients only call during weekdays from

8:30 AM-5:00 PM. Therefore, with this schedule it is defined that arrivals occurred only one

30

time every day and the amount of calls arriving follows a Poisson distribution with a parameter

lambda equal to 53. The parameter used for the Poisson distribution is the average daily arrival

of patients in the current year in which the simulation begins, as shown in Table 10.

Table 10-Parameter for the Poisson distribution

Year Expected patients

arrival per year

average weekly

arrivals

average daily

arrivals Increment

2005 6,500 163 33

2006 7,500 188 38 5

2007 8,500 213 43 5

2008 9,500 238 48 5

2009 10,500 263 53 5

A sensitivity analysis was conducted to identify the impact of the selected distribution in

the performance. The results are shown in Appendix F and present a bias in the average

throughput time for patients treated by Excision when the distribution used to simulate the

number of new patients arriving every day is Poisson instead of deterministic. The difference is

approximately 5 days on average.

In the current situation, there is no data available about the current arrival pattern of new

patients to the clinic, only historic data of patients scheduled for treatment. With this lack of

information and the knowledge about the influence of the selection of the distribution for the

arrival of new patients in the system performance, it was chosen the distribution that introduces a

higher variability to the system. This distribution provides results for a worst case scenario.

- Service Time Distributions:

The service time for all the activities in the process were provided by the resources

directly involved in the process, since no historical data was available. For cases were no data is

available, Law and Kelton (2000) suggested assuming a triangular or beta distribution, and

estimating the parameters. In this case the triangular distribution was selected, since the

parameters could be easily estimated by the personnel from the clinic which are not familiar with

statistics. The triangular distribution is defined by the minimum, most likely (modal) and

maximum value which is a natural way to estimate the time required to perform a task (Kelton,

2004).

Even though the personnel used for the estimation of those times is very experienced,

they may not provide perfect estimates of the parameters for the distributions. The appropriate

approach as suggested by Wilson (1990) would be to collect sample data and hypothesize a

standard distribution. Then, the parameters of the corresponding distribution could be estimated

using the data collected to assess the adequacy of the fit with a goodness-of-fit test. With this

approach it is possible to identify if the hypothesis that the data followed the corresponding

distribution cannot be rejected. Sample data was collected observing the activities in the process,

but the amount of observations was not sufficient to perform goodness-of-fit tests. Therefore, we

rely on the opinion of personnel involved in the process and small samples were taken to identify

if they lay between the parameters described for the distribution. The results from the activities

evaluated are shown in Appendix D, and were used as input for the model.

31

- Patients classification:

Patients are classified in two groups: new and control patients, using an attribute called

PatientType equal to 1 and 2, respectively. Those patients are also classified in: regular patients

or ODT. The regular patients follow the actual process flow at the clinic, while the second group

are diagnosed and treated during one day, following the redesigned ODT process.

From the total amount of patients in consultation, approximately 40% are diagnosed

cancer free and the rest requires further clinical tests. The results from those tests also define

different routings for the patients in the process. Data obtained from the clinic records included

the percentage of patients that are sent to diagnostic testing, as well as the percentage that are

referred for treatment and the type of treatment. Using these percentages, patients are also

categorized based on their treatment as: (1) 19%, non BCC patients; (2) 44%, self-treatment;

(3)14%, PDT; (4) 9% for MOHs, and (5) 14% for Excision.

- Resources:

The resources considered in the model were grouped in four different sets of resources,

each one with a specific scheduled availability. Those resources were the same described in

Table 1. This section will describe how they were modeled. First, the Operating Room which

includes 5 different resources: OR1, OR2, KCI1, KCI2 and KCI3. Internal operating rooms

(OR’s) are available five days a week, in two shifts from 8:30AM- 12:30 and 1:30-5:00 PM with

a one hour break for lunch, while the external ones (KCI) are only scheduled to operate on

Tuesday and one block during Friday.

Secondly, the Specialists which include two types of resources in a set called specialist:

(1) the specialists for supervising the consultation and (2) specialists assigned for treatment. To

schedule their availability the time was ignored in which they are assigned for research or

another activity not included in this model. The amount of specialists for supervision available

per day is obtained from the weekly schedule shown in Table 14, and available for treatment in

Table 17.

Thirdly, the Doctors which include two types of resources in a set called doctors: (1)

doctors assigned for consultation and (2) doctors assigned for treatment. To schedule their

availability it was ignored the time in which they are assigned for research. The amount of

doctors available per day is obtained from the weekly schedule shown in Table 13, and available

for treatment in Table 16.

Finally, the Assistants which group the secretaries and nurses based on their main

functions in the process. The resources included in this model were: (1) front desk, with a

scheduled capacity of 2 available every working day; (2) secretary, with a scheduled capacity of

3; (3) biopsy, including 2 planned resources per day; (4) PDTnurse, following a regular Work

week schedule because it has a scheduled capacity of 1 nurse per day; and finally (5) Excision

nurse, including the nurses available to assist during the MOHs and Excision with a variable

availability of resources during the week from one until three nurses per days. These values are

assigned based on the current schedule for the nurses.

32

4.1.2 Model flow logic

The model developed includes two significant processes that interact to provide the service

for patients with basal cell-carcinoma arriving at the clinic. First, the patients flow logic,

including the activities followed by a patient since they contact the clinic for an appointment,

receives the diagnosis by a doctor until finish the recommended treatment. Secondly, it includes

the appointment planning, which includes the internal process or group of decisions involved in

the assignment of an appointment date and time with a specific doctor for consultancy and for

treatment.

The model was built integrating: (1) the simulation model in Arena to build the patients

flow logic described previously; (2) a code using Visual Basic for Applications (VBA) for the

appointment planning; And finally (3) an integrated Excel user interface with the Arena

simulation model to simplify the review of specific output values and the model verification. All

explained in detail in Appendix C.

4.1.3 Performance measures

The statistics collected in the model includes the throughput time per patient group and

the amount of patients treated. The statistics are collected independently between patient’s

groups and included the following: BCC patients referred to self-treatment, regular patients for

PDT, patients for PDT- following the redesigned ODT process, patients referred for MOHs,

regular patients referred for Excision, and patients for Excision by the redesigned ODT process.

The throughput time is defined as the time since patients arrive to the consult meeting,

until the first treatment is completed. This information is important to identify how the

implementation of the redesigned ODT process does not only affect the throughput time of the

patients using the redesigned ODT process, but also the average throughput time including all

the patients treated. The resources utilization and average waiting time in all the queues

throughout the process are also collected with the Arena simulation tool.

4.1.4 Verification and Validation

The verification consists evaluating if the computer model represents the conceptual

model authentically. This verification was performed while building the model isolating the

different parts of the model and identifying if they provide the expected values. The interface

between the model built using the VBA code and Excel was mostly developed to be able for this

verification. A sample of the output generated in Excel is shown in Table 11. This output

corresponds to the planning of appointments for Excision. For instance, it shows that the

appointment day is never set before the day when patients contact the clinic, as expected.

Additionally, the position is never greater than 6 per block in one day using the same room. It

can be observed that in line 7, the position restart to one, but nextcol (representing the operating

room) change from 3 to 4. In general, the model was verified dividing it in different parts

manageable to identify that changes in input parameters produce the expected change in the

output (Kelton et al, 2004).

33

Table 11 – Output generated in Excel for the model verification

Once the model is verified, the next step is to validate if it behaves according to the actual

system. There are several methods to validate a simulation model. In this case, the statistic of

interest for the validation of the model is the throughput time for patients per treatment type. To

calculate those statistics the run length was set to one year, represented by 280 days (40 weeks*7

days a week), using 50 replications with a warm-up period of 160 days. The procedure to

estimate the warm up period is described in detail in Appendix G. The results obtained were an

average throughput time for new and control patients treated by PDT is 14.35 days comparable

to the current expected waiting time of 12-21 days approximately. For the Excision, the 95%

confidence interval for the average throughput time for new and control patients is from 35.0 to

35.7 days. This value lies between the current expected waiting time, which varies between 21

and 45 days.

The main interest of this study is to evaluate different scenarios for the redesigned ODT

process that is not in operation at the moment at the clinic. Therefore, the next section will

explain the changes required for the redesigned ODT process.

4.2 Modeling the redesigned one-day treatment (ODT) process

4.2.1 Model flow logic

The main differences introduced with the ODT-redesigned process are related to the

reduction in the time spent by the pathology department to provide the results from the biopsy.

Nowadays, the results provided from pathology are sent to the secretary approximately two

weeks after the samples are taken. For the redesigned ODT process, samples from the biopsy

taken in the morning before 11:00 AM are sent to the laboratory and the results are available in

approximately 2 hours. There is also a reduction in the assistant’s utilization regarding

administrative tasks. Details related to the changes required in the model to implement the

redesign are explained in detail in Appendix E.

4.2.2 Verification and Validation

The verification was performed in the same way as described for the regular process.

However, for the validation, a sample data of 20 patients was used. This data consists on the

results gathered from a pilot study in which 20 patients were invited to receive their treatment on

the same day of diagnosis. This pilot test intended to evaluate the feasibility to implement this

new process and the willingness of patients to be treated on the same day. The test consisted on

Patient_ID PatientType call-day week-dayAppointment

daynextrow_treat4 nextcol_treat4 position

94 1 18 6 21 12 3 1

26 1 18 6 21 12 3 2

92 1 19 6 21 12 3 3

119 1 19 6 21 12 3 4

118 1 19 6 21 12 3 5

36 1 19 6 21 12 3 6

3 1 20 6 21 12 4 1

187 1 21 2 24 4 1 1

154 1 24 3 25 5 3 1

34

the identification of patients with solid or superficial basal cell carcinoma and their referral to

receive Excision or the PDT during the same day of diagnosis.

The validation consisted on identifying the average throughput time for patients. The

results gathered varied between 3 to 25 hours, with one patient treated in the next day available.

The number of observations was not enough to create an accurate confidence interval. Therefore,

the results gathered from the simulation were varied between 3 to 52 hours for PDT and from 3

to 17 hours for Excision.

4.3 Concluding from the modeling An elaboration of the simulation model is described in this chapter, to represent the

current situation followed by a description of the changes incorporated to model the redesigned

ODT process. The model was constructed using a simulation tool called Arena 7 for the flow

logic, supported by a visual basic code to model the planning of the different appointments for

consultation and treatment. Also, an interface with Excel was used to facilitate the verification of

the model.

During the model development, it was validated using data from the actual system and

comparing with the statistics gathered from the model. The main performance measure used for

the validation was the patient’s throughput time per type of treatment. The results obtained were

an average throughput time for new and control patients treated by PDT is 14.35 days

comparable to the current expected waiting time of 12-21 days approximately. For the Excision,

the average throughput time for new and control patients is 34.25 days. This value lies between

the actual expected waiting time, which varies between 21 and 45 days.

Moreover, the model was modified to incorporate the necessary changes to implement

the redesigned ODT process. Those changes included a new classification for the patients (using

an attribute) in regular or ODT patients. This classification determines a difference in the steps

that they should follow for their treatment. The most significant change in their path is that

pathology provides the results of the biopsy in approximately less than two hours, while in the

actual system the results are provided almost two weeks after the biopsy. The reduction of time

for this activity is critical for the success of the implementation of the redesigned ODT process.

Therefore, the further analysis is performed assuming the reduction in the waiting time of this

activity. Using the model developed, the next chapter presents alternative scenarios to evaluate

the effect of changing some parameters in the model, in case of implementing the redesigned

ODT process.

35

5 SCENARIOS

5.1 Introduction The previous chapter described a simulation model used to represent the actual process

and the redesigned ODT process followed by patients diagnosed with BCC at the Catharina

hospital. Simulation has shown to be a good approach to analyze different scenarios as explained

previously in the literature review. Therefore, the factors identified in the process analysis are

used in this chapter to define different settings to build the scenarios most promising to improve

the actual system performance with the implementation of the redesigned ODT process.

5.2 Selection of factors Using the results gathered from the capacity analysis in Chapter 3, some factors were

identified as critical for a successful implementation of the redesigned ODT process. They are

described as follows:

5.2.1 Admission rule The admission planning consists in scheduling the admission of patients according to

medical priorities and their resources requirements. It involves important decisions, including:

(1) the number of patients admitted for a specialty each day; (2) the priority between patients,

studied by Klassen and Rohleder (2004); (3) the placement of appointment slots per patient type;

(4) the duration of each appointment slot. The current admission planning divide every day in

two blocks, in which the maximum number of patients admitted for consult meetings per doctor

is 17, a maximum of 5 new patients per block per doctor and 12 control patients (See Appendix

H ). The first two appointment slots are reserved for control patients followed by one new

patient. The amount of time reserved for each consult meeting varied between patient types, 5

minutes assigned for control patients and 15 minutes for new patients. After two control patients

there are 5 minutes blocked to manage the variability present in the process.

Nowadays, the admission of patients for consult meeting is independent from the

admission of patients for treatment. However, for the implementation of the redesigned ODT

process this situation change, as shown in Chapter 3. The admission of new patients for consult-

meeting influences the inflow of patients to be treated following the redesigned ODT process

every day. Therefore, it is important to identify how the admission rule could be modified to

increase the number of new patients treated following the redesigned ODT process. It is also

important to consider that the patients should complete their diagnosis, picture and biopsy during

the morning to receive the results from pathology during the same day. Under those restrictions,

it is proposed to evaluate the feasibility of implementing the redesigned ODT process for all the

new patients arriving at the clinic who are interested to be treated on the same day. It implies that

the patient has the option to choose between been included in the waiting list for a scheduled

appointment, or been treated during the same day of diagnosis. In this study it is evaluated a

proposed alternative in which the amount of new patients assigned for each doctor per block is

changed from 5 to 10 every day. The proposed allocation of appointment slots is shown in

Appendix H. It includes 10 new patients per doctor per block during the morning and 7 control

patients. Therefore, all the appointment slots during the afternoon are held for control patients.

36

It was suggested by some doctors that 5 minutes for appointment slot for control patients

was not sufficient. This was approached before by the clinic blocking 5 minutes after two slots

for control patients. The duration of the appointment slots for control patients is set to 10 minutes

for the proposed plan, removing the 5 minutes block in the current plan. Finally, the admission

rule will be explored in two different levels: actual and proposed as described before.

5.2.2 Percentage of open slots available for PDT and Excision The analysis in section 3.3.1 concluded that for the current demand, the average amount of

appointment slots that should be held open is 27.5% for ODT-patients referred for PDT. The

amount of appointment slots available for PDT every day is 8, indicating that on average 2.2

rounded to 2 appointment slots should be held open every day. However when the inflow of new

patients increase, this percentage also increases considering that the resources available remain

the same. Considering the interest of the clinic to offer the opportunity to be treated during the

same day of diagnosis to all the new patients, it is necessary to evaluate the amount of

appointment slots that should be held open and the effect on the performance for other patient

groups, for instance control patients. For the PDT, 4 appointment slots should be held open as

estimated in Chapter 3.

For the Excision, it was found in the analysis that the resources available are underutilized,

allowing the inclusion of extra patients for treatment. This analysis was performed considering

the worst case scenario, in which all the new patients referred for Excision can be treated during

the same day. In this case, 5 appointments slots can be held open for Excision every day. While

for the actual admission rule with only half of the new patients arriving for treatment during the

same day, only 3 appointment slots should be reserved for those patients. Finally, two different

levels will be evaluated for this factor: (1) Appointment slots open for PDT equal to 2 and 3 for

Excision; and (2) Appointment slots open for PDT equal to 4 and 5 for Excision.

5.2.3 Resources allocation for Excision The analysis identifies that the amount of resources available for the Excision and MOHs

are sufficient to fulfill the current demand and additionally to treat the patients arriving every day

to follow the redesigned ODT process. However, it was also discovered that the current

allocation of resources shown in Table 12 requires improvement to support this implementation.

Table 12 – Current allocation of resources for MOHs and Excision

Block

Doctors -

consult meeting

Doctors -

treatment

Specialists -

superv. consult

Specialists -

Excision and MOHs

Nurses -

Excision

Monday morning 5 2 1 2 2

Monday afternoon 5 2 1 2 2

Tuesday morning 5 4 1 3 3

Tuesday afternoon 2 4 1 3 3

Wednesday morning 5 0 2 1 1

Wednesday afternoon 5 0 2 1 1

Thursday morning 3 1 2 2 2

Thursday afternoon 4 1 2 2 2

Friday morning 8 0 2 1 1

Friday afternoon 6 2 2 2 1

37

In order to improve the resources allocation, a mathematical model was constructed to

translate this planning problem. This problem is formulated using integer linear programming

and solved using a heuristic method proposed by OpQuest, which is capable of finding optimal

or near optimal solutions to complex problems involving elements of uncertainty. This method

evaluates the responses from the current simulation run and compares it with previous runs, to

define a new set of values for the control variables which are evaluated by running the model

built in Arena. The heuristic technique used by OpQuest has proven to be very efficient finding

optimal or near optimal solutions.

The objective function defined in the model is to minimize the average throughput time

for BCC patients treated with Excision. The decision variables are the amount of doctors and

specialists, allocated every day for consultation and for treatment (Excision and MOHs); and

nurses available for treatment: PDT or Excision every day at the clinic. All the variables were

defined as integers.

The constraints defined are:

o Amount of doctors available <= 8 per day

o Amount of appointment blocks for consult-meeting available >= 48 per week

o Amount of appointment blocks for consult-meeting available <= 64 per week

o Amount of specialists available <= 7 per day:

o Amount of appointment blocks to supervise consult-meeting available <= 36 per week

o Amount of nurses (expressed in working blocks) available per week <= 18

The optimization was performed running 500 simulations, each replicated 3 times. This

optimization is applied to every combination of the admission rule and appointment slots held

open, to guarantee an optimization for each individual setting. Therefore, four different

combinations were optimized: (1) actual admission rule without open appointment slots; (2)

actual admission rule with open appointment slots in the amount described before per treatment

type; (3) proposed admission rule without open appointment slots; and (4) proposed admission

rule with open appointment slots. Using the factors analyzed in this section, it is possible to

propose some alternative scenarios that could guide to a successful implementation of the

redesigned ODT process.

5.3 Definition of scenarios

In this section some scenarios will be proposed using a combination of the factors previously

described: (1) admission rule, (2) appointment slots held open for PDT; and (2) the allocation of

resources shared for Excision and MOHs, actual or optimal. A scheme to define how those

factors were combined to develop scenarios is shown in Figure 6.

Figure 6- Scenarios scheme, derived from the combination of three factors

Each scenario is defined to

the different combination of factors. A

advantages or disadvantages of each scenario is described

5.3.1 Scenario # 1

This scenario represents

advance, without appointment slots open for ODT

for patients arriving to the consult meeting, and

offered by this scenario is that it tries to maximize the use of resources with the drawback that is

not considering the impact for patients. All patients are expected to stay in a waiting list for

several weeks before treatment.

5.3.2 Scenario #2

The difference between this scenario and the previous one is that it uses an optimal

allocation of resources for Excision and MOHs

evaluation of how the throughput time for patients referred for Excision

reallocating available resources. The advantage expected from this scenario is an improvement in

the throughput time of new patients treated by Excision. However,

any alternative of improvements for PDT patients.

5.3.3 Scenario #3 This scenario evaluated how the actual admission rule and resource allocation could be

improved including the option for new patients diagnosed during the morning to be tre

the same day. Two appointment slots are held open every day for patients referred for PDT

following the redesigned ODT

For patients treated by Excision, 3 appointment slots are open

advantage of this scenario is

38

Scenarios scheme, derived from the combination of three factors

is defined to provide some insights into the impact in the performance of

the different combination of factors. A brief description and explanation about the expected

advantages or disadvantages of each scenario is described below.

scenario represents the current situation, in which all the patients are scheduled in

advance, without appointment slots open for ODT-patients. It considers the actual admission rule

for patients arriving to the consult meeting, and current resources’ allocation.



several weeks before treatment.


allocation of resources for Excision and MOHs (See Table 24 in Appendix

the throughput time for patients referred for Excision

resources. The advantage expected from this scenario is an improvement in

the throughput time of new patients treated by Excision. However, this sce

any alternative of improvements for PDT patients.

evaluated how the actual admission rule and resource allocation could be

improved including the option for new patients diagnosed during the morning to be tre

. Two appointment slots are held open every day for patients referred for PDT

redesigned ODT process, and 6 appointment slots available for scheduled patients.

For patients treated by Excision, 3 appointment slots are open every day.

advantage of this scenario is the flexibility that it incorporates to the process, allowing some

the impact in the performance of

explanation about the expected

situation, in which all the patients are scheduled in

patients. It considers the actual admission rule

allocation. The advantage




Appendix I). It allows for the

could be improved by

resources. The advantage expected from this scenario is an improvement in

this scenario does not offer

evaluated how the actual admission rule and resource allocation could be

improved including the option for new patients diagnosed during the morning to be treated on

. Two appointment slots are held open every day for patients referred for PDT

process, and 6 appointment slots available for scheduled patients.

every day. The expected

incorporates to the process, allowing some

39

patients to be treated during one day. The consequence is an expected reduction in the average

throughput time for new patients. The disadvantage is that the utilization of the nurse for PDT

will change based on the variability in the arrivals.

5.3.4 Scenario #4 This scenario is similar to the scenario 3, but with a reallocation of resources required for

the Excision and MOHs. The advantage offered by this scenario is the alignment of the demand

and the supply of resources, considering the new requirements imposed by the arrival of patients

every day for treatment. The disadvantage is that it does not offer any chances of improvement

for ODT-patients treated by PDT.

5.3.5 Scenario #5: This scenario evaluates the behavior of the current situation (resources allocation and all

appointments scheduled), assuming a different admission rule. This scenario brings insights into

the isolated effect of the new admission rule, comparing its results with scenario 1. Also, the

isolated effect of the appointment slots open compared to scenario 7.

5.3.6 Scenario #6: This scenario is similar to scenario 5, but considers a resource reallocation to reduce the

throughput time for patients treated by Excision. This scenario provides insights into the isolated

effect of the resources allocation compared to scenario 5.

5.3.7 Scenario #7: This scenario proposes a reduction in the average throughput time for new patients

following the redesigned ODT process, both treated by PDT and Excision. It evaluates how an

increase in the demand from new patients for the redesigned ODT process could be handled with

the current allocation of resources. This scenario could reduce the average throughput time for

new patients treated by PDT and Excision. The disadvantage is that the current allocation of

resources does not necessarily provide support for the unscheduled demand.

5.3.8 Scenario #8: This scenario is similar to scenario 7, but with an optimal reallocation of the resources

available for Excision and MOHs. This scenario is very promising because it includes all new

patients and provides a better alignment between the new daily demand of resources and their

current availability.

5.4 Concluding from scenarios

Three different factors were identified as important for the implementation of the

redesigned ODT process in the clinic: (1) the admission rule, (2) the percentage of open

appointment slots available for PDT and Excision, and (3) the allocation of resources for

Excision. The admission rule includes several decisions, such as: the number of patients admitted

for a specialty each day, the priority between patients, the placement of appointment slots per

patient type and the duration of each appointment slot. Two different alternatives are evaluated

in this chapter. The first alternative is the current admission rule, which only allows half of the

new patients to decide whether they want to be treated on the same day of their diagnosis. This

option assigns 5 new patients per doctor for consult meeting during the morning and 10 control

40

patients. Appointment slots for each patient type are allocated to balance the workload of doctors

and specialists. However, the second option is called the proposed admission rule in which all

the appointment slots for new patients are assigned during the morning. This appointment rule

allocates 10 new patients and 5 for control patients per doctor during the morning, and the other

appointment slots for control patients are assigned during the afternoon. This option assigns a

higher priority to new patients increasing their probability to be treated on the same day.

The second factor is the percentage of open appointment slots available for PDT and

Excision. This percentage varied based on the demand, and it was estimated for the current

admission equal to 2 for PDT and 3 for Excision. The amount of appointment slots held open

with the proposed admission rule which duplicates the demand from new patients is estimated as

4 appointment slots for PDT and 5 for Excision.

The last factor evaluated was the resource allocation. This factor provides a better

alignment between the daily demand and supply from a different patient’s group, like the ODT-

patients for Excision. An integer linear model was developed to reduce the throughput time of

patients treated by Excision, by changing the allocation of resources throughout the week. The

resources included in this optimization were: the amount of doctors for consult-meetings every

day, doctors for treatment, specialists to supervise consult-meetings, specialists for treatment and

nurses for Excision.

Those factors were combined in two different levels each to define eight different scenarios

summarized in Figure 6. Every scenario provides a different insight into the impact of those

factors in the performance which are evaluated showing the results in the next chapter.

41

6 EXPERIMENTAL RESULTS 6.1 Introduction

This section presents the results gathered after running 50 replicates of each scenario

described previously. The performance measure of interest is the average throughput time for

patients diagnosed with BCC treated by Excision and PDT, and the amount of patients treated

following the redesigned ODT process. The results are compared among scenarios to identify the

most significant factors from the three evaluated, and their best combination.

The comparison among scenarios is performed using a paired t-test. The paired t-test

calculates the difference between each pair of scenarios. A null hypothesis is tested to identify if

this difference is significant or not. The second part of this chapter includes an analysis of the

results.

6.2 Results

6.2.1 Throughput time for patients treated by Excision The throughput time for new patients diagnosed with BCC and referred for Excision

differs among scenarios as shown in Figure 7 (values in the figure are expressed in hours). The

average throughput time for new patients, expressed in days are: 35.4, 38.3, 6.9, 3.6, 36.5, 36.8,

4.5 and 0.7 for scenarios from 1 to 8 respectively. From these results, it is very simple to identify

a difference between scenarios 1 and 4. However, it is not the case when comparing 35.4 and

38.3 days gathered from scenarios 1 and 2.

Figure 7- Confidence interval for the throughput time for new patients treated with Excision

42

A paired t-test for the means is performed to identify if the difference identified is a

significant difference between means for each pair of scenarios. Results show that the difference

between all the pair of means resulted from the evaluation of the different scenarios is

statistically significant at a 95% level of confidence, except for scenarios 4 and 7 and scenarios 7

and 8. It is also observed that scenarios 1, 2, 5 and 6 have the worst performance, while scenario

8 is significantly better, in terms of the average throughput time. A reduction of a 90% can be

achieved when comparing the current situation (scenario 1) with the best performance presented

by scenario 8.

Another statistics collected were the average throughput time for control patients treated

with Excision. The average throughput time for control patients in days is 34.6, 38.7, 15.0, 17.2,

36.3, 36.7, 14.6 and 15.0 corresponding to the scenarios 1 until 8 respectively. It is derived from

these values and from Figure 8, that the scenarios in which some appointment slots are held open

to treat new patients following the redesigned ODT process even exerted a positive influence on

the average throughput time for control patients. For the other scenarios, the effect on the

throughput time for control patients do not change significantly from the actual situation.

Figure 8- Throughput time for new and control patients treated with Excision

The achieved reduction in time is approximately 58%, which translates to almost two

weeks. This pattern is consistent not only assuming the actual admission rule, but also for the

proposed one in which all new patients can decide whether been treated on the same day or not.

The second option is evaluated with scenarios 7 and 8. The number of new patients that could be

treated during one day increases with the proposed admission rule from 118 and 120 patients in

scenarios 3 and 4, to 242 and 247 for scenarios 7 and 8 respectively. Another statistic of interest

is that 99.90% of the Excisions planned for ODT-patients during the simulation were performed

on the same day, without the need to reschedule patients for the next day available. Only in

scenario 4, on average of 18 minutes of overtime were needed for three ODT-patients during the

simulation.

1 2 3 4 5 6 7 8

New patients 849.0 920.0 141.0 86.6 875.0 882.0 108.0 17.9

Control patients 831.0 928.0 359.0 412.0 871.0 880.0 350.0 359.0

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

900.0

1000.0

hours

43

Also, the average throughput time for patients treated with MOHs was assessed,

considering that they share the resources with patients treated by Excision. Their performance is

not significantly affected, with a maximum increase of 2% of their current time, which is

approximately 1.5 days. To conclude, the scenario 8 over-performed compared to the others, not

only in terms of the average throughput time for new patients, but also providing improvements

for control patients up to 58% from the actual situation.

6.2.2 Throughput time for patients treated by PDT The throughput time for new patients treated with PDT also differs among scenarios as

shown in Figure 9. The average throughput times for new patients in days are the following: 14.7,

14.5, 2.1, 3.2, 14.8, 14.7, 1.53 and 1.5 for scenarios from 1 to 8 respectively. Similar to the

analysis performed for excision, a paired t-test for means is conducted. The results failed to

reject the hypothesis that there is a significant difference between means corresponding to

scenarios 1, 2, 5 and 6, providing the worst performance in terms of the average throughput time

for new patients treated with PDT. The best performance is provided by scenarios 7 and 8,

similar to the results gathered for patients treated with Excision. The improvement achieved

comparing the best and worst scenarios, is a reduction of 89.83% in the throughput time for new

patients treated with PDT.

It was incorporated in all the scenarios the decision that ODT-patients who already have

an appointment slot assigned for treatment but could not be treated on the same day, should be

scheduled for the next open slot available. However, if a treatment is initiated it has to be

finished on the same day, even though it represents to work overtime. The situation to work

overtime is not desirable. Therefore, the number of patients treated during overtime was

measured in the simulation, and the average overtime needed. Results have shown that with the

actual situation, all the patients could be treated during the regular working time. Conversely,

when ODT-patients are included in the analysis, approximately 5% of them (for scenario 4 and

5) and 11% (for scenario 7 and 8) required on average 46.5 minutes of overtime to complete their

treatment.

Figure 9- Confidence interval for the throughput time for new patients treated with PDT

44

The average throughput time for control patients treated with PDT is not significantly

different among scenarios, varying between 14.6 and 15.1 days approximately, except for

Scenarios 7 and 8, with an average throughput time of 25 days (See Figure 10). The number of

new patients that could be treated during one day increases from 144 and 137 patients in

scenarios 3 and 4, until 281 and 276 patients in scenarios 7 and 8 respectively. This represents

approximately an increment of 98%.

Figure 10- Throughput time for new and control patients treated with PDT

Another characteristic observed in the results provided by the scenarios 7 and 8 in Figure 9,

is a significant difference between the minimum and maximum values gathered from the

simulations. It could be explained because new patients that cannot find an open appointment

slot available to follow the redesigned ODT process are scheduled for a regular appointment. As

observed in Figure 10, the throughput time for patients following a regular appointment is

approximately 30 days for scenarios 7 and 8.

Even though some new patients become regular patients, these scenarios still provides the

best performance in terms of the average throughput time for new patients. The explanation is

that only few new patients are required to follow the regular process to be treated by PDT.

Finally, the next section presents an analysis of the factors influencing the results gathered from

the simulations and described in this section.

6.3 Analysis The results gathered in terms of average throughput time and numbers of patients treated

by the redesigned ODT process, from the simulation of the different scenarios proposed were

presented in the previous section. This section aims to translate those results in practical

recommendations, comparing the performance of the different scenarios described in this study.

Secondly, to propose the best combination of factors that should be implemented in the clinic.

1 2 3 4 5 6 7 8

New patients 353.0 349.0 51.4 76.0 356.0 352.0 36.6 35.9

Control patients 349.4 349.9 359.2 361.8 362.0 362.6 574.5 620.5

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

Hours

45

6.3.1 Comparative analysis of scenarios for Excision From the statistics gathered in the previous section it is possible to identify that the

percentage of appointment slots held open for ODT-patients is the most influential factor in

terms of the average throughput time for new patients. When a percentage of appointment slots

are held open for new patients, the statistics showed a reduction in this performance measure up

to 90.58% with the actual admission rule and 97.8% for the new admission rule. The

improvements achieved indicate that it is always advantageous to hold appointment slots open.

However, the maximum amount of appointment slots proposed to be held open for ODT-patients

treated with Excision is 3 for the actual admission rule and 5 for the proposed one, which should

be defined considering the amount of resources available and their current demand as done in

Chapter 3. The option to hold appointment slots open for ODT-patients also reduces the average

throughput time for control patients almost two weeks. Therefore, it has been consistently the

best alternative to be implemented in the clinic.

The second factor in terms of its impact in the performance is the admission rule, which

directly affects the demand. With the actual admission rule only approximately half of the

patients could be able to decide whether to be treated on the same day of their diagnosis or not.

With the proposed admission rule, all new patients could have this option. Based on the results, it

is always advantageous to select the alternative to offer the treatment on the same day of

diagnosis to all new patients instead of only one group, which could also create conflicts to the

clinic when defining the priorities between patients. With a simple relocation of the appointment

slots for new patients during the morning, the number of ODT-patients is duplicated and the

average throughput time for new patients is not affected. The average throughput time for control

patients is not significantly different between both alternatives. Additionally, results have shown

that no overtime is needed to cope with the demand.

The resources allocation is the third factor influencing the performance. The influence

exerted by this factor changes based on its interaction with the other factors. For instance, when

no appointment slots are held open, the improvements provided by a resource allocation was less

than 1%, indicating that with the current system, changes in the resources allocation will not

improve the average throughput time for new patients. Nevertheless, when appointment slots are

held open, with the actual admission rule, the improvement reached was up to 38.6%, while with

the proposed admission rule this percentage increases up to 83.4%. This result confirms the

theory behind the definition of these factors, considering that combined, they should improve the

alignment between demand and supply of resources. This factor does not impact the performance

for control patients.

Finally, the scenario that offers the best performance in terms of the average throughput

time for new patients, the number of ODT-patients treated and the average throughput time for

control patients is scenarios 8. It proposes a combination of: (1) the proposed admission rule in

which all new patients can decide to be treated on the same day of their diagnosis; (2) a patient’s

mix, combining regular patients and ODT-patients by holding the maximum amount of

appointment slots open as possible, estimated as five, based on the resources available; and (3)

the optimal allocation of resources presented in Table 27.

46

6.3.2 Comparative analysis of scenarios for PDT The percentage of appointment slots held open for ODT-patients is the most significant

factor in terms of the average throughput time for new patients treated with PDT. However, the

level of improvement is up to 85.4 % for the actual admission rule and 89.8% for the proposed

one. It can be observed that when more new patients are treated during one day (ODT-patients),

an increase in the amount of open appointment slots held open improves the average throughput

time for new patients. However, this is not the case for control patients. For the actual admission

rule, control patients are not significantly affected by changes in the amount of appointment slots

held open when there are up to 2. But for the proposed admission rule, the average throughput

time for control patient increases approximately 10 days. This interaction between the admission

rule and the percentage of appointment slots held open emphasizes the importance to align both

decisions to avoid affecting other patient groups.

The resources allocation has not exerted any significant influence in the average

throughput time for new or control patients. This result was expected, considering that the

optimization only includes the resources necessary for Excision and MOHS, which are

independent to the resources available for PDT. To conclude, the scenarios that offered the best

performance in terms of the average throughput time for new patients are scenario 7 and 8.

However, there is a trade-off that should be evaluated which is the negative effect in the

throughput time for control patients. If the clinic considers that the average throughput time for

control patients should not be exceeded with the changes proposed, then the best alternatives for

the clinic are scenarios 3 and 4. In this case, the alternative to be treated through the redesigned

ODT-process would be offered only to half of the new patients, and a policy should be defined

for their selection.

6.3.3 Sensitivity analysis This section intends to analyze how changes in some decisions made to build the

simulation model and assumptions considered, could influence the results of this study. For

instance, in the model development it was shown that changes in the distribution assumed to

define the demand could influence the results (See Appendix F). If the variability in the actual

demand is greater than the variability assumed in this study, the clinic could receive some days

more ODT-patients than expected which should not be a big issue if the policy to assign this

patient to the next day in the first open appointment slot available is considered. Another solution

to this situation is to work overtime during those days. However, when the patient arrivals for

treatment are less than expected, it is possible to observe an underutilization of the resources for

treatment.

A critical activity for the success of the implementation of the redesigned ODT process is

the medical tests performed by the Pathology department to corroborate the doctor’s diagnosis.

The duration of this activity is assumed to be less than 2 hours. A change in the duration of this

activity directly influence the throughput time of patients, reducing the probability of treatment o

the same day.

47

6.4 Concluding from experiments

This chapter presents the results gathered after running 50 replications for each scenario

simulated. The results were presented, including the average throughput time for new and control

patients and the amount of new patients treated by the redesigned ODT process. The results

showed that the most significant factor for both Excision and PDT patients is the percentage of

appointment slots held open. The advantages of holding appointment slots for ODT-patients

improve the average throughput time for new patients up to 90% for Excision and 89.8% for

PDT. This alternative also improves the performance for control patients, reducing their average

throughput time by approximately one week.

The second most influential factor is the admission rule. The proposed setting in which all

the new patients have their consultation meeting in the morning, is directly related to an increase

of ODT-patients. For patients referred for Excision, the proposed alternative duplicates the

number of ODT-patients maintaining the same level of improvements provided by the other

factors in the average throughput time for new patients. However, for PDT patients, when the

number of appointment slots held open increases to 58%, the performance for new patients is

significantly affected increasing the average throughput time for control patients almost 10 days.

Finally, the alternative setting proposed for the allocation of resources for each

combination of factors, influence the performance based on its interaction with the other factors.

When no appointment slots are held open, the improvements provided by a resource allocation

was less than 1%, indicating that with the current system, changes in the resources allocation will

not improve the average throughput time for new patients. Nevertheless, when appointment slots

are held open, with the actual admission rule, the improvement reached was up to 38.6%, while

with the proposed admission rule this percentage increases up to 83.4%. This result confirms the

theory behind the definition of these factors, considering that combined, they should improve the

alignment between demand and supply of resources. This factor does not impact the performance

for control patients. Next section will summarize the main findings and contribution of this

research.

48

7 CONCLUSIONS AND RECOMMENDATIONS The aim of this research has been to analyze the dermatology oncology outpatient clinic at

the Catharina hospital from a logistic perspective to determine the feasibility of implementing

the redesigned ODT process. This redesign offers the option to new patients diagnosed with

BCC and referred for Excision or PDT to complete their diagnosis and treatment during the same

day. This final chapter includes answers for the research questions stated for this study and also

some recommendations for future research.

7.1 Conclusions

This study proposed a simulation model to evaluate the effect of changes in alternative

scenarios to answer the research question as described below:




In order to implement the redesigned ODT process, the clinic should align their planning

for treatment with the admission of patients for consult meeting. This alignment is necessary,

considering that new patients who are arriving every day for consultation could decide whether

they receive their treatment on the same day of the diagnosis or not, and the clinic should have

the resources available to fulfill that demand.

Another condition that should be fulfilled is the availability of the results from the

pathology department within the same day to complete the process. This could be considered as

the most critical factor to implement the redesigned ODT process with the drawback that it is an

external resource and out of the scope of this study. This suggests the need to discuss the

redesign with the chief of this external party, stressing the advantages of this implementation for

the patients.



To fulfill the condition of an alignment between the demand and supply, the results

showed that one significant factor is the percentage of open appointment slots held open to treat

ODT-patients. This percentage is estimated identifying the expected demand of services from

ODT-patients and the average percentage of slots held open for treatment, both for Excision and

PDT. This percentage changes with changes in the demand, considering that the resources

available remain the same. For the current demand, 27.5% of the slots should be held open,

which can be translated to 2 appointment slots for PDT and 3 for Excision every day.

To extend the option to choose for treatment on the same day of diagnosis, another factor

that should be considered is the admission rule. The actual admission rule is that every doctor

receives a maximum of five new patients for consultation during one block (half a day: morning

or afternoon). However, this rule only aims to balance the workload between doctors but is not

patient-centered. The proposed rule includes all the appointment slots for new patients during the

49

morning. This aims to extend the option to choose the redesigned ODT process to all new

patients, under the actual assumption that the sample biopsy should be available before

11:30 AM for the evaluation at the pathology department and further answer in less than two

hours.



The advantages of implementing the redesigned ODT process can be measured in terms

of the time performance. The average throughput time for new patients could be reduced to 90%

for Excision and 95% for PDT, compared to the current situation. This redesign also adds

flexibility to the process, which allows patients to decide whether they want to be treated on the

same day or follow the regular process. To implement the redesigned ODT process is mandatory

to receive the results from pathology in less than two hours, which nowadays takes

approximately two weeks. This improvement could increase the cost of this service for the clinic.




The effect of implementing the redesigned ODT process for new patients also reduces the

average throughput time for control patients treated with Excision by approximately two weeks.

The effect for control patients treated with PDT is not affected by the actual admission rule in

which only 27.5 % of the appointment slots are held open for ODT-patients, but it is significantly

affected when this percentage is increased to 50%. The analysis is performed independently for

patients treated by Excision and PDT because their resources are independent. The only

resources shared are those between Excision and MOHs. The average throughput time for

patients treated with MOHs does not show a significant difference between the current situation

and the proposed scenario, measured in approximately 1.5 days. This difference is not significant

considering that the current throughput time is greater than two months.

7.2 Limitations and further research

The results provided in this study are limited to the dermatology oncology outpatient clinic

at the Catharina hospital. The conclusions included are based on the analysis performed using a

simulation model which is a simplification of the real situation. One limitation of this study is the

exclusion of the variability in the demand pattern throughout different months of the year.

Historical data is available about the amount of patients scheduled for treatment per month, but it

does not reflect the current demand which also, includes the amount of patients looking for

treatment on a daily basis. A more accurate insight into the behavior of the demand and seasonal

changes could help the clinic to improve their planning of resources.

Another limitation is that all the results are based on the availability of results from

pathology in less than two hours. This factor is critical for the implementation of the redesigned

ODT process. However, the flexibility expected from the pathology department could increase

the actual cost for this service. In this case, it is important to consider the alternative to evaluate

50

the accuracy of the diagnosis performed by the specialists compared to their results. Also,

perhaps agree in the future to only use a sample of the total amount of patients to check the

reliability of the results but to gain some flexibility which is favorable for the success of the

redesigned ODT process implementation.

51

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53

LIST OF ABBREVIATIONS

• BCC : Basal cell carcinoma

• MOHs : Micrographic surgery

• ODT : One-day-treatment redesigned process including diagnosis and treatment

• ODT-patient: a patient treated following the redesigned ODT process.

• PDT : Photodynamic therapy

• SCC : Squamous cell carcinoma

APPENDICES

Appendix A - Organizational Chart

54

Chart of Catharina Hospital

55

Appendix B- Master Scheduling for doctors and Specialists

A mater schedule is used to assign doctors and specialists to the different activities during

the week. The activities included the following: consultation and treatment (MOHs and

Excision). There are eight doctors available nowadays in the clinic and 7 specialists. The

schedule divide the week in 14 different blocks assuming two blocks per day (morning and

afternoon). The outpatient clinic operates from Monday until Friday. The weekly assignment of

doctors for consultation is shown in Table 13. If the cell has a value of 1, then the doctor

specified in the column is available for consultation during this day. The maximum amount of

appointment slots available to consult new patients is equal to 5 per doctor per block. And the

maximum available for control patients is equal to 12 per doctor per block.

Table 13- Weekly schedule of doctors for consultation

The weekly assignment of specialists for consultation is shown in Table 14. Similar to the

previous table, if the cell has a value of 1, then the specialist specified in the column is available

to supervise the doctors in consultation during this day. The amount of specialist per day

assigned for supervision varies between one and two based on the demand.

Table 14- Weekly schedule of specialists for consultation

SPR SPR SPR SPR SPR SPR SPR SPR

Consult_Doctors Doctor1 Doctor2 Doctor3 Doctor4 Doctor5 Doctor6 Doctor7 Doctor8

Sunday morning 1 0 0 0 0 0 0 0 0

Sunday afternoon 2 0 0 0 0 0 0 0 0

Monday morning 3 1 1 0 1 1 1 0 0

Monday afternoon 4 1 1 1 1 1 0 0 0

Tuesday morning 5 1 0 0 1 0 1 1 1

Tuesday afternoon 6 0 0 0 0 0 0 1 1

Wednesday morning 7 1 0 1 1 1 1 0 0

Wednesday afternoon 8 1 0 1 1 1 1 0 0

Thursday morning 9 0 0 1 1 0 1 0 0

Thursday afternoon 10 1 0 1 1 0 1 0 0

Friday morning 11 1 1 1 1 1 1 1 1

Friday morning 12 1 0 1 1 1 0 1 1

Saturday morning 13 0 0 0 0 0 0 0 0

Saturday afternoon 14 0 0 0 0 0 0 0 0

ONCO ONCO DT ONCO DT DT DT

Consult_SupervisorsSpec1 Spec2 Spec3 Spec4 Spec5 Spec6 Spec7

Sunday morning 1 0 0 0 0 0 0 0

Sunday afternoon 2 0 0 0 0 0 0 0

Monday morning 3 1 0 0 0 0 0 0

Monday afternoon 4 1 0 0 0 0 0 0

Tuesday morning 5 1 0 0 0 0 0 0

Tuesday afternoon 6 1 0 0 0 0 0 0

Wednesday morning 7 0 0 1 0 0 1 0

Wednesday afternoon 8 0 0 1 0 0 1 0

Thursday morning 9 0 0 0 0 1 1 0

Thursday afternoon 10 0 0 0 0 1 1 0

Friday morning 11 0 0 1 0 0 0 1

Friday morning 12 0 0 0 1 0 0 1

Saturday morning 13 0 0 0 0 0 0 0

Saturday afternoon 14 0 0 0 0 0 0 0

56

The assignment of treatment type to be perfumed in one of the available operating rooms

is shown in Table 15. The nomenclature used is: 1 for the MOHs and 2 for Excision.

Table 15 - Weekly schedule for MOHs and Excision

The weekly assignment of doctors for treatment is shown in Table 16. The value in the

cell indicates the number that identifies the doctor assigned for treatment during this day. The

assignment of specialists for treatment is included in Table 17.

Table 16- Weekly schedule of doctors for treatment

Internal Internal External External External

OpRoom_Procedure OK OK KCI KCI KCI

(Set_ID) 1 2 3 4 5 Scale

Sunday morning 1 0 0 0 0 0 1 1 = Mohs

Sunday afternoon 2 0 0 0 0 0 2 2 = Excision

Monday morning 3 1 1 0 0 0

Monday afternoon 4 1 1 0 0 0

Tuesday morning 5 1 1 2 2 2

Tuesday afternoon 6 1 1 2 2 2

Wednesday morning 7 0 0 0 0 0

Wednesday afternoon 8 0 0 0 0 0

Thursday morning 9 0 1 0 0 0

Thursday afternoon 10 0 1 0 0 0

Friday morning 11 0 0 0 0 0

Friday afternoon 12 0 0 2 2 0

Saturday morning 13 0 0 0 0 0

Saturday afternoon 14 0 0 0 0 0


OpRoom_Doctor OK OK KCI KCI KCI

(Set_ID) 1 2 3 4 5

Sunday morning 1 0 0 0 0 0

Sunday afternoon 2 0 0 0 0 0













57

Table 17- Weekly schedule of specialists for treatment


OpRoom_Specialist OK OK KCI KCI KCI

(Set_ID) 1 2 3 4 5

Sunday morning 1 0 0 0 0 0

Sunday afternoon 2 0 0 0 0 0













Appendix C- Elaboration of the simulation model in Arena

The simulation model described in this project was built using a simulation tool Arena 7

from Rockwell Software. In order to use the model is necessary to install a professional license

of this software and initialize some parameters that will allow the interface between Arena, Excel

and Visual Basic for Applications (VBA).

• First, redefine the array setting used on your computer to avoid errors while running the

model. Go to the main menu Run > Setup > Array Sizes, and set ICXM =3000,

RVEC =90000, IVEC=90000 and RSET = 90000. The new array sizes settings will be

saved with the model file.

• And select another application’s objects that should be available to run the Visual Basic

Editor. Therefore, it is necessary to select in the main menu: Tools > Macro > Show

Visual Basic Editor. Once in the main window, go to Tools > References and select form

the References list the following libraries:

o Visual Basic for Applications

o Arena 12.0 Type Library

o OLE Automation

o Microsoft Excel 11.0 Object Library

The last step is necessary to allow the interaction between the VBA’s code with the

variables used in Arena and the application in Excel. Some of the variables are used as inputs to

plan the appointment for patients, modified and returned to be used in Arena. Some other

variables used to schedule the appointments are taken from a file in Excel and even printed back.

This application is important for the process verification

- Arena flow logic:

The model presented in Figure 11 captures the actual process followed by patients in the

dermatology oncology outpatient clinic. First, new patients contact the clinic referred by their

family doctor, another specialist or self-referring requesting an appointment. The appointment

request will be described in detail in the next subsection called visual basic application. At the

end of this sub process, the patient will receive an appointment date, time and a specific doctor

assigned. The model uses the attributes Appointment_date, appointment_time and

Doctor_assigned, to identify the specific values assigned for each patient. The list of variables

and attributes used in the model with a brief description is included in Table 18 and

Table 19.

Moreover, the patient needs to wait until the day of the appointment to go to the clinic

and register with the secretary at the front desk. The doctor calls patients based on their position

in the appointment list, represented in the model by an attribute called position. The consultation

meeting is divided in three different parts: (1) consult meeting init, in this part the doctor receives

the patient and makes an evaluation, then the doctor already has a first idea of the diagnosis

which will be confirmed by the specialists supervising this day; (2) consult meeting sup, the

doctor informs one specialist about the diagnosis and this specialist check the patient and provide

feedback to the doctor about the specific diagnosis.; and consult meeting end, finally the doctor

59

inform the diagnosis to the patient and explain the further steps. The consultation meeting was

divided in three parts considering the fact that the specialists are shared resource required by all

the doctors treating new patients and sometimes even for consults about the diagnosis of a

control patient, those occurrences are estimated to be true for 25% of the control patients in the

model. A specialist is required only during the consult meeting sup activity while the doctor

spend the complete consultation time with the patient.

Figure 11-Arena flow chart for patients diagnosed with BCC at the oncology clinic

Another reason to split this activity is to identify the average time that doctors spend waiting

for a specialist, considering that it is not a value added time for the activity but influence the total

time of the consultation and the waiting time for the others patients in queue. When the

consultation meeting finish, an estimated 40% of the patients are diagnosed cancer free. In the

model those patients are counted using a record block called Counter cancer free and the other

60% is referred for further analysis. The secretary informs the patient about the details of the

further tests:

1. Take photo. First, the patient needs to go to take a photo of the area indicated by the doctor during the consultation. The photo is taken in the Catharina Hospital in another

area which is not very busy but far from the dermatology area, requiring on average 20

minutes for a patient to go, take the photo and return to the dermatology area. This

activity is simulated as a delay, considering that the resources involved are external of the

60

dermatology area. The secretary registers when the patient has returned from the photo

and put the file on queue for the next step which is the biopsy.

2. Biopsy. The biopsy consists on taking a sample from the tumor for a further pathological

analysis. A nurse is responsible to take the sample and transport it to the lab. The duration

of this activity is on average 8 minutes. In some especial cases the nurse asks for support

from the doctor, but their incidence was considered negligible to be represented in this

model.

Samples are transported by the nurses during the day without any specific rule, sometimes in

batches of two, three or even one sample at a time. For the current situation is not significant to

define a specific order to send the samples to the lab considering that the lab will evaluate those

samples and provide the results two weeks after they receive them. The analysis performed by

pathology is simulated as a delay, because the resources involved are external. Nevertheless this

activity has a significant impact in our analysis because its output is used to confirm every

diagnosis at the clinic and nowadays has a delay of two weeks on average for the clinic to

receive those results.

The results from pathology are received at the secretary counter. The secretary in charge of

planning the appointments for the doctors, take the results and ask for the patients’ file digitally

in a database used in the hospital for the general archive called “Jim”. Those files are transported

by external personnel from the general archive to all the different areas in the hospital during the

day. When the secretary receives all the files, they organize them in groups per appointment day

per doctor. The aim of this activity is to guarantee that the doctor will have all the information of

each patient at hand, to inform the final diagnosis and treatment to the patient. This information

is provided by calling the patient and is planned as a TEL appointment with an estimated time of

five minutes per patient.

The result from pathology aims to verify the doctor’s diagnosis, indicating if the patient has a

Basal cell carcinoma (BCC) or another type of tumor. In the model all the patients diagnosed

with a different type of tumor than the BCC or cancer free were considered out of the scope of

this analysis. However, for the BCC patients there are four different options of treatment

depending on the location and characteristics of the tumor, as described at the introduction

section. An attribute called DiagnoseType is used to classify the patients in 1 = non BCC

patients, 2 = BCC with self-treatment, 3 = BCC with PDT, 4= BCC with MOHs and 5=BCC

with Excision. The patient is informed by the doctor about the following treatment, but the

specific appointment to receive the treatment is not planned yet. The doctor brings the files to the

secretaries at the end of the day and they need to call the patient back and schedule an

appointment for the next time available. Figure 12 shows the submodel that simulates the

different paths. The secretary is responsible for the planning of new patients referred for the PDT

or Excision. Patients requiring the MOHs procedure are informed through letter and their

appointments are planned by an assistant.

61

Figure 12-Submodel for making an appointment for BCC patient’s treatment

This study is mainly focus on the Excision, which is only one of the four different types of

treatments offered by the clinic. But it is also necessary to include the others in the model to

analyze the collateral effect of changes in the model to the performance of BCC patients

receiving other treatments. For that reason, a submodel is defined for each treatment type. For

patients referred for self-treatment, they are informed about the treatment by the doctor and are

scheduled for follow-up appointments to evaluate their status. This treatment consists on the

application of a special ointment prescribed by the doctor, at home for a specific period of time.

Some patients experiencing unexpected reactions to the cream can call back the doctor or ask for

another appointment before they continue their treatment. This possibility is not represented in

the model because its occurrence is not significant.

The submodel for patients diagnosed with BCC and prescribed to receive Photo Dynamic

Therapy (PDT) is shown in Figure 13. Every day there is one nurse assigned for the PDT. At the

beginning of the appointment slot, the nurse has the files from all the patients with one

appointment. This treatment is divided in three parts: (1) Ointment application, (2) Waiting time

to let the ointment work, and (3) Exposition to the infrared light. First, the nurse registers the

arrival of the patient, following by a check of the patient’s file. The nurse calls the patient from

the waiting room. The nurse examines the tumor to confirm it has not change since the doctor’s

diagnosis. Following, she applies the special ointment on the tumor and sends the patient back to

the waiting room. The time spend on the application of this ointment is on average 10 minutes.

Patients wait for two or three hours, depending on the treatment required, before the nurse call

them back to apply the infrared light during 10 minutes after 3 hours or 30 minutes after 2 hours.

Finally, the nurse schedules the next appointment for a follow-up visit with the doctor after three

months.

62

Figure 13-Submodel for the Photo Dynamic Therapy (PDT)

The second type of treatment model is the MOHs. The submodel for the patients treated with

MOHs is shown in Figure 14 . Patients arrive and stay at the waiting room until the assistant ask

them to go to the operating room. At the operating room, the doctor with one assistant prepares

the patient for the surgery, first marking the area to be excised and then applying the anesthesia.

The shared assistant calls the specialist assigned which will remains in the room to supervise

when the doctor excise the tumor. When the tumor is completely removed, then the supervisor

leaves the operating room. The doctor controls the blood drain from the wound and then the

assistant put a bandage in the open wound, until the doctor prepares the tumor samples.

The patient returns to a waiting room until those samples are evaluated and confirmed if

the patient is cancer free or if still requires excising more tissue. The samples are sent to the lab

to be prepared in slices for their analysis. The activity to prepare the sliced samples in the lab is

modeled as a delay, because even though is important for the process but is performed by an

external resource. When the doctors finish excising the tumor for all the patients scheduled for an

appointment block, then they evaluate all the sliced samples received from the laboratory. For

this evaluation both, the doctor and the specialist, together decide if the patient is cancer free or

still requires another excision.

For the second appointment block, the priority is based on the remaining time needed to

finish the treatment. They start treating patients that requires another excision and finally the

patients already cancer free. The percentage of patients needing a second or third excision is

approximately 50%. When all patients are cancer free, then they are called to go back to the

operating room to close their wound and schedule their next appointment in two weeks to

remove the stitches and in six months for a control post-treatment visit.

63

Figure 14-Submodel for the MOHs Procedure

The submodel for the Excision is also included in the model and shown in Figure 15. The

patient is prepared by the assistant at the operating room and the doctor starts evaluating and

marking the tumor. This activity is supervised by one specialist. The specialist leave the room

and the doctor continue excising the tumor and then closing the wound with stitches. In parallel,

the doctor prepare the samples from the tumor to send them to the laboratory, while the assistant

schedule the next appointment for the patient to remove the stitches in two weeks and for the

next control visit in six months.

Figure 15-Submodel for the Excision

The model also contains some blocks only to collect statistics from the process, like the number

of patients for each DiagnoseType, and the average time that a patient spend in the process since

their first consultation appointment until they receive their treatment. A list of the activities

described in the model with their resources and time duration is included in Appendix D.

- Visual basic application:

The process of making an appointment was described using a visual basic code. This

code was built for three principal purposes: first, to model the decisions related to the assignment

of an appointment date and time for each patient; secondly, to allocate the patient with a specific

doctor; and finally, to facilitate the changes in the parameters used as input variables by the

model. This process is represented using a submodel. The secretary asks the patient’s name and

date of birth to schedule the appointment for a consultation meeting in the first day available.

The patient’s name and date of birth are used to identify each patient individually, in the model it

64

is substituted by the attribute Patient_ID. The Patient_ID is a unique integer number defined for

each patient to identify them in the process. The actual activity is performed using a Visual Basic

for Applications (VBA) block. Every time an entity arrives to the VBA block, the program

observes the type of patient asking for the appointment (attribute PatientType: 1=New and

2=Control patients) and evaluate when is the next appointment slot available.

The VBA code included more than 40 pages that will not be included in this document

considering that they will not provide any important insight for users without background

knowledge in this programming language. It is important to present the decisions made in the

process of assigning an appointment. Therefore, this process will be described using flow charts,

which are self-explanatory and do not require a special training to be interpreted. Flow charts are

descriptive languages that have proved to allow users to gain knowledge over what has been

modeled. To simplify the comprehension of the processes modeled using flow charts, Table 18

and Table 19 present a brief description of the main global variables and attributes used in the

model.

Table 18-Description of variables

Name Description Type Array

Dimension

Rows Col.

appointment_time_morning Indicates the specific time of the appointment slot assigned

to a patient. The first column point out the hour and the

second column the minute of the appointment. There are a

maximum of 17 appointment slots during one block. They

differ on the block for which the appointment is assigned:

morning or afternoon.

Array-

Integer

17 2

appointment_time_afternoon Array-

Integer

17 2

App_rule_afternoon The appointment rule defines the policy used to schedule

the appointment per patient type. The actual system use the

same policy for both appointment blocks: morning and

afternoon, but both variables are differentiating to allow

changes in the proposed scenario. The current policy is to

receive in one block a maximum of 5 new patients, and start

the block with two appointment slots for control patients

followed by one new patient.

Array-

Integer

17 2

App_rule_morning Array-

Integer

17 2

Consult_Doctors Presents a schedule of the doctor available for consultation

per block per day. The maximum number of doctors

available is 8, and during one week there are 14 blocks,

each one corresponding to the morning and afternoon for 7

days a week.

Array -

integer

14 8

Consult_Supervisors Presents a schedule of the specialist available for

supervision during consultation, per block per day. The

maximum number of specialists available is 7.

Array -

integer

14 7

Max_control_pat_slots Indicates the maximum number of appointment slots

available for control patients during the week. This amount

is assigned per doctor per block.

Array -

integer

14 8

Max_new_pat_slots Indicates the maximum number of appointment slots

available for control patients during the week. This amount

is assigned per doctor per block.

Array -

integer

14 8

Max_doctor Shows the maximum amount of doctors assigned for

consultation per block per day, in the current situation.

Array -

integer

14 1

65

Max_sup Shows the maximum amount of specialists assigned to

supervise the doctors during consultation, per block per

day.

Array -

integer

14 1

Max_treat3_pat_slots Indicates the maximum number of appointment slots

available for PDT treatment per block every day.

Array -

integer

14 1


available for MOHs treatment per block every day. The

value is assigned to a specific Operating Room from the 5

available.

Array -

integer

14 5


available for Excision per block every day. The value is

assigned to a specific Operating Room from the 5 available.

Array -

integer

14 5

OpRoom_Doctor Identifies the doctor assigned for treatment every day in

their respective operating room. The doctors are coded with

values form 1until 8.

Array -

integer

14 5

OpRoom_Procedure Shows a weekly schedule of the treatments to be performed

at a specific operating room.

Array -

integer

14 5

OpRoom_Specialist Identifies the specialists assigned for treatment every day in

their respective operating room. Specialists are coded with

values form 1until 7.

Array -

integer

14 5

patient_counter Variable used to increment in one unit every time a new

patient arrives to the clinic and assign this value as a

personal identification for the patient.

Integer

Patarrival Indicates the parameter of the Exponential distribution that

indicates the arrival of patient’s calls every day to the clinic.

4

today Indicates the day during the run, starting at 1. It is assumed

7 days a week and 40 weeks a year, full time.

0

weekday Assumes a value from 1-7, in which 1=Sunday and

7=Saturday. The initial value is read from the day of the

week in the first day of the simulation

CalDayOfWeek(TNOW)

= 2 (Monday)

Table 19-Description of Attributes

Name Description Type Initial value

Appointment_date Indicates the appointment day for consultation

assigned for each patient. The initial value is a

variable called today which is the day that the patient

call the clinic, represented in the model by an arrival.

Integer Variable “today”

appointment_time_hour Indicates the time of the appointment for

consultation for each patient. The specific hour and

minute for the appointment. The notation used is

integer values: (1) hours within a range between 9

and 17; and (2) minutes from 1 to 60.

Integer 0

appointment_time_min 0

Appointment_treat Indicates the appointment day for treatment assigned

for each patient. The initial value is a variable called

today which is the day that the doctor inform the

patient about their diagnosis and refer the files from

the patient to the secretary to schedule an

appointment.

Integer Variable “today”

ArrivalTime Indicates the specific moment in time of the arrival.

It represents the moment during the day that the

Integer TNOW

66

patient call to make an appointment for consultation.

call_day Indicates the specific day that the patient calls. Integer today

DiagnoseType Define the type of diagnosis defined for each patient.

The diagnosis could assume five different values: (1)

non BCC, after all the test the patient is diagnosed

with melanoma or SCC (10%); (2) BCC refereed for

self-treatment (20%); (3) BCC referred to PDT

(20%); (4) BCC referred for Mohs surgery (20%);

and finally (5) BCC referred for Excision (30%).

Integer Discrete

Distribution with

cumulative prob.

0.1 - 1

0.3 - 2

0.5 - 3

0.7- 4

1.0 - 5

Doctor_assigned Indicates the number of the specific doctor assigned

for consultation or treatment during the day of the

appointment. The range of values change from 1-8.

Integer 0

Patient_ID Represents the unique identity number assigned to

each patient, which in the model substitute the

patient’s name and date of birth together.

Integer patient_counter

PatientType Indicates if the patient is new=1 or control=2. This

classification is used to define the patient routing in

the process and to collect statistics per patient type.

Integer 1

position Indicates the position in the list of appointment slots

available during one day.

Integer 0

Room_assigned Indicates the number of the specific operating room

for treatment during the day of the appointment. The

range of values change from 1-5.

Integer 0

Spec_assigned Indicates the number of the specific specialist

assigned for consultation or treatment during the day

of the appointment. The range of values change from

1-7.

Integer 0

waitingforappoint Indicates the time that a patient needs to wait since

an appointment is scheduled until the specific day

and time of the appointment.

Integer 0

Week_day_appoint Indicates the day of the week with an appointment,

for consult or treatment. Assumes a value from 1-7,

in which 1=Sunday and 7=Saturday.

Integer weekday

The process to assign an appointment slots for the consultation of new and control

patients are shown in Figure 16, Figure 17, Figure 18, Figure 19 and Figure 20. The current

appointment system used in the oncology outpatient clinic is divided in 2 blocks per day

(morning and afternoon), including for the consultation in every block a combination of one

appointment slot for a new patient followed by two slots for control patients and one blocked

slot. The appointment slots planned for new patients are a maximum of five per block per doctor,

and have an expected duration of 15 minutes. While the appointment slots for control patients

have a planned duration of 5 minutes. It has been observed that is common to spend more than 5

minutes with a regular control patient, increasing the patients’ waiting time in queue. The

planner decided to manage this concern, including a blocked slot of 5 minutes after two

consecutive control patients.

The process starts comparing the day that the patient call the clinic looking for an

appointment named “Appoint_date”, with the next day in which an appointment slot is available

for consultation called previousday_new. If the patient call before the next day available for an

67

appointment, then the initial day to search for the appointment is the previousday_new. Besides,

the initial day to search for an appointment is one day after the call, assuming that appointments

are not assigned for the same day of the call. It can be observed in Figure 16 that some operations

are performed only to clean the agenda form one week to another, with this operation the agenda

only store appointments for one week and the information about the appointment is carried as

attributes by every individual patient.

The second part of the process is shown in Figure 17. The secretary start looking in the

suggested day resulted from the previous step, and furthermore look if during this day there is an

appointment slot available for consultation with any of the doctors available this day. In the

model this operation is represented comparing the value stored in the variable Slots_busy_new

(nextrow1, nextcol1) with the Max_new_pat_slots(nextrow1, nextcol1). The variable

Slots_busy_new (nextrow1, nextcol1) is a matrix of dimension 14 x 8 that stores the number of

appointment slots already taken by a patient, in this case new patient, per block per doctor. The

blocks are 14 starting in 1=Sunday morning and ending in 14 = Saturday afternoon. The columns

represent the maximum amount of doctors available for consultation. This matrix is use to

compare the number of appointments already booked with the maximum appointments available

this day per patient type. These variables were created assuming that the amount of doctors

available for consultation every day varies significantly, similar to the amount of appointments

available depending on the patient type as explained before. For new patients there is a

maximum of 5 appointment slots per doctor available, and 12 for control patients. The variable

nextrow1 indicates the block corresponding to the suggested next day available for appointment,

while nextcol1 represents the first doctor available for an appointment.

When the next appointment slot available is found, the Appoint_date is assigned for the

patient, together with the specific position and specific time for the appointment considering if

the appointment is defined for the morning of afternoon. It is also necessary to save the values of

the last day of the appointment called previousday_new and the specific block (nextrow1) and

doctors (nextcol1), to make the search more efficient for the next patients arriving to the clinic

for an appointment. The last part of the process, which is represented in Figure 17, fist assigns a

specialist to supervise the patient consult, considering those who are scheduled to supervise

during the specific day of the appointment. At the end of the process the resulted values related

to the appointment date, time, doctor and supervisor assigned are printed as attributes that will be

used in the regular patient’s flow logic in Arena.

The same approach was considered for the appointment of the different treatments: PDT,

MOHs and Excision. The flow chart for the PDT is included in Figure 21 and Figure 22. In this

process there are no difference between new and control patients for the treatment, in terms of

the planning. The decisions made in this process are the same explained for the assignment of

appointment slots for consultation, differing in the fact that for PDT is not necessary to assign a

doctor, specialist and operating room because it is performed by a nurse in a specific room

independently from the operating rooms. Finally, patients need to wait until the day of the

appointment to go to the clinic and register at the front desk.

68

- Model to make appointments for consultation and treatment for new and control

patients:

There are four different processes described in this section related to the scheduling of

appointments for new and control patients. The first process describes the assignment of

appointment slots for the consultation of new and control patients, which are differentiated per

patient type. This process includes three parts described in: (1) Figure 16 and Figure 17, for new

patients; (2) Figure 19 and Figure 20, for control patients; and (3) Figure 18 for both. The

descriptions of the different decisions made in those processes are similar and only differ in the

variables used to guarantee that the outputs of both processes are independent of each other. For

instance, the arrival of a new patient will not affect the number of appointment slots available for

control patients during one day and neither the other way around. The variables identified with

an extension 1 correspond to new patients, and with extension 2 to control patients (i.e. nextrow1

for new patients and nextrow2 used for the appointment of control patients). Another notation is

to use new or control in the variable name, for example previousday_new and

previous_day_control.

The second, third and fourth processes are related to the assignment of an appointment

slot for patient’s treatment: PDT, MOHs and Excision, respectively. Those processes are

described together for new and control patients. The appointment of PDT is shown in Figure 21

and Figure 22. The process is the same used for the MOHs and Excision, with small changes in

the notation: the extension 3 is used for PDT, 4 for the MOHs and 5 for Excision (i.e.

previousdaytreat3, previousdaytreat4 and previousdaytreat5, used to define the last day available

for treatment using PDT, MOHs and Excision). The main idea is that the assignment of an

appointment slot available for a PDT treatment is independent of the next day available for an

appointment slots for MOHs.

69

Figure 16- Process to make an appointment for the consultation of a new patient part 1

Consult_Part1_New

pat_Type=1

q=nextrow1

p=nextcol1

No

q>=nextrow1nextrow1=q

nextcol1=p

Appoint_date

<previousday_new

Yes

Consult_Part 2_new

Appoint_date=previousday_new

previousday_new=Appoint_date+1

nextrow1=((week_day_appoint+1)*2)-1

nextcol1=1

Appoint_date -

previousday_new <7

Yes

No

Yes

Yes

No No

i=0

j=1

Max_new_pat_

slots((q+i),j)>0

Slots_busy_new(

(q+i),j)=0

Yes

No j<5

i<nextrow1-q-1

No

j=j+1

Yes i=i+1

Yesposition_new=0

q=nextrow1

p=nextcol1

No

i=0

j=1

Max_new_pat_

slots(i,j)>0

Slots_busy_new(i,j)=0

Yes

No j<5

i<14

No

j=j+1

Yes i=i+1

Yes

position_new=0

q=nextrow1

p=nextcol1

No

“Control”

“New”

Consult_Part

1_Control

week_day_app

oint =7

previousday_new=Appoint_date+1

nextrow1=1

nextcol1=1

Yes

No

70

Figure 17- Process to make an appointment for the consultation of a new patient part 2

Consult_Part 2 _

New

i=1

j=1

Slots_busy_new

(nextrow1,nextcol1)

<

Max_new_pat.slots

(nextrow1,nextcol1)

Slots_busy_new(nextrow1,nextcol1)=0

nextcol1=nextcol1+1nextcol1>8

nextcol1=1

nextrow1=nextrow1+1nextrow1>14

nextrow1=1

Yes

YesYes

j<8

i<14 i=i+1

j=j+1

No

nextcol1=pnextrow1=q

nextrow1>q

i=14

j=8

No

Yes Yes

No position_new=0

q Mod 2=1 And

nextrow1 Mod

2=0

q Mod 2=1 And

nextrow1 Mod 2=0Yes

No

No

previousday_new =

previousday_new+round((nextrow1-q-1)/2)Yes

previousday_new =

previousday_new+round((nextrow1-q)/2)No

previousday_new =

previousday_new+round((14-q+nextrow1-1)/2)

previousday_new =

previousday_new+round((14-q-nextrow1)/2)

Yes

No

Appoint_date=previousdat_new

position_new=0

i=14

j=8

No

No

Yes

No

nextrow1 Mod

2 = 1

k = position_new

k = position_new

K<17k = k+1

App_rule_morning(k,2)

=1

position_new = App_rule_morning(k,1)

Slots_busy_new(nextrow1, nextcol1)

=Slots_busy_new(nextrow1, nextcol1) + 1

appointment_time(1, 1) =

appointment_time_morning(k, 1)



k = 17

Yes

Consult_Part3

K<17k = k+1

App_rule_afternoonk,2)

=1

position_new = App_rule_afternoon(k,1)




appointment_time_afternoon(k, 1)



k = 17

NoYes

Yes No

Noposition = position_new

Doctor_assigned = nextcol1

Yes


71

Figure 18- Process to make an appointment for the consultation of patients- part 3

Consult_Part 3

Week_day_appoint=(week_day_appoint +

Appoint_date-Call_day) Mod 7

Week_day_app

oint=0

Patient_type=1

pointer_sup(nextrow1,1)

> Max_sup(nextrow1,1)

Week_day_appoint=1

Yes

pointer_sup(nextrow1,1) =

pointer_sup(nextrow1,1)+1

Yes

pointer_sup(nextrow1,1)=1

i=pointer_sup(nextrow1,1)

i<Max_sup(nextrow1,

1)

i=i+1

Yes

No

Sup_availab(next

row1,i)>0

No

No

Yes

Spec_assigned=Sup_availab(nextrow1,pointer_s

up(nextrow1,1))

i=Max_sup(nextrow1,1)

Yes

End

No

pointer_sup(nextrow2,1)

> Max_sup(nextrow2,1)

pointer_sup(nextrow2,1) =

pointer_sup(nextrow2,1)+1

pointer_sup(nextrow2,1)=1

i=pointer_sup(nextrow2,1)

1<Max_sup(nextr

ow2,1)

i=i+1

Yes

No

Sup_availab(ne

xtrow2,i)>0No

Yes

Spec_assigned=Sup_availab(nextrow2

,pointer_sup(nextrow2,1))

i=Max_sup(nextrow2,1)

Yes No

No

72

Figure 19- Process to make an appointment for the consultation of a control patient part 1

Consult_Part1_Control

q=nextrow2

p=nextcol2

q>=nextrow2nextrow2 = q

nextcol2 = p

Appoint_date

<previousday_control

Yes

Consult_Part 2_control

Appoint_date=previousday_control

Appoint_date -

previousday_control <7

No

Yes

Yes

No No

i=0

j=1

Max_control_pat_

slots((q+i),j)>0

Slots_busy_control

((q+i),j)=0

Yes

Noj<5

i<nextrow2-q-1

No

j=j+1

Yesi=i+1

Yesposition_control=0

q=nextrow2

p=nextcol2

No

i=0

j=1

Max_control_pat_

slots(i,j)>0

Slots_busy_control(i,j)=0

Yes

No j<5

i<14

No

j=j+1

Yes

i=i+1

Yes

position_control=0

q=nextrow2

p=nextcol2

No

week_day_app

oint =7Yes

No

previousday_control=Appoint_date+1

nextrow2=1

nextcol2=1

previousday_control=Appoint_date+1

nextrow2=(week_day_appoint*2)-1

nextcol2=1

73

Figure 20- Process to make an appointment for the consultation of a control patient part 2

Consult_Part 2 _

control

i=1

j=1

Slots_busy_control

(nextrow2,nextcol2)

<

Max_control_pat.slots

(nextrow2,nextcol2)

Slots_busy_new(nextrow2,nextcol2)=0

nextcol2=nextcol2+1nextcol2>8

nextcol2=1

nextrow2=nextrow2+1nextrow2>14

nextrow2=1

Yes

YesYes

j<8

i<14 i=i+1

j=j+1

No

nextcol2=pnextrow2=q

nextrow2>q

i=14

j=8

No

Yes Yes

No position_control=0

q Mod 2=1 And

nextrow2 Mod

2=0

q Mod 2=1 And

nextrow2 Mod

2=0

Yes

No

No

previousday_control =

previousday_control+round((nextrow2-q-1)/2)Yes


previousday_control+round((nextrow2-q)/2)No


previousday_control+round((14-q+nextrow2-1)/2)


previousday_control+round((14-q-nextrow2)/2)

Yes

No

Appoint_date=previousdat_new

position_new=0

i=14

j=8

No

No

Yes

No

nextrow2 Mod

2 = 1

k = position_control

k = position_control

K<17k = k+1

App_rule_morning(k,2)

=1

position_new = App_rule_morning(k,1)







k = 17

Yes

Consult_Part3

K<17k = k+1

App_rule_afternoon(k,2)

=1

position_control = App_rule_afternoon(k,1)

Slots_busy_control(nextrow2, nextcol2)

=Slots_busy_control(nextrow2, nextcol2) + 1





k = 17

NoYes

YesNo

Noposition = position_control


Yes

Appoint_date=previousday_control

74

Figure 21- Process to make an appointment for the Photo Dynamic Therapy treatment part 1

PDT_Part1

q3>=

nextrowtreat3nextrowtreat3=q3

Appoint_date3

<previousdaytreat3

Yes

PDT_Part 2

Appoint_date=previousdaytreat3

previousdaytreat3=Appoint_date3+1

nextrowtreat3=((week_day_appoint+1)*2)-1

Appoint_date3 -

previousdaytreat3 <7

No

Yes

Yes

No No

i3=0

Max_treat3_pat_

slots((q3+i3),1)>0

Slots_busytreat3

((q3+i3),1)=0

Yes

No

i3<

nextrowtreat3-

q3-1

i=i+1

Yes positiontreat3=0

q3=nextrowtreat3No

i3=0

Yes

No

i<14

i=i+1

Yes

No

week_day_app

oint =7

previousdaytreat3=Appoint_date3+1

nextrowtreat3=1Yes

No

q3=nextrowtreat3

Max_treat3_pat_

slots(i3,1)>0

Slots_busytreat3

(i3,1)=0

positiontreat3=0

q3=nextrowtreat3

75

Figure 22- Process to make an appointment for the Photo Dynamic Therapy treatment part 2

PDT_Part 2

i3=1

Slots_busytreat3

(nextrowtreat3, 1)

<

Max_new_pat.slots

(nextrowtreat3, 1)

Slots_busytreat3 (nextrowtreat3 ,1)=0

nextrowtreat3 =nextrowtreat3 +1nextrow1>14 nextrow1=1

Yes

i3<14

i3=i3+1

No

Nextrowtreat3

=q3

nextrowtreat3

>q3

No

Yes

No

q3 Mod 2=1 And

nextrowtreat3 Mod 2=0

q3 Mod 2=1 And

nextrowtreat3 Mod

2=0Yes

No

previousdaytreat3 =

previousdaytreat3 +round((nextrowtreat3-q3-1)/2)Yes

previousdaytreat3 = previousdaytreat3

+round((nextrowtreat3 -q3)/2)No


+round((14-q3+nextrowtreat3 -1)/2)


+round((14-q3-nextrowtreat3 )/2)

Yes

No

Appoint_date3=previousdaytreat3

positiontreat3 =0i3=14

k3 = positiontreat3 +1

Slots_busytreat3

(nextrowtreat3,1<

Max_treat3_pat_slots(nex

trowtreat3,1)

k3 = k3+1

positiontreat3= Slots_busytreat3(nextrowtreat3,1)

Slots_busytreat3(nextrowtreat3,1)=Slots_busytreat

3(nextrowtreat3,1) + 1

k3 = Max_traet3_pat_slots(nextrowtreat3,1)

position = position_new


Yes


i3=14

Yes

No

Yes

K3<

Max_treat3_pat_slots

(nextrowtreat3,1)

Yes

No

No

week_day_app

oint =0

week_day_appoint =7

End

Appendix D - Overview of the different tasks with a definition of the resources required and their estimated service times

Type

Set Name

Quantity

Selection R

ule

Set In

dex

Take info

via call

Seize

Delay

Triangular

Minute

sValue A

dded

12

3Set

Assista

nt

1Specific M

ember

2

Finish call

Delay R

elease

Constant

Minute

sValue A

dded

Set

Assista

nt

1Specific M

ember

2

wait for appointm

ent

Delay

Exp

ression

Day

sW

ait

Waitin

g for ap

pointm

ent time

Delay

Constant

Minute

sW

ait

Reg

ister front des

k consult

Seize

Delay R

elease

Triangular

Minute

sValue A

dded

0.5

11.5

Set

Assista

nt

1Specific M

ember

1

Consult m

eeting init

Seize

Delay

Triangular

Minute

sValue A

dded

23.5

5Set

Docto

r1

Specific M

ember

Docto

r_assigned

Consult m

eeting sup

Seize

Delay R

elease

Triangular

Minute

sValue A

dded

1.5

22.5

Set

Specialists

1Specific M

ember

Spec

_ass

igned

Consult m

eeting supcon

Seize

Delay R

elease

Triangular

Minute

sValue A

dded

11.5

2Set

Specialists

1Specific M

ember

Spec

_ass

igned

Consult m

eeting end

Delay R

elease

Triangular

Minute

sValue A

dded

0.5

11.5

Set

Docto

r1

Specific M

ember

Docto

r_assigned

Wait next appoint consult

Delay

Constant

Day

sW

ait

Rece

ive in

fo photo

and biopt

Seize

Delay R

elease

Triangular

Minute

sValue A

dded

12

3Set

Assista

nt

1Specific M

ember

1

Take

photo

Delay

Triangular

Minute

sValue A

dded

18

19

20

Registe

r front desk bio

pt

Seize

Delay R

elease

Triangular

Minute

sValue A

dded

0.5

11.5

Set

Assista

nt

1Specific M

ember

1

Take sam

ple b

iopt

Seize

Delay R

elease

Triangular

Minute

sValue A

dded

3.5

812.5

Set

Assista

nt

1Specific M

ember

3

Send sam

ples to lab

Seize

Delay R

elease

Triangular

Minute

sValue A

dded

0.5

11.5

Set

Assista

nt

1Specific M

ember

3

Path

ology analysis regular pat

Delay

Triangular

Minute

sValue A

dded

10

14

16

Update patient record

Seize

Delay R

elease

Triangular

Minute

sValue A

dded

1.5

2.5

4Set

Assista

nt

1Specific M

ember

2

info

rm d

iagnosis and tre

atm

ent

Seize

Delay R

elease

Triangular

Minute

sValue A

dded

3.5

55.5

Set

Docto

r1

Specific M

ember

Docto

r_assigned

Waiting for treatm

ent

Delay

Constant

Day

sW

ait

wait to remove stitches

Delay

Constant

Day

sW

ait

Wait until next appoin

t 90

Delay

Constant

Day

sW

ait

Wait u

ntil next appoin

t 180

Delay

Constant

Day

sW

ait

Plan Exc

ision

Seize

Delay

Triangular

Minute

sValue A

dded

12

3Set

Assista

nt

1Specific M

ember

2

Info

rm patient Exc

ision

Delay R

elease

Triangular

Minute

sValue A

dded

0.5

11.5

Set

Assista

nt

1Specific M

ember

2

Plan M

ohs

Seize

Delay

Triangular

Minute

sValue A

dded

14.5

15

15.5

Set

Assista

nt

1Specific M

ember

2

Send info

Mohs to patients

Delay R

elease

Triangular

Minute

sValue A

dded

0.5

11.5

Set

Assista

nt

1Specific M

ember

2

Plan PDT

Seize

Delay

Triangular

Minute

sValue A

dded

12

3Set

Assista

nt

1Specific M

ember

2

Info

rm patient PDT

Delay R

elease

Triangular

Minute

sValue A

dded

0.5

11.5

Set

Assista

nt

1Specific M

ember

2

Maximum

Submodel

Pro

cess

Action

Delay T

ype

Units

Allocation

Minim

um

Value

Main m

odel

Make appoin

t treatm

ent

BCC

Resources

0 5

180

waitingfo

rappoint

12

90

180

waitingfo

rappoint

Make appointm

ent

77

Type

Set N

ame

Quantity

Selection R

ule

Set In

dex

Register arrival PDT patient

Seize D

elay R

elease

Triangular

Minutes

Value A

dded

0.5

11.5

Set

Assistant

1Specific M

ember

4

Check file and ointm

ent ap

plication

Seize D

elay R

elease

Triangular

Minutes

Value A

dded

10

12

15Set

Assistant

1Specific M

ember

4

Ointm

ent work tim

e2

Delay

Constant

Hours

Value A

dded

Ointm

ent work tim

e3

Delay

Constant

Hours

Value A

dded

Infrared light ex

position and plan

postcare2

Seize D

elay R

elease

Triangular

Minutes

Value A

dded

30

31

32Set

Assistant

1Specific M

ember

4

Infrared light ex

position and plan

postcare3

Seize D

elay R

elease

Triangular

Minutes

Value A

dded

10

11

12Set

Assistant

1Specific M

ember

4

Set

Operating R

oom

1Specific M

ember

Room_ass

igned

Set

Docto

r1

Specific M

ember

Docto

r_as

signed

Set

Assistant

1Specific M

ember

5

Register arrival M

OHs patient

Seize D

elay R

elease

Triangular

Minutes

Value A

dded

0.5

11.5

Set

Assistant

1Specific M

ember

6

Superv

ision by specialis

tSeize D

elay R

elease

Triangular

Minutes

Value A

dded

23

5Set

Spec

ialists

1Specific M

ember

Spec

_assigned

Set

Operating R

oom

1Specific M

ember

Room_ass

igned

Set

Docto

r1

Specific M

ember

Docto

r_as

signed

Set

Assistant

1Specific M

ember

5

Slice samples in lab

Delay

Triangular

Minutes

Value A

dded

10

15

20

Set

Docto

r1

Specific M

ember

Docto

r_as

signed

Set

Spec

ialists

1Specific M

ember

Spec

_assigned

Set

Operating R

oom

1Specific M

ember

Room_ass

igned

Set

Docto

r1

Specific M

ember

Docto

r_as

signed

Set

Assistant

1Specific M

ember

5

Register arrival Exc

ision patient

Seize D

elay R

elease

Triangular

Minutes

Value A

dded

0.5

11.5

Set

Assistant

1Specific M

ember

7

Set

Operating R

oom

1Specific M

ember

Room_ass

igned

Set

Spec

ialists

1Specific M

ember

Spec

_assigned

Set

Docto

r1

Specific M

ember

Docto

r_as

signed

Set

Assistant

1Specific M

ember

7

End superv

ision

Delay R

elease

Triangular

Minutes

Value A

dded

Set

Spec

ialists

1Specific M

ember

Spec

_assigned

Perform

surg

ery

and close w

ound

Delay

Triangular

Minutes

Value A

dded

10

16

20

Set

Operating R

oom

1Specific M

ember

Room_ass

igned

Set

Docto

r1

Specific M

ember

Docto

r_as

signed

Set

Assistant

1Specific M

ember

7

Exc

ision pro

cedure

0

Maximum

Submodel

Pro

cess

Action

Delay T

ype

Units

Alloca

tion

Minim

um

Value

PDT T

reatm

ent

2 3

Resources

Prepare samples fo

r lab and update

record

15

15.5

Delay R

elease

Triangular

MOHs pro

cedure

Prepare patien

t an

d perform

surg

ery

Prepare sam

ples and bandage

Close w

ound and prepare report

Analyze

samples

Seize

Delay

Triangular

Minutes

Value A

dded

14.5

Minutes

Value A

dded

35

7 7

Delay R

elease

Triangular

Minutes

Value A

dded

23

5

Seize D

elay R

elease

Triangular

Minutes

Value A

dded

3

5

Receive patient and define exc

ision

area

Seize

Delay

Triangular

Minutes

Value A

dded

0.5

11.5

Seize D

elay R

elease

Triangular

Minutes

Value A

dded

23 5

Appendix E- Modeling the redesigned ODT process

This Appendix shows the changes requires in the current model for the redesigned ODT

process. First, variable was defined called Treat_type which assumes two values 1=ODT-patients

and 2 = for regular patients. When the patient arrive from the biopsy, the model checks if there is

some slot available for the patient to be treated on the same day, using the block VBA_7. The

default value for all the patients at the beginning of the process is 2 (regular treatment), but if the

patient find an appointment slot available for treatment during the same day, then Treat_type

changes to 1. For patients treated by redesigned ODT process, the results from pathology are

obtained TRIA(30,50,85) in minutes instead of the actual TRIA(10,12,14) in days. Also the task

to inform the patient is independent from the regular process and has a priority compared to

patients following the regular process.

Figure 23-Changes in pathological test for the redesigned ODT process

Figure 24- Changes in submodel for making an appointment for BCC patient’s treatment for the

redesigned ODT process

79

Appendix F- Sensitivity analysis for the arrival of new patients for consultation

The sensitivity analysis consists on evaluating the changes in the output due to changes in

some of the input variables. This analysis aims to identify the effect of the decision about the

selection of a Poisson distribution to model the number of patients arriving every day to the

clinic, instead of just assuming a deterministic value.

Figure 25- Sensitivity analysis of the probability distribution for the arrival pattern

It can be derived from Figure 25 that the null hypothesis that there are no differences in

the output provided using the Poisson distribution to model the arrival pattern than using a

deterministic arrival is rejected. The first line in Figure 25 shows 34.83 days (836 hours) as the

average throughput time for new patients treated by Excision when the distribution used to

simulate the arrival of new patients is Poisson. This value is 30.04 days (721 hours) when the

arrival is considered deterministic. It suggests that the Poisson distribution will provide bias to

the system’s performance.

80

Appendix G- Defining the warm-up period

The warm-up period is defined to avoid the bias caused by the initial conditions defined in the

model. In this case, the model initialized empty, but an artifact was created to avoid the need to

run the experiment for 5 years in which the clinic has been operating. The artifact was to identify

the current waiting time by treatment type, and added this amount of time to define the first day

available for treatment in the system. For instance, the waiting time for PDT is on average 2

weeks therefore, at the beginning of the simulation, the first appointment for a patient arriving at

the system looking for an appointment slot for treatment with PDT is available on day 14 instead

of day 1 which would be the case if the simulation started empty. After running 50 replicates, the

statistics of interest were collected and the warm-up period was defined as 160 days, indicating

that the statistics collected to evaluate the model includes the observations of patients arriving at

the clinic for the next 120 days during this year. It is necessary to explain that in Figure 26 the

time units are expressed in minutes.

Figure 26- Average throughput time for BCC patients treated with Excision and PDT

81

Appendix H - Admission rule for actual and proposed

Table 20 – Current allocation of appointment slots for consult meetings during the morning

Table 21- Current allocation of appointment slots for consult meetings during the afternoon

Position Description Patient_type Block

13 0 1 Control 2

13 5 2 Control 2

13 15 3 New 1

13 30 4 Control 2

13 35 5 Control 2

13 45 6 New 1

14 0 7 Control 2

14 5 8 Control 2

14 15 9 New 1

14 30 10 Control 2

14 35 11 Control 2

14 45 12 New 1

15 0 13 Control 2

15 5 14 Control 2

15 15 15 New 1

15 30 16 Control 2

15 35 17 Control 2

Afternoon

appointment_time

(hour, minutes)


9 0 1 Control 2

9 5 2 Control 2

9 15 3 New 1

9 30 4 Control 2

9 35 5 Control 2

9 45 6 New 1

10 0 7 Control 2

10 5 8 Control 2

10 15 9 New 1

10 30 10 Control 2

10 35 11 Control 2

10 45 12 New 1

11 0 13 Control 2

11 5 14 Control 2

11 15 15 New 1

11 30 16 Control 2

11 35 17 Control 2

Morning

appointment_time

(hour, minutes)

82

Table 22 – Proposed allocation of appointment slots for consult meetings during the morning

appointment_time (hour, minutes)


9 0 1 New 1

Morning

9 15 2 New 1

9 30 3 Control 2

9 40 4 New 1

9 55 5 New 1

10 10 6 Control 2

10 20 7 New 1

10 35 8 New 1

10 50 9 Control 2

11 0 10 New 1

11 15 11 New 1

11 30 12 Control 2

11 40 13 New 1

11 55 14 New 1

12 10 15 Control 2

12 20 16 Control 2

12 30 17 Control 2

Table 23 – Proposed allocation of appointment slots for consult meeting during the afternoon

appointment_time (hour, minutes)


13 30 1 Control 2

Afternoon

13 40 2 Control 2

13 50 3 Control 2

14 0 4 Control 2

14 10 5 Control 2

14 20 6 Control 2

14 30 7 Control 2

14 40 8 Control 2

14 50 9 Control 2

15 0 10 Control 2

15 10 11 Control 2

15 20 12 Control 2

15 30 13 Control 2

15 40 14 Control 2

15 50 15 Control 2

16 0 16 Control 2

16 10 17 Control 2

83

Appendix I – Resources allocation proposed by OpQuest for scenarios 2, 4, 6 and 8

Table 24 – Optimal allocation of resources for MOHs and Excision for scenario 2




BlockDoctors - consult

meeting

Doctors -

treatment

Specialists -

superv. consult

Specialists - Excision

and MOHsNurses - Excision












meeting

Doctors -

treatment

Specialists -

superv. consult














meeting

Doctors -

treatment

Specialists -

superv. consult














meeting

Doctors -

treatment

Specialists -

superv. consult













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