ali mohammad medawi al owad bachelor of science in ... mohammad... · evaluate and improve work and...

INTEGRATED LEAN SIX SIGMA

APPROACH FOR PATIENT FLOW

IMPROVEMENT IN HOSPITAL

EMERGENCY DEPARTMENT

Ali Mohammad Medawi Al Owad

Bachelor of Science in Industrial Engineering, KKU, Abha,

KSA, 2006 & Master of Engineering Management, QUT,

Australia, 2009

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Chemistry, Physics and Mechanical Engineering

Faculty of Science and Engineering

Queensland University of Technology

December 2015

Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department i

Keywords

Emergency Department, Integrated Lean Strategy, Lean healthcare, Patient Flow

Problems, Voice of Process, Voice of Customer

ii Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department

Abstract

Because of increased competition between health care providers, higher

customer expectations, stringent checks on insurance payments and new government

regulations, it has become vital for health care organisations to enhance the quality of

care provided, increase efficiency and improve the cost effectiveness of their

services. Consequently, a number of quality management concepts and tools have

been employed in the health care domain to achieve the most efficient use of time,

manpower, space and other resources.

Emergency Departments are designed to provide high quality medical service,

with immediate availability of resources to those in need of emergency care. The

challenge of maintaining smooth patient flow in emergency departments (EDs) has

become a global issue. Reducing non-value-added activities is the key for patient

flow improvement. Therefore, many hospitals have attempted to implement various

methods to enhance the flow of patients throughout the care system. Lean and Six

Sigma concepts offer methods for solving quality and patient flow problems and

improving overall hospital performance. While some of the recommended solutions

to improve patient flow in emergency departments have arisen from systematic

analyses, many methods focus on short term goals. The absence of a systematic

integrated framework for patient flow improvement in EDs, including the voice of

process, voice of patients and the voice of ED employees presents a major challenge.

In addition, each concept and theory of quality techniques has been considered

separately, or did not combine these theories and concepts in a comprehensive

fashion in a single investigation or case study. In spite of many efforts, scientific

knowledge is still limited regarding which strategies and systematic models actually

improve patient flow in emergency departments.

This research study aims to develop an Integrated Lean Six Sigma

methodology to investigate and identify the patient flow problems in hospital

emergency departments. This study proposes that the voice of the customer (patients

and staff) and the voice of the process (process mapping) should be considered

simultaneously to investigate the current process of patient flow in emergency

department. This approach has included quantitative and qualitative research data

Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department iii

collection methods which are designed specifically for patients, ED staff and staff of

quality department in order to collect field data in one single case study. This

research used the ‘voice of process’, ‘voice of customer’ and A3 Problem Solving

Sheet to identify non-value added activities in the ED process. If non-value added

activities are properly identified and removed, patient flow and quality of care should

be significantly improved. Value stream mapping (VSM) was used to model,

evaluate and improve work and process flow in the hospital’s emergency department

through all patient flow process steps. The current activities were analysed using

VSM and the A3 Problem Solving Sheet as visual tools to identify sources of waste.

Statistical analysis, visual process mapping, and cause and effect diagrams have been

used to identify the major patient flow problems in emergency departments. A fuzzy

logic based performance evaluation model is also proposed to integrate major metrics

that influence patient flow and continuous quality improvement into a single

performance indicator.

The study identified six major factors, namely quality management concepts in

ED, facilities, patients, physicians, nurses, and administrative practices that affect

patient flow in the ED. To solve this problem, a future value stream map (VSM) is

proposed to design a lean process flow through eliminating the root causes of waste

and through process improvements. The appropriate integration of engaged frontline

workers, long-term leadership obligation, an understanding of patients’ requirements

and the implementation of a systematic integration of lean strategies could

continuously improve patient flow, health care service and growth in the ED. Also,

performance metrics based on patients’ and staff’s perspectives were determined, to

be demonstrated and evaluated in a proposed model for continuous quality

improvement.

iv Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department

Table of Contents

Keywords ................................................................................................................................................. i

Abstract ................................................................................................................................................... ii

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

List of Figures ..................................................................................................................................... viii

List of Tables ......................................................................................................................................... ix

List of Abbreviations ............................................................................................................................. xi

List of Publications ............................................................................................................................... xii

Statement of Original Authorship ....................................................................................................... xiii

Dedication ............................................................................................................................................ xiv

Acknowledgements ............................................................................................................................... xv

INTRODUCTION ....................................................................................................... 1 CHAPTER 1:

Background and Research Motivation ......................................................................................... 1 1.1

Research Problem ........................................................................................................................ 5 1.2

Research Questions ...................................................................................................................... 6 1.3

Research Objectives ..................................................................................................................... 7 1.4

Justification for Choosing Saudi Arabia As Case Study .............................................................. 8 1.5

Contributions to knowldge ........................................................................................................... 9 1.6

Thesis structure .......................................................................................................................... 11 1.7

LITERATURE REVIEW ......................................................................................... 15 CHAPTER 2:

Healthcare Systems .................................................................................................................... 16 2.1

Healthcare Challenges .................................................................................................... 16 2.1.1

Role of Hospitals and Patient Flow problems in ED ...................................................... 16 2.1.2

International Perspectives on ED Issues ......................................................................... 18 2.1.3

Operations Management Techniques and Patient Flow Improvements in Emergency 2.2

Departments .......................................................................................................................................... 23

Lean and six sigma Techniques ................................................................................................. 25 2.3

Development of LM and SS in manufacturing ............................................................... 25 2.3.1

Lean and six sigma in healthcare ............................................................................................... 27 2.4

Overview ........................................................................................................................ 27 2.4.1

Patient flow improvement in emergency departments .................................................... 30 2.4.2

Why Lean Six Sigma in ED? .......................................................................................... 32 2.4.3

LSS in manufacturing and healthcare: Major Differences ......................................................... 34 2.5

Evaluation of Lean Six Sigma in Healthcare System ................................................................ 35 2.6

Evaluation of effectiveness in healthcare ....................................................................... 35 2.6.1

Limitations and Research Gap ................................................................................................... 37 2.7

Research Gap Analysis .............................................................................................................. 39 2.8

RESEARCH METHODOLOGY ............................................................................. 41 CHAPTER 3:

Introduction................................................................................................................................ 41 3.1

Research Phiosophy ................................................................................................................... 43 3.2

Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department v

Reserch Approach ...................................................................................................................... 43 3.3

Research Design......................................................................................................................... 44 3.4

Choice of Research Methodology ................................................................................... 44 3.4.1

Research Strategies ......................................................................................................... 45 3.4.2

Research Techniques and Procedures ............................................................................. 48 3.4.3

Data Collection Methods ........................................................................................................... 49 3.5

Survey Questionnaire ...................................................................................................... 50 3.5.1

Archival Records and Documentation ............................................................................ 53 3.5.2

Un-structured interview with focus group ...................................................................... 53 3.5.3

Direct observation ........................................................................................................... 54 3.5.4

Data collection procedures.............................................................................................. 54 3.5.5

Data Analysis Methods .............................................................................................................. 56 3.6

Population and Sample ................................................................................................... 57 3.6.1

Quantitative analysis ....................................................................................................... 58 3.6.2

Qualitative analysis ......................................................................................................... 60 3.6.3

Relaiability and Validitiy ........................................................................................................... 60 3.7

Ethical Considerations ............................................................................................................... 64 3.8

INTEGRATED LEAN SIX SIGMA MODEL FOR PATIENT FLOW CHAPTER 4:

IMPROVEMENT IN EDS ................................................................................................................. 67

Development of Lean Six Sigma Methodology in Emergency Departments ............................ 67 4.1

Proposed Integrated LSS Model in EDs .................................................................................... 68 4.2

Detailed Procedures in LSS Model ............................................................................................ 69 4.3

Lean Six Sigma Methodologies and the case study ................................................................... 74 4.4

Voice of The Process ................................................................................................................. 75 4.5

Voice of the customer ................................................................................................................ 77 4.6

Waste Identification in ED ......................................................................................................... 81 4.7

PATIENT FLOW PROBLEMS AND VOICE OF CUSTOMER (PATIENTS) .. 83 CHAPTER 5:

Voice of The external Customer (patients) ................................................................................ 83 5.1

Descriptive Analysis .................................................................................................................. 83 5.2

Reasons for Attending Emergency Department.............................................................. 85 5.2.1

Concept of Quality in Emergency Department Services ................................................ 87 5.2.2

Satisfaction with Internal and External Environment in ED ........................................... 88 5.2.3

Satisfaction with Systems and Procedures in ED ........................................................... 89 5.2.4

Satisfaction with Medical Services in ED ...................................................................... 91 5.2.5

Satisfaction with Medical Support Services in ED ......................................................... 92 5.2.6

Agreement about Waiting Time Problems...................................................................... 93 5.2.7

Agreement about Medical Services Problems ................................................................ 94 5.2.8

Agreement about ED Staff Care Problems ..................................................................... 95 5.2.9

Exploratory Factor Analysis (EFA) ........................................................................................... 96 5.3

Reasons for Attending an Emergency Department ......................................................... 96 5.3.1

Concepts of quality in ED services ................................................................................. 98 5.3.2

Internal and external environment .................................................................................. 99 5.3.3

Systems and work procedures....................................................................................... 100 5.3.4

Medical services ........................................................................................................... 101 5.3.5

Medical support services .............................................................................................. 103 5.3.6

Long Waiting time ........................................................................................................ 104 5.3.7

Medical service problems ............................................................................................. 105 5.3.8

ED staff care ................................................................................................................. 106 5.3.9

Inferential Statistics ................................................................................................................. 106 5.4

Reasons for attending ED ............................................................................................. 107 5.4.1

Concept of quality in ED services ................................................................................ 107 5.4.2

vi Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department

Experience with internal and external environment in ED ........................................... 107 5.4.3

Experience with systems and procedures in ED ........................................................... 108 5.4.4

Experience with medical services in ED ...................................................................... 109 5.4.5

Experience with medical support services in ED ......................................................... 110 5.4.6

Problems with waiting time in ED ................................................................................ 111 5.4.7

Problems with medical services in ED ......................................................................... 112 5.4.8

Problems with care by ED staff .................................................................................... 112 5.4.9

Structural Equation Modelling ................................................................................................. 113 5.5

Interpretation of the final model .............................................................................................. 118 5.6

PATIENT FLOW PROBLEMS: VOICE OF THE PROCESS AND VOICE OF CHAPTER 6:

THE INTERNAL CUSTOMER ...................................................................................................... 123

Voice of the Internal Customer (ED Staff) .............................................................................. 123 6.1

Descriptive Analysis ................................................................................................................ 123 6.2

Years of ED staff’s Experience .................................................................................... 123 6.2.1

ED Staff’s Satisfaction with Bed Capacity ................................................................... 124 6.2.2

ED Staff’s Satisfaction with Medical Equipment ......................................................... 124 6.2.3

Causes of Waiting Time from ED Staff Perspectives ................................................... 125 6.2.4

A3 Problem Solving Sheets ..................................................................................................... 127 6.3

Problem Identification .................................................................................................. 128 6.3.1

Importance of the Problem: How it affects hospital’s goals or values? ........................ 128 6.3.2

Current Conditions ....................................................................................................... 128 6.3.3

Root Cause Analysis ..................................................................................................... 130 6.3.4

Target Condition ........................................................................................................... 132 6.3.5

Voice of the Process ................................................................................................................ 133 6.4

Observation and voice of the process ........................................................................... 134 6.4.1

Analysis of the emergency department in Asir Central Hospital .................................. 134 6.4.2

Development of ED process flow map ......................................................................... 136 6.4.3

Staff feedback and the A3 problem sheet ..................................................................... 137 6.4.4

Development of the current VSM ................................................................................. 137 6.4.5

ED Layout Considerations ............................................................................................ 142 6.4.6

MAJOR FINDINGS DISCUSSION AND PATIENT FLOW IMPROVEMENT CHAPTER 7:

PLAN 147

Introduction.............................................................................................................................. 147 7.1

Voice of Patients as Lean Strategy .......................................................................................... 148 7.2

Reasons for visiting ED and relevant waste ................................................................. 149 7.2.1

Concept of quality in Emergency Department services ................................................ 152 7.2.2

Patient Satisfaction and Healthcare Service Quality .................................................... 154 7.2.3

Major problems from patient perspectives ................................................................... 157 7.2.4

Voice of staff and process as Lean Strategy ............................................................................ 158 7.3

Root causes of non-value-added activities ............................................................................... 159 7.4

Root causes of overcrowding........................................................................................ 161 7.4.1

Model for Future improvement in patient flow........................................................................ 164 7.5

Future value stream mapping........................................................................................ 165 7.5.1

Integration system for lean strategy IMPLEMENTATION .................................................... 168 7.6

Roadmap for change using Lean strategy approach ..................................................... 170 7.6.1

Evaluation for continuous quality improvement........................................................... 178 7.6.2

Evaluation Model for LSS in ED ............................................................................................. 179 7.7

An efficient leanness assessment model ....................................................................... 180 7.7.1

Lean healthcare measures and performance metrics .................................................... 182 7.7.2

Developing a fuzzy logic model ................................................................................... 184 7.7.3

Performance Metrics..................................................................................................... 187 7.7.4

CONCLUSIONS AND FUTURE WORKS ........................................................... 190 CHAPTER 8:

Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department vii

Significance of The Research Study ........................................................................................ 190 8.1

Development of Innovative Lean Strategy............................................................................... 192 8.2

Voice of Patient ............................................................................................................ 193 8.2.1

Voice of Staff ................................................................................................................ 194 8.2.2

Voice of Process ........................................................................................................... 195 8.2.3

PLAN FOR PATIENT FLOW IMPROVEMENT .................................................................. 196 8.3

Contribution ............................................................................................................................. 196 8.4

Research Limitation and Future Works ................................................................................... 197 8.5

BIBLIOGRAPHY ............................................................................................................................. 199

APPENDICES ................................................................................................................................... 240 Appendix A Patient observation in Aseer Central Hospital emergency department:

reception through triage ................................................................................................ 240 Appendix B Patient observation in Aseer Central Hospital emergency department:

arrival through discharge .............................................................................................. 242 Appendix C Time Study Sheet Developed for Aseer Central Hospital Emergency

Department ................................................................................................................... 244 Appendix D Letter to Aseer Central Hospital Emergency Department ................................... 245 Appendix E Ethical Clearance ................................................................................................. 246 Appendix E Ethical Clearance ................................................................................................. 247 Appendix F Info-consent-questionnaire ................................................................................... 248 Appendix G Patient Questionnaire, page 1 .............................................................................. 249 Appendix G Patient Questionnaire, page 2 .............................................................................. 250 Appendix G Patient Questionnaire, page 3 .............................................................................. 251

Appendix H Agreement to use questions ................................................................................. 252

Appendix I Agreement to use questionnaire ............................................................................ 253

Appendix J Supervisor letter to emergency department director ............................................. 254

Appendix J Supervisor letter to my sponsor ............................................................................ 255

Appendix J Letter from Saudi cultural mission for data collection .......................................... 256

Appendix J Agreement from ED to conduct our research study .............................................. 257

Appendix J Approval for hospital ............................................................................................ 258

Appendix K Statistical analysis tests ....................................................................................... 259

Appendix L A3 problem solving sheets ................................................................................... 280

viii Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department

List of Figures

Figure 1.1: Thesis Structure ....................................................................................................... 14

Figure 3.1: The research ‘Onion’, ............................................................................................. 42

Figure 3.2: Concurrent triangulation Strategy ........................................................................... 45

Figure 3.3: Flowchart of the basic procedures in implementation a convergent desig .............. 49

Figure 3.4: Resaerch methodology ............................................................................................ 56

Figure 4.1: Coneptual framework of LSS for ED ...................................................................... 70

Figure 4.2: Integrated Lean Six Sigma strategy and procedures ................................................ 71

Figure 4.3: Integrated Lean Six Sigma concepts and tools ........................................................ 75

Figure 4.4: Investigating sheet, page 1 ...................................................................................... 79

Figure 4.5: Imporvement sheet, page 1 ...................................................................................... 80

Figure 5.1: Pie chart of transformed educational level variable (number, percentage

shown) ............................................................................................................................ 84

Figure 5.2: Main effect of educational level on systems and procedures scale score

(Standard error shown) ................................................................................................. 108

Figure 5.3: Interaction between gender and educational level in relation to systems and

procedures scale score (Standard error shown) ........................................................... 109

Figure 5.4: Interaction between gender and educational level in relation to medical

support services scale score (Standard error shown) ................................................... 111

Figure 5.5: Main effect for educational level in relation to waiting time problems scale

(Standard error shown) ................................................................................................ 111

Figure 5.6: Measurement model with all significant pathways (unidirectional,

bidirectional) between the nine ED related scale scores (standardised beta

weights show) .............................................................................................................. 116

Figure 6.1: ED’s Staff Experience ........................................................................................... 123

Figure 6.2: ED’s Staff Satisfaction with Bed Capacity ............................................................ 124

Figure 6.3: ED’s Staff Satisfaction with Medical Equipment .................................................. 125

Figure 6.4: ED staff’s perceptions of Overcrowding by Family of Patients at ED .................. 125

Figure 6.5: Staff perceptions of Shortage of Nurses at ED ...................................................... 126

Figure 6.6: Staff perceptions of Patients’ Awareness at ED .................................................... 126

Figure 6.7: Staff perceptions of Shortage of Physicians at ED ................................................ 127

Figure 6.8: Staff perceptions of Insufficient Facilities at ED .................................................. 127

Figure 6.9: Spaghetti diagram for current patient flow in Aseer Central Hospital ED ............ 129

Figure 6.10: Spaghetti diagram for prposed improved patient flow in Asir Central

Hospital ED .................................................................................................................. 132

Figure 6.11: Functional analysis of patient flow in the Aseer Central Hospital

emergency department (ED) ........................................................................................ 136

Figure 6.12: Patient flow in Asir Central Hospital emergency department (ED) (process

map) ............................................................................................................................. 139

Figure 6.13: Waiting time after registration and before triage ................................................. 140

Figure 6.14: Patients’ length of stay ........................................................................................ 141

Figure 6.15: Current layout of the emergency department ...................................................... 144

Figure 7.1: Main factors which are explored from the voice of patient ................................... 149

Figure 7.2: Root causes of overcrowding: Cause and effect diagram for overcrowding in

the emergency department (ED) .................................................................................. 160

Figure 7.3: Current value stream map for patient flow in emergency department (ED) .......... 166

Figure 7.4: Performance category, metrics and the proposed evaluation using fuzzy

approach ....................................................................................................................... 184

Figure 8.1: Key problems from staff’s perspectives ................................................................ 194

Figure 8.2: Key problems from staff’s perspectives ................................................................ 195

Figure 8.3: ED layout problems ............................................................................................... 195

Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department ix

List of Tables

Table 2.1: literature review structure ......................................................................................... 15

Table 2.2: A comparison of payment Systems, ED Crowding, and Attempts to Mitigate

Crowding across 15 Countries ........................................................................................ 21

Table 2.2: Continued .................................................................................................................. 22

Table 4.1: The 7th Sources of Waste in Lean Healthcare. ......................................................... 81

Table 5.1: Descriptive statistics for patient’s gender ................................................................. 84

Table 5.2: Description for the distribution of patien’s gender and education level ................... 85

Table 5.3: Descriptive statistics for importance to attend Emergency Department

(ordered by mean) ........................................................................................................... 85

Table 5.4: Descriptive statistics for agreement with 11 concepts of quality items in ED

services (ordered by mean) ............................................................................................. 87

Table 5.5: Descriptive statistics for satisfaction with six internal and external

environment items in ED (ordered by mean) .................................................................. 88

Table 5.6: Descriptive statistics for satisfaction with eight systems and procedures items

in ED (ordered by mean) ................................................................................................ 90

Table 5.7: Descriptive statistics for satisfaction with six medical services items in ED

(ordered by mean) ........................................................................................................... 91

Table 5.8: Descriptive statistics for satisfaction with 13 medical support services items

in ED (ordered by mean) ................................................................................................ 92

Table 5.9: Descriptive statistics for agreement about six waiting time items in ED

(ordered by mean) ........................................................................................................... 93

Table 5.10: Descriptive statistics for agreement about four medical services items in ED

(ordered by mean) ........................................................................................................... 94

Table 5.11: Descriptive statistics for agreement about three care of ED staff items

(ordered by mean) ........................................................................................................... 95

Table 5.12: Component matrix with nine reasons for attending an ED ..................................... 97

Table 5.13: Rotated component matrix with 11 reasons for attending an ED ............................ 97

Table 5.14: Component matrix with nine reasons for attending an ED ..................................... 98

Table 5.15: Rotated component matrix with five concepts of quality items .............................. 99

Table 5.16: Component matrix with five environment items .................................................... 99

Table 5.17: Component matrix with three external environment items .................................. 100

Table 5.18: Component matrix with 8 systems and procedures items ..................................... 101

Table 5.19: Component matrix with five efficient procedure items ........................................ 101

Table 5.20: Component matrix with six medical services items .............................................. 102

Table 5.21: Component matrix with six medical services items .............................................. 102

Table 5.22: Component matrix with 13 medical support service items ................................... 103

Table 5.23: Rotated component matrix with six medical services items ................................. 103

Table 5.24: Component matrix with five waiting time problem items .................................... 104

Table 5.25: Component matrix with three waiting time problem items ................................... 104

Table 5.26: Component matrix with four medical service problem items ............................... 105

Table 5.27: Rotated component matrix with four medical service problem items ................... 105

Table 5.28: Component matrix with three ED staff health care items ..................................... 106

Table 5.29: ANOVA with reasons scale score as DV and with gender and educational

level as IVs ................................................................................................................... 107

Table 5.30: ANOVA with concepts of quality scale score as DV and gender and

educational level as IVs .............................................................................................. 107

Table 5.31: ANOVA with environment scale score as DV and gender and educational

level as IVs ................................................................................................................... 107

Table 5.32: ANOVA with systems and procedures scale score as DV and gender and

educational level as IVs ................................................................................................ 108

Table 5.33: ANOVA with medical services scale score as DV and gender and


Table 5.34: ANOVA with medical support services scale score as DV and gender and


x Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department

Table 5.35: ANOVA with waiting times problems scale score as DV and gender and

educational level as IVs ............................................................................................... 112

Table 5.36: ANOVA with medical services problems scale score as DV and gender and


Table 5.37: ANOVA with care of ED staff scale score as DV and gender and


Table 5.38: The multi-fit indices with level range and fits ...................................................... 114

Table 5.39: Regression weights ............................................................................................... 117

Table 5.40: Standardized regression weights ........................................................................... 117

Table 6.1: Main steps/activities during patient flow in emergency department ....................... 138

Table 6.2: Waiting times during patient flow in ED process from patient’s perspectives ....... 142

Table 7.1: Dimensions of quality in healthcare services .......................................................... 156

Table 7.2: Roadmap stage 1 (get ready) ................................................................................. 170

Table 7.3: Roadmap stage 2 (set-up) ...................................................................................... 171

Table 7.4: Roadmap stage 3 (soll out) .................................................................................... 172

Table 7.5: Roadmap stage 4 (serform) .................................................................................... 174

Table 7.6: Roadmap stage 5 (sustain) ..................................................................................... 177

Table 7.7: Performance metrics and indexes ........................................................................... 186

Table 7.8: Numerical example and data................................................................................... 187

Table 7.9: Main critical metrics based on Voice of Customer in ED ...................................... 188

Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department xi

List of Abbreviations

A3 A3 Problem Solving Sheet

ED Emergency Department

KSA Kingdom of Saudi Arabia

LSS Lean Six Sigma

MOH Ministry of Health

PHC Primary Health Care

VOC

VOP

VSM

Voice of Customer

Voice of Process

Value Stream Mapping

WHO World Health Organisation

xii Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department

List of Publications

Journal Paper:

• Al Owad, Ali, Karim, M. A., & Ma, Lin (2013) Integrated Lean Six Sigma

approach for patient flow improvement in hospital emergency department.

Advanced Materials Research, 834-836, pp. 1893-1902.

• Al Owad, Ali, Karim, M.A.& Ma, Lin (2014). "Identifying Root Causes of

Patient Flow Problem in Emergency Department by Applying an

Innovative Lean Methodology". (BMC Health Services Resaerch: Under

Review).

International Conferences Attendance and Co-Author Participation:

• The 7th Australian Redesigning Healthcare Summit in Melbourne on 9th &

10th May 2011 (Lean Enterprise Australia & Australasian Lean Healthcare

Network, (Workshop attendance).

• A. Islam, A. M. M. Al Owad, O. M. Badraig, L. Ma, and M. A. Karim (2014)

Factors Affecting New Project Delays in Saudi Arabian Manufacturing

Organisations. In proceeding of The 7th

IEEE International conference on

Management of Innovation & Technology, 23-25 September 2014,

Singapore.

Future Plan for Publications

1. The Relationship between Emergency Department System and Patient

Satisfaction, SEM.

2. The need fo Lean Six Sigma Integration to Solve Patient Flow Isuues in

Emergency Deaprtment: Review Paper.

3. Evaluation of Leanness for Patient Flow in Emergency Department Using Fuzzy

Logic.

4. Redesgin Emergency Department Layout with Process Mapping.

5. The Integration of Action Resaerch with Lean Six Sigma Strategy for Patient Flow

Improvement.

Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department xiii

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the

best of my knowledge and belief, the thesis contains no material previously

published or written by another person except where due reference is made.

Signature:

Date: 12/12/2015

QUT Verified Signature

xiv Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department

Dedication

I dedicate this achievement to my father, Mohammad, and my mother, Zahraa.

Also, I dedicate this accomplishment to my beloved wife, Norah, and my children,

Mohammad and Aseel, who have been a great source of motivation and inspiration.

Finally, I dedicate this thesis to my brothers, Abdulrahman, Khalid, Ahmed and

Medawi, and to my sisters, Fatimah, Aishah, Sharifah, Sumayyah and Ghadi.

Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department xv

Acknowledgements

I am indebted to a number of individuals, teams and organisations for their

support during my studies. Foremost, I express my sincere gratitude to my

supervisors’ team principal supervisor Dr. Azharul Karim and my associate

supervisor Prof. Lin Ma, for their kind efforts, insightful guidance, and relevant

feedback.

I extend my appreciation and thanks to Jazan University in Saudi Arabia for

their support, and their provision of my scholarship. Many thanks going to Saudi

Cultural Mission for their valuable support during my study in Australia. Also, my

special thanks are extended to all my study participants. I also would like to thank all

staff in emergency department at Asir Central Hospital in Saudi Arabia for their help

and support during the data collection in this hospital. Special thanks going to Mousa

Alfifi, Turki Asiri, and all staff in emergency department who gave me time for

conversation and discussion during the time of data collection. Thank to Dr Christina

Houen for copy editing this thesis according to the standards of the Institute of

Professional Editors. Special Thanks going to Dr.Peter Grimbeek and Mr.Ray

Duplock for their help with statistical analysis using SPSS. Also, I would like to

thank all master’s student for brainstorming session and participation for their project

with this research.

My heartfelt appreciation also goes my parents and family, who always

supported and encouraged me with their best wishes and love. Holding a special

place in my heart is my gratitude and love for my wife; she was always available,

through the good times and bad, listening to me when I needed cheering up, and

standing by me at all times.

Finally, many people have contributed to the completion of my study, my

neighbours and friends in Brisbane, my friends in Saudi Arabia to all of them I say

thank you.

Chapter 1: Introduction 1

Introduction Chapter 1:

This chapter outlines the background and research motivation, problem

statement of the research, research questions, and research objectives. It also

describes the contribution of this work. Finally, an outline of the thesis structure is

provided.

BACKGROUND AND RESEARCH MOTIVATION 1.1

In today’s competitive environment, organisations are facing pressure to meet

community expectations regarding quality of product, lower cost and continue

improvement (Drohomeretski et al. 2014; Karim et al. 2008; Nahm et al. 2006;

Ndaita et al. 2015). Thus, process management practices are widely used to reduce

cost and improve quality (Ataseven et al. 2014). 75% of organisations currently

employ strategies to improve their process in order to meet such expectations

(Dhallin 2011). Healthcare organisations are no exception to customer demand and

global competition. Because of increased competition between healthcare providers,

higher customer expectations, stringent checks on insurance payments and new

government regulations, it has become vital for healthcare organisations to enhance

the quality of care provided, increase efficiency, and improve the cost effectiveness

of their services (Al Owad et al. 2014; Rivers and Glover 2008). Clearly, it is

important to serve the right patient in the right place at the right time. Even though

healthcare organisations aspire to achieve high levels of staff, equipment, and space

utilisation, variability in demand and uncertainty of treatment and test duration can

result in situations where given resources are not available at the time they are

needed. This creates a bottleneck that can cause a patient to experience a delay in

treatment (Thompson et al. 2013). Therefore, an improvement in patient flow brings

the benefits of increased safety, increased customer satisfaction and overall,

improvement in the quality of healthcare services.

Services at the emergency departments of hospitals are essential, and the

quality of service affects the quality of life of the patients, and often makes the

difference between life or death. Unnecessary deaths can be the result of poor quality

services in emergency departments (EDs) (Maa 2011). Present day emergency

2 Chapter 1: Introduction

departments have seen dramatic increases in patient volume (Eitel et al. 2010; Schuur

et al. 2013). For example, the demand for ED services has recently increased in the

United States, where millions of individuals access ED services each year (Schuur

and Venkatesh 2012). In the past two decades, Australia has seen a major growth in

the use of EDs: there has been over 15% increase in the use of ambulance services,

and over 30% of Australians attend EDs each year (Pines et al. 2011; Toloo et al.

2011). The annual utilisation rate of EDs in Australia has increased over the past

decade by 2.0% for walk-in patients and 3.7% for those brought by ambulance (He et

al. 2011). In Saudi Arabia, there were more than 31 million visits to Primary

Healthcare Centres (PHC) and over 15 million ED visits in 2006, and 70% of EDs

have reported more than 100,000 annual visits also in 2006 (Pines et al. 2011).

It is clear that emergency departments in public and private hospitals are under

continuous pressure to serve all patients with a high standard of care and minimum

cost. Therefore, the patient flow issue or overcrowding in EDs has become one of the

most challenging health delivery problems that hospitals and healthcare providers are

facing over the entire world (Pines et al. 2011; Sun et al. 2013). Certainly, patient

flow is a key factor in a hospital's operational performance, and is strongly associated

with the total quality and cost of health care (Armony et al. 2011; Niska et al. 2010;

Pitts et al. 2008). Because EDs are treated as gatekeepers for the hospital system, it is

natural that hospitals concerned about timely delivery of their services are looking

for the most effective ways to improve the patient flow process (Saghafian et al.

2014). The patient flow process can be identified from two different perspectives: the

clinical view which represents the progress of patient health status; and the

operational side which represents the movement of patients through the healthcare

facility (Cote 2000; Cote and Stein 2000; Marshall et al. 2005). To address the

challenge of how to improve patient flow, the literature review outlined three general

solutions to ED crowding which are related to increasing resources, demand

management and operations research (Hoot and Aronsky 2008). The literature has

focused on techniques for patient flow improvement by monitoring and forecasting

overcrowding using regression modelling, formula-based equations, queuing theory-

based models, time series analysis, and discrete event simulation models. However,

these methods concentrate on near future expectations and do not address

overcrowding and patient flow in ED space (Xin-li et al. 2013). In addition, little


consideration has been paid in the literature to the impact of overcrowding on the

quality of care and patient outcomes in EDs (Forster 2005; Graban 2009). Overall,

measures addressing overcrowding have been only weakly combined with improving

quality of care (Bernstein et al. 2003).

Periods of high ED crowding are a global problem, and are associated with

increased adverse events and patient mortality, greater inpatient length of stay and

hospital costs, and poor quality of care (Bernstein and D'Onofrio 2009; Pines et al.

2011; Sun et al. 2013). In addition, ED crowding is the cause of significant concern

with patient safety internationally (Hoot and Aronsky 2008; Moskop et al. 2009;

Pines et al. 2011).

With approximately all health care institutes involved in some kind of quality

improvement (QI) activities (Ben-Tovim et al. 2008; Eller 2009; Gowen Iii et al.

2008), the failure to make significant improvements in quality is concerning (Eller

2009; Glasgow et al. 2010). Since overuse of EDs has been almost entirely left out of

recent quality improvement activities in the health care system (Chassin 2013), there

is a pressing need to address the overuse of health services and reduce avoidable

shortcomings of care. While improving the quality of care and keeping costs down,

healthcare suppliers need to study, develop, implement and sustain process

management systems that use innovative and creative solutions to the healthcare

delivery processes. These new approaches must investigate a clearly identified

problem with clear goals in order to re-design their existing process management

system to develop better patient and worker safety (Dickson, Singh, et al. 2009).

Recently the literature on emergency medicine has reported the successes of

implementing operations management strategies that many other industries have long

been implementing. Many studies and governing organisations, including the

Institute of Medicine (IOM), have recommended increased utilisation of

improvement science and systems engineering to address this rising challenge

(Brook 2010; Hines et al. 2004; Singh and Sharma 2009; Vinodh et al. 2010; Yusof

et al. 2012). There is an opportunity to make use of these tools in EDs in an effort to

continuously improve the standard of care (Bell 2010; Irani et al. 1999; Robson

2002; Roszell 2013; Taner et al. 2007; Vermeulen et al. 2014). For instance, the lean

method, which was initially developed for process improvement in manufacturing, is

identified as one possible tool for use in improving the systems of care and


throughput in the ED (Dickson, Anguelov, et al. 2009; Dickson, Singh, et al. 2009;

Han et al. 2007). These methods, although great attention has been paid to them in

relation to other situations, have only been considered by a few researchers for the

entire health care system, with less attention to EDs (Holden 2011).

The question of how lean thinking can best be modified to health care has been

raised (Holden 2011). The use of lean in healthcare, which was virtually non-existent

a decade ago, is beginning to have prominence (Ben-Tovim et al. 2008; Decker and

Stead 2008; Eller 2009; Jimmerson et al. 2005; King et al. 2006; Ng et al. 2010;

Piggott et al. 2011). Some researchers have reviewed and analysed the limited

applications of lean thinking in EDs. However, researchers have concluded that

“more work remains in understanding lean in the ED” (Holden 2011); and that “we

don’t thoroughly understand the use of lean in the ED, nor do we know how to apply

lean methods in the ED” (Dart 2011). Lean thinking has recently been coupled with

Six Sigma to simultaneously make efforts to eliminate waste and improve quality

(Berwald et al. 2010; Langabeer et al. 2009). Lean Six Sigma (LSS) can be used to

solve the issues of patient flow and waste while eliminating variations in standard of

care (Bhat et al. 2014). The basic lean concepts are the elimination of waste through

the standardisation of processes, and the participation of all employees and

(customers) in process improvement (Dickson, Singh, et al. 2009). Six Sigma helps

to quantify problems; facilitates evidence-based decisions (and so keeps time from

being wasted on anecdotal evidence); helps the organisation understand variation and

reduce it; and identifies the root causes of variation in order to find sustainable

solutions (Kuo et al. 2011).

To contain costs and improve organisational performance and quality of care, a

number of quality management concepts and tools are being employed in the

healthcare domain. It is clear that optimal care can only be delivered when optimum

use of the resources can be ensured, and the right patient is in the right place with the

necessary service available (Cameron et al. 2014; Mckee et al. 2014). Operations

management techniques help to identify the most efficient ways of using time,

manpower, space and other resources. Thus, EDs must focus on improving their

patient flow process as a main strategy for addressing the aforementioned issues.

This brings us to the research problem of patient flow management in emergency

departments.


RESEARCH PROBLEM 1.2

Emergency Departments (EDs) are designed to provide high quality medical

service, with immediate availability of resources to those in need of urgent care at

any time. In a research study involving 56 emergency departments (EDs), it was

found that 37% of all ED patients were non-urgent patients (Hansagi et al. 2001).

Moreover, delays in providing treatment often cause patients to leave the EDs

without any treatment. As a result, overcrowding has become a problem globally in

emergency departments (Eitel et al. 2010).

The growth in demand has placed increased pressure on emergency

departments, with waiting times stated as the most important cause of patient

dissatisfaction (Al Owad et al. 2014; Trout et al. 2000). Consistent with the MORI

survey, reduction of the waiting times for patients attending emergency departments

was the most significant area for development (Cooke and Jenner 2002). Further,

delays in emergency departments have been related to adverse outcomes (Derlet and

Richards 2000) and increased violence (Stirling et al. 2001).

Numerous factors affect patient flow in emergency departments (Walley 2003;

Yoon et al. 2003). The key performance measures that determine patient satisfaction

are waiting time for care and turnaround time. Waits and delays occur as a result of

poorly designed flows and inefficient processes. Researchers working to improve

understanding of the causes of overcrowding in emergency departments have found

the input-throughput-output theoretical model to be an accepted approach (Bernstein

and D'Onofrio 2009; Green and Kolesar 1987; Rotstein et al. 1997). Improvement in

flow should increase patient satisfaction, reduce waste, and eventually improve the

quality of services.

Some improvements concentrate on short term enhancements, although a

number of the suggested solutions to improving patient flow in emergency

departments have resulted from systematic analyses (Harvey et al. 2008). Recently,

lean approaches have started to appear in the healthcare industry, but they are still in

the early stage. Many healthcare organisations are turning to lean processes to

eliminate waste, reduce costs, and offer quality healthcare (Weinstock 2008). Many

of the new approaches are motivated by lean healthcare thinking, with a focus on

flow direction, reduction in waste elements, continuous quality improvement, and

involvement of all employees (Ahlstrom 2004; de Koning et al. 2006; Oredsson et al.


2011). However, no systematic approach to identifying the root causes of

overcrowding in EDs can be found in the literature, and little evidence has been

given in reports to support claims about the effective utilisation of lean systems to

decrease journey times and improve emergency department patient flows (Rogers et

al. 2004). In spite of efforts made at improvement, scientific evidence is still limited

as to which strategies and systematic models actually improve patient flow in

emergency departments.

Simultaneous consideration of the voice of the patient, the voice of staff and

the voice of the process can help identify the root causes of patient flow problems in

ED. However, researchers have failed to take this integrated approach. The American

Academy of Emergency Medicine states that “it is currently unknown which

strategies provide the best solution to fix throughput in the ED” (Eitel et al. 2010). It

is obvious that researchers have not brought all the stakeholders (including patients

and ED staff) into a single platform. Currently, there is no integrated model available

for improving patient flow in emergency departments. According to Lawal et al.

(2014), “primary studies often lack appropriate concepts explicitly stated, research

designs, appropriate analysis and outcome measures”.

Hence, to address the gap in the literature, the main aim of the research

presented in this thesis is to:

‘Investigate and identify the patient flow issue (overcrowding) in emergency

departments hospitals by using an Integrated Lean Six Sigma methodology and

taking the voice of patient, voice of staff and voice of the process into

consideration’.

RESEARCH QUESTIONS 1.3

The research study is going to address the following key research question to

achieve the overall research aim:

How can the patient flow in emergency departments and quality of

services be continuously improved with the integration of Lean Six Sigma

methodology?

To answer the main research question the following sub-questions are going to

be addressed:


1. How can Lean thinking and Six Sigma methodology be integrated in

one combined approach for continuous quality improvement in the

patient flow in emergency departments?

2. How can the root causes of overcrowding that impact patient flow in

emergency departments be determined by integrating the voice of

customer and voice of process?

3. How can waste affecting patient flow and quality of service in EDs be

identified by using Lean strategy and Six Sigma techniques?

4. How can patient flow improvement in EDs be developed based on

Lean Six Sigma adoption?

5. How can the effectiveness of integrating Lean Six Sigma in patient

flow be evaluated for continuous process improvement?

RESEARCH OBJECTIVES 1.4

In order to address the research questions outlined above, this research aims to

develop a systematic approach based on integration of Lean and Six Sigma

methodology to identify and eliminate non-value added activities and as a result

improve the patient flow in emergency departments. In addition, it proposes an

evaluation method based on fuzzy logic to evaluate the performance of Lean Six

Sigma Integration. More specific objectives are listed below:

Integrate Lean concepts with Six Sigma methodology for Saudi

Arabian context for continuous quality improvement in the patient

flow in emergency departments.

Develop an innovative method by combining voice of customer and

voice of process to investigate the main causes for overcrowding that

affect the patient flow in emergency department.

Develop value stream mapping to identify waste that takes place in

emergency departments

Propose future value stream map suggesting future improvements.


Develop an evaluation method to measure the performance

efficiency of an Integrated Lean Six Sigma approach in patent flow

improvement.

JUSTIFICATION FOR CHOOSING SAUDI ARABIA AS CASE STUDY 1.5

A number of studies have explored the utilisation of the lean technique and Six

Sigma method (DMAIC) in healthcare services management in Western countries

(Dickson, Singh, et al. 2009; Ng et al. 2010). It has been validated that the

application of lean philosophy in ED can develop patient flow and then reduction of

congestion and access block (King et al. 2006). In the literature review, ED process

and structural changes has been involved to improve patient care in 15 EDs in the

United State, Australia and Canada (Holden 2011). However, there was no evidence

that these research findings have application in developing countries where contexts

are completely different. In addition, many opposing results of lean implementation

in healthcare have been reported (Chan et al. 2014). There is a real need to conduct

further studies of LSS in different healthcare settings, including emergency

department facilities particularly in developing countries.

This need is increased in Saudi Arabia, where there is increased utilisation of

EDs for non-urgent problems, which is the important cause of overcrowding

(Qureshi 2010). While Saudi populations have access to unlimited, free medical care

among a network of primary healthcare centres (PHCCs) in the country, Middle

Eastern prevalence studies have obtained that between 59.4% (Siddiqui and Ogbeide

2002) and 88.7% (Shakhatreh et al. 2003) of patients visiting EDs are categorised as

non-urgent. This can increased waiting times and delayed interference for more

urgent patients (Elkum et al. 2009). One study examining trends in ED utilisation

over a 3-year period in a hospital in the Eastern region of Saudi Arabia found that the

number of visits increased by approximately 30% and length of stay also increased

over this period and of these, approximately 60% of patients presented with non-

urgent conditions with some non-urgent patients had multiple visits to the ED

(Rehmani and Norain 2007). In addition, in a recent study in the capital city of

Riyadh, 50% of ED managers stated that overcrowding was always a challenge in

their department, and 40% of them described it was often a problem (Tashkandy et

al. 2008). A total of 70% of EDs have stated more than 100,000 annual visits, and


more than half of the patients waited more than six hours in the ED, and 15% waited

more than 24 hours (Pines et al. 2011).

Despite the allocation of massive resources and huge financial spending by the

Saudi Ministry of Health (MOH), issues on the quality of healthcare with population

and patients’ demand expectations are still arising, then quality of healthcare in the

Saudi Arabia may be collapsing (Albejaidi 2010; Almutairi and Moussa 2014;

Barratt et al. 2011). With this increase in overcrowding issue in Saudi EDs, it is

important to investigate this challenge with a specific goal and objectives to solve the

patient flow issue in emergency departments. However, there are no specific national

initiatives to reduce emergency department crowding in Saudi Arabia hospitals

(Pines et al. 2011). To the best of my knowledge, no work has been done in this area.

Consequently, there is a need to conduct a research study to consider emergency

department overcrowding in Saudi Arabia and investigate the patient flow issue by

using LSS methodology and tools. The findings may assist to identify the major

causes of overcrowding that affect patient flow in emergency departments, and

develop a plan and strategies for short to long term continuous quality improvement.

CONTRIBUTIONS TO KNOWLDGE 1.6

Overcrowding can be seen as a global issue which directly affects the quality

of services in the emergency department. Therefore, providers need to streamline

their organisational systems and processes to fully support the process required to

deliver high quality care. It is clear that EDs are under even greater pressure to

improve their operations; however, there are only a few indications of providers

awareness of the tremendous need for focus on improving ED operations (Saghafian

et al. 2014). Indeed, such improvements are needed for increasing profit (Reeder et

al. 2003; Weiss et al. 2004), improving patient satisfaction (Epstein and Tian 2006;

Weiss et al. 2006), and more importantly, improving patient safety (Raj et al. 2006;

Weiss et al. 2002).

The proposed research has developed a systematic approach through

integration of Lean strategy with Six Sigma methodology to improve patient flow in

emergency departments. An in-depth investigation has been conducted to consider

the main causes for various wastes that directly or indirectly affect emergency

department patient flow. In addition, this approach has included quantitative and


qualitative research methods designed specifically for patients, ED staffs, and

Quality Department staffs in order to collect real data based on Lean thinking and

Six Sigma principles.

By achieving the objectives outlined in section 1.4, this research study aims to

make the following contributions to the scientific communities, practitioners and

healthcare providers:

For the scientific community:

Development of a new systematic approach based on integrated Lean

and Six Sigma to improve the patient flow in an emergency department.

The research will be conducted in Saudi Arabia, a developing country

where Lean Six Sigma concepts are still new. Thus, this research will

be a new contribution to the research community in the Middle East.

Development of an evaluation model to measure the effectiveness of an

integrated Lean Six Sigma model in patient flow improvement based on

fuzzy logic.

For practitioners:

A new layout for patient flow in the emergency department with

minimum non-value added activities.

The root causes for delay and overcrowding in emergency departments

will be made known, which will help them to deal with these problems.

Understanding of the patient’s needs and priorities and their

requirements will be increased.

For hospitals:

Elimination of waste and non-value activities, which will result in

better patient flow and shorter turnaround time. Elimination of waste

will also result in better utilisation of resources.

Increase in patients’ satisfaction.

Improvement of hospital staff satisfaction.

Increase in reliability and credibility in healthcare.


Reduction of costs and improvement in the quality of health care.

THESIS STRUCTURE 1.7

The thesis structure is designed based on the research questions and objectives,

and outlined in a flow chart in Figure 1.1.

As shown in Figure 1.1, this thesis starts with an extensive literature review of

the areas of patient flow problems (overcrowding) in emergency departments, and

Lean Six Sigma in healthcare. Particularly, in emergency departments, it explores:

the identification of patient flow problems in EDs using integration of Lean Six

Sigma concepts and methodology; assessment methods to evaluate the effectiveness

and performance of Lean Six Sigma Integration on patient flow improvement in ED

and healthcare system; and patient flow problems in emergency departments in Saudi

Arabia hospitals as a case study.

This is then followed by a discussion of the methodologies, approaches and

tools that are employed to achieve the research objectives. Upon completion of the

review, the framework structure is described and finally a real emergency department

case study is presented. A brief outline of the thesis chapters is given below.

Chapter 2: Literature review

This chapter reviews the literature on emergency department challenges,

concentrating on patient flow problems. It also discuss the concepts of lean six sigma

in healthcare and the available solutions that have been used to eliminate waste,

reduce variation and improve the quality of the healthcare system. Then, the

implementation of an integrated lean Six Sigma approach in emergency departments

is reviewed, to guide the development of an appropriate integrated framework to

investigate and improve the patient flow in emergency departments. Also, a review

of previous research into assessment of lean integration efficiency in the healthcare

system is described, which leads to the proposal of an evaluation model for

continuous quality improvement.

Chapter 3: Research methodology

This chapter describes the research philosophy, approach, design, strategies,

techniques and procedures employed for carrying out the study. It also explains the

justification of the research method used and highlights the data collection methods


and the analysis tools used during the different stages of this study. Finally,

reliability, validity, ethical considerations and limitations are presented.

Chapter 4: Integrated Lean Six Sigma model for patient flow improvement

This chapter outlines the proposed integrated framework of Lean Six Sigma for

continuous improvement of patient flow in emergency departments. It includes the

integration of Lean Thinking principles, Six Sigma methodology (DMAIC), Lean

Six Sigma attributes, performance metrics, and the proposed evaluation model.

Chapter 5: Patient flow problems and voice of customer (patients)

This chapter outlines the analysis of patient questionnaires which describe their

experience with current systems and identify their needs. To explore the reasons for

attending ED, the concept of quality in ED services and the satisfaction levels with

ED systems, descriptive analysis, exploratory factor analysis, inferential analysis and

structural analysis model are used. Then, the importance of patient voice in ED flow

improvement is discussed.

Chapter 6: Patient flow problems and voice of the process and staff

This chapter describes the analysis of the internal customer (ED staff) and

explains the ED process based on observation of the current system. Descriptive

analysis is used to explore the experience of ED staff and measure their level of

satisfaction ED with bed capacity and medical equipment in the current system.

Also, an A3 problem solving sheet is used to identify the patient flow problems in

EDs from the staff’s perspective, based on their working experience. Finally, direct

and participant observation, current system procedures, ED and current value stream

mapping are considered to understand the voice of the current process.

Chapter 7: Discussion of major findings and patient flow improvement plan

This chapter integrates, discusses and concludes the significant findings of the

previous chapters (4, 5 and 6). Then it decides a plan for patient flow improvement

based on the current and future situation. Next, it identifies significant performance

metrics that are based on voice of customer and a process for continuous quality

improvement. These proposed performance metrics will help hospital decision

makers in evaluation of overall performance in ED patient flow for continuous

quality improvement. It will include a section about assessment. This section


proposes an assessment model to evaluate the efficiency of lean integration in

solving patient flow problems for continuous quality improvement. Based on the

major findings which described in chapter 7, qualitative and quantitative

performance metrics that represent emergency department performance are

identified. This study identifies the major performance metrics and proposes an

initial assessment model based on fuzzy logic to be evaluated in the future.

Chapter 8: Conclusion, limitations, recommendations and future research works

Finally, a discussion of the research study’s significance, contributions,

outcomes, limitations and recommendation for future research is presented.


Figure 1.1: Thesis Structure

Chapter 1:

Introduction

Chapter 2: Literature

Review

Chapter 3: Research

Methodology

Chapter 4: Integrated

Lean Six Sigma Model

for Patient Flow

Improvement

Chapter 5: Patient Flow

Problems and Voice of

Customer (Patients)

Chapter 6: Patient

Flow Problems and

Voice of the

Process and Staff

Chapter 7: Major

Findings Discussion

and Patient Flow

Improvement Plan

Chapter 8: Conclusions

and Future Works

Chapter 2: Literature Review 15

Literature Review Chapter 2:

The previous chapter outlined the essential research issues of this work. This

chapter examines the literature and provides an insight into the literary contributions

made towards Lean and Six Sigma healthcare. It also outlines the framework of the

methodologies of Lean Six Sigma problem investigation and leanness assessment.

The main aims of this chapter are to demonstrate the gaps in the academic literature

on the subject of Lean Six Sigma integration in healthcare, particularly regarding

problem investigation methodology and leanness assessment methods for healthcare

organisations, with special reference to patient flow problems in emergency

departments.

Table 2.1: literature Review Structure

As shown in Table 2.1, this chapter starts with an extensive literature review of

the areas of healthcare system challenges with concentration on ED issues. Then, it

Section Sub-section

16 Chapter 2: Literature Review

discusses the available solutions to solve this issue using operation management

techniques with focus on lean six sigma techniques. Next, a general overview of lean

six-sigma in manufacturing is given. How these concepts transfer to healthcare sector

will is also presented. Then, the need for lean six sigma for patient flow

improvement in ED has been discussed. Finally, this chapter will present the

limitation of the previous literature about the application of lean six sigma for patient

flow improvement in ED and will conclude with some analysis for research gap.

HEALTHCARE SYSTEMS 2.1

Healthcare Challenges 2.1.1

There are continual pressures on healthcare costs coupled with growing needs

for healthcare services, linked with limited resources and evidence of poor

performance which affect national and local healthcare organisations (Medicine

2013; Porter and Lee 2013; Publishing and Oecd 2002). Public demand for increased

quality coupled with the increased need to do more with less has led healthcare

organisation management teams to re-evaluate their operations strategy (Ballé and

Regnier 2007). These days, healthcare settings such as hospitals operate under a

barrage of improvement programs as a result, often adding to the pressure of

operations rather than dealing with problems in the existing systems (Fillingham

2007). It is clear that healthcare organisations are complex dynamic systems that

focus on improving quality of care and meeting stringent guidelines. Therefore, the

need for changes in health care is more apparent today, and these challenges have led

healthcare organisations to look for methods to improve quality, safety and value in

health service delivery (Holden 2011; Sloan et al. 2014).

Role of Hospitals and Patient Flow problems in ED 2.1.2

Hospitals combine many service units which work together in a systematic

process to provide the needs of the patients under treatment. A characteristic hospital

system involves a number of cooperating departments/sub-units: outpatient

department (OPD); emergency department; operating theatre (OT); inpatient wards;

intensive care unit (ICU)/intensive therapeutic unit (ITU); diagnostic services, such

as pathology and radiology, and so on, all within a physical range in an organisation.


Each department has the responsibility for a single, or at the most, a few related

functions within the hospital. Such an integrated system dealing with different

aspects of healthcare and its problems may have several possible combinations of

service types (hence, varieties of care pathways), patient status, and types of

facilities, as well as physician profile/specialisation, all available for treatment under

varieties of constraints (primarily determined by availability of resources)

(Bhattacharjee and Ray 2014). The overall performance of a hospital system is

dependent on the performance of all the departments/sub-units which have different

operational issues, and over the years various performance measures, such as the

waiting time of patients at an emergency department, length of stay of an inpatient,

utilisation of operating rooms, bed utilisation in ICU, to mention a few, have been

used to assess the performance of these operations (Cardoen et al. 2010; Cayirli and

Veral 2003; Kim et al. 1999; McClean and Millard 1998). Despite an increasing need

for development (Leape and Berwick 2005; Medicine 2000, 2001; Wachter 2010),

hospitals struggle to improve patient experience, efficiency and quality of care

(Berwick et al. 2006). Unsuccessful operations, defined as cases where an employee

does not have the supplies, equipment, information, or people needed to complete

work missions, cause hospitals’ poor performance (Tucker 2004; Tucker et al. 2014).

Certainly, health care delivery has usually been arranged around specialties and

professional groups that address patients' issues one at a time (function-based

organisation), rather than around the entirety of each patient's requirement (Beech

and Vissers 2005; Glouberman and Mintzberg 2001a, 2001b). Process and flow

problems are factors that contribute to delays and overcrowding. As the emergency

department (ED) is considered a central place and main gate in the healthcare

system, issues in other components of the system may be linked with ED (Yen and

Gorelick 2007). Also, problems in the ED may strongly affect the public’s point of

view about the whole healthcare organisation (Mazzocato et al. 2012). Therefore, the

emergency department is regularly under increased pressure to meet community

expectations in terms of healthcare quality services. Clearly, overcrowding, treatment


delays, reduction in both quality and safety of care, and inefficient use of available

resources are considered as patient flow obstacles during the patient’s journey in ED

(Campbell et al. 2004; Cooke et al. 2004; Lecky et al. 2014; Pines et al. 2011;

Richardson and Mountain 2009). Under this increased demand for healthcare

services, emergency departments all over the world are challenged with a variety of

problems which affect patient flow. The following section presents an international

overview of emergency department problems, and then considers the flow of patients

through the emergency department system as an important factor that affects and is a

determinant of the performance of healthcare delivery processes in a hospital system.

International Perspectives on ED Issues 2.1.3

In the US, emergency departments are the main gate to hospitals through which

50% of non-obstetrical admissions occur (Pitts et al. 2008; Saghafian et al. 2014). Of

late, the demand for ED services has considerably increased, with millions of

individuals accessing healthcare in the emergency department each year in the

United States (Schuur and Venkatesh 2012). In addition, the number of emergency

departments has decreased by 10%, while the number of visits to the emergency

departments has increased by more than 20% (Hopp and Lovejoy 2012). Therefore,

overcrowding has increased in the ED environment.

In Australia, growing demand for emergency services and overcrowding have

increased in public hospital EDs since 1990 (AIHW 2008). There is an ongoing 3.5%

annual rise in visits across a wide sample of EDs accredited for training, according to

the Australasian College for Emergency Medicine (ACEM) (Richardson et al. 2009)

and there were more than 6.7 million emergency department visitors in 2012–13

(AIHW 2013).

The Saudi health care system is one of the health care systems across the world

which are suffering from quality and patient safety issues. In Saudi Arabia, as

elsewhere, improving the utilisation of EDs is the subject of research and debate

(Arabi and Al-Shimemeri 2003; Qureshi et al. 1997). The Ministry of Health in

Saudi Arabia (MOH) arranges primary health care services through a network of

primary health care medical centres (PHCs) around the country, and every citizen


there has the right to access to unlimited free medical care, particularly in public

hospitals and primary health care centres (Almalki et al. 2011; Pines et al. 2011). In

2012, the number of PHC centres rose from 1,640 in 1989 to 2,259, and the total

number of hospitals increased to 259, with 35,828 beds; there were 35.57 million

visits to PHCs and over 20 million ED visits (Health 2012). Despite the allocation of

massive resources and huge financial spending by the Saudi Ministry of Health

(MOH), the reality is that the quality of care has not been satisfactory (Albejaidi

2010; Barratt et al. 2011).

A total of 70% of EDs have reported more than 100,000 annual visits, and

more than half of these patients waited more than six hours in the ED, and 15%

waited more than 24 hours (Pines et al. 2011). In addition, in a recent survey in the

capital city of Riyadh, 50% of ED directors reported that overcrowding was always a

problem in their department, and 40% of them reported it was often a problem

(Tashkandy et al. 2008). In Saudi Arabia, increasing utilisation of EDs for non-

urgent problems is the leading cause of overcrowding (Alyasin and Douglas 2014;

Qureshi 2010). One study examining trends in ED utilisation over a three year period

in a hospital in the Eastern region of Saudi Arabia found that the number of visits

increased by approximately 30%, and of these, approximately 60% of patients

presented with non-urgent conditions, and some non-urgent patients had multiple

visits to the ED, which increased the length of stay over this period (Rehmani and

Norain 2007). With the assumption that patients will get better care at the tertiary

hospitals, many of them prefer going directly to tertiary hospitals, particularly in

EDs, rather than accessing the primary health care centre and the community

hospitals (Pines et al. 2011). Evidently, a systematic approach has not been found to

reducing overcrowding and improving patient flow in emergency departments.

Clearly, there are major gaps in the Middle Eastern literature on ED overcrowding.

Currently, there are no specific national initiatives to reduce ED crowding in Saudi

Arabia (Alyasin and Douglas 2014; Pines et al. 2011).


The growth of ED overcrowding is related to increased adverse events and

patient mortality, greater inpatient length of stay and hospital costs, and poor quality

of care (Bernstein and D'Onofrio 2009; Pines et al. 2011; Sun et al. 2013). In

addition, emergency department (ED) overcrowding creates an important

international concern for patient safety (Hoot and Aronsky 2008; Moskop et al. 2009;

Pines et al. 2011). Table 2.2 summarises information on ED crowding from different

countries.


Table 2.2: A comparison of payment systems, ED crowding, and attempts to mitigate crowding across

15 countries (Pines et al. 2011)


Table 2.2: Continued

With respect to the speed of service, the cost of care, overcrowding and patient

safety, there is a great need for improvement in emergency departments (EDs)


(Holden 2011). To do this, it is necessary to understand the operations in emergency

departments where control of patient flow is a major factor for improving hospital

operations. Armony et al. (2011); (Huang et al. 2012; Niska et al. 2010; Pitts et al.

2008) conclude that patient flow is a key factor in a hospital's operational

performance, which is integrated with the overall quality and cost of healthcare, and

these factors must be considered for investigation and improvement, especially in

emergency departments, which are the first and main gate or window into the

hospital system.

OPERATIONS MANAGEMENT TECHNIQUES AND PATIENT FLOW 2.2

IMPROVEMENTS IN EMERGENCY DEPARTMENTS

Patient flow refers to the management and movement of patients in a

healthcare facility. To evaluate and improve aspects of the patient experience and the

quality of healthcare services, healthcare institutions utilise patient flow analyses

(Asplin et al. 2006; Baker et al. 2009; Derlet et al. 2001; Fieldston et al. 2011;

Fieldston et al. 2014; Litvak 2004; Pines et al. 2007; Solutions 2009; Wennberg

2004). Public demand for increased quality coupled with the pressure to do more

with less, as previously shown in section 2.1, has led healthcare organisation

management teams to re-evaluate their operations strategy (Ballé and Regnier 2007).

In fact, the importance of patient flow management has been acknowledged by the

medical community, as stated in Leadership Standard LD.3.10.10, set by the Joint

Commission on Accreditation of Hospital Organisations (Huang et al. 2012).

It is clear that healthcare settings such as hospitals these days operate under a

barrage of improvement programs as a result, often adding to the pressures of

operations rather than dealing with the problems in the existing systems (Fillingham

2007). Over many years, patient flow improvement has been a common theme, as

cited by Silvester et al. (2014) based on work reviews (Haraden et al. 2003; Haraden

and Resar 2004; Kosnik 2006; Litvak 2010; Millard and McClean 1996; Nolan

1996). Clearly, patient flow has caught the attention of researchers in Operations

Research, Applied Probability, Service Engineering and Operations Management.


With nearly all health care institutions participating in some form of quality

improvement (QI) activities (Chassin 2013; Gowen Iii et al. 2008; Manuel 2008;

Walston et al. 2000), the lack of substantial improvement in quality is disheartening

(Chassin 2013; Glasgow et al. 2010). Unless substantial changes are made to the way

in which quality improvement is conducted with an effort to seriously address the

widespread overuse of health services, desired progress will not be achieved

(Chassin 2013). Thus, healthcare providers need to study, develop, implement, and

sustain process management systems which must investigate a clear identified

problem and improve patient and worker safety while enhancing the quality of care

and keeping costs down. For a number of institutions this means a re-engineering of

their current process management system (Dickson, Singh, et al. 2009).

Many quality management and process reengineering practices and process

improvement methodologies offering increased efficiency originate in the

manufacturing and electronics industry. These have been developed and applied with

measurable success in the service sector, particularly in healthcare (Glasgow et al.

2010; Radnor and Boaden 2008). However, there are challenges to operation

research (OR) and operation management (OM) in the ED when it comes to

assessing trade-offs between operational improvement and quality. According to

Saghafian et al. (2014) while this approach is useful for identifying major issues, it is

insufficiently sensitive to capture small changes in quality that may result from day-

to-day changes in ED operations. Nevertheless, OR/OM techniques have

significantly helped various parts of hospitals (and especially the ED) to improve

their performance gauging metrics; see, for example (Hopp and Lovejoy 2012;

Ozcan 2009). Therefore, Lean Manufacturing and Six Sigma operations

improvement techniques are appropriate to help health care organisations solve the

challenge (Dickson, Anguelov, et al. 2009; Womack and Miller 2005).

The use of lean in healthcare, which was virtually non-existent a decade ago, is

beginning to be well-known (Ben-Tovim et al. 2008; Decker and Stead 2008;

Dickson, Singh, et al. 2009; Eller 2009; Holden 2011; Jimmerson et al. 2005; King et

al. 2006; Ng et al. 2010; Piggott et al. 2011). This has recently been coupled with Six

Sigma to simultaneously make efforts to eliminate waste and improve quality


(Berwald et al. 2010; Langabeer et al. 2009). Hence, combining them with operation

research methods can be a successful path for research (Glasgow et al. 2010).

LEAN AND SIX SIGMA TECHNIQUES 2.3

LSS is deemed as a well-liked and accepted tool for improving the operational

quality in manufacturing sectors (George 2002) and other fields (Koning et al. 2006;

Pojasek 2003; Wang and Chen 2010). Therefore, the combination of the two

approaches improves competence and efficiency, and assists in achieving superior

performance in comparison to the approach where in each method is independently

put into practice (Antony and Kumar 2012). The LSS importance has directed

several efforts to build an all-inclusive approach of attaining constant development

and enhancement (Albliwi et al. 2014). To optimise the whole system and focus on

the right strategies in the correct places, it is necessary to adapt an organised

approach (Pepper and Spedding 2010).

Development of LM and SS in manufacturing 2.3.1

Lean Manufacturing (LM) is based on the production system that Toyota

developed to cut costs and improve product quality in car manufacturing. It aims to

eliminate non-value-adding steps in the production process (Shadur et al. 1995;

Womack and Jones 2003). Waring and Bishop (2010) recognise five key principles

of LM:

(1) Specify the value of the operational process; (2) identify value

streams (i.e. ‘those processes that will ultimately add value to the process’);

(3) create flow by breaking down barriers and boundaries between

occupations and groups; (4) pull of customers rather than suppliers’ push; and

(5) continuous activity or continuous quality improvement.

However, Six Sigma (SS) uses an experimental cycle, DMAIC, focused on

measurement and statistical control of product and process variability, and

supplements LM approaches by focusing on product quality inefficiencies in

operations (Arnheiter and Maleyeff 2005). Both LM and SS can be used in process


improvement programmes. LM and SS can be seen as a set of tools and practices

(Shah et al. 2008).

Lean and Six Sigma were been integrated in 1986 in the US-based George

group (Souraj et al. 2010). However, the term LSS was initially presented into

literature around 2000 and the popularity and application of LSS is remarkable in the

manufacturing world, particularly in large western organisations such as Motorola,

Honeywell, GE, Du Pont, Merck, Johnson & Johnson, Bank of America (Antony et

al. 2012; Laureani and Antony 2011; Snee 2010), and in some small- and medium-

sized industrialised enterprises (SMEs) (Antony et al. 2005; Kumar et al. 2011).

The implementation of LSS as a set of process improvement tools has been

approached in a variety of ways. It can be argued that without the involvement of

employees with their practical understanding of what is happening in work

processes, it is not possible to achieve the full potential of LSS (Stanton et al. 2014).

Shah et al. (2008) review of practices in manufacturing indicates that the context is

important. For instance, they found a correlation between unionisation and a lack of

implementation of cross-functional teams. Albliwi et al. (2014) systematically

reviewed the literature and found that while remarkable success stories of LSS

deployment in the industrial area can be observed in many academic papers, not all

organisations can achieve real benefits from LSS implementation; a poor attempt at

LSS implementation can actually make it ineffective (Chakravorty 2009; Dinesh

Kumar et al. 2007; Glasgow et al. 2010; Kumar, Antony, et al. 2008; Kumar,

Nowicki, et al. 2008).

Lean Six Sigma (LSS) puts together the advantages derived from the Six

Sigma method with the Lean engineering’s waste-reduction viewpoint which is

highly successful in process-improvement and is generally applied in the chief

performing administrations (Arnheiter and Maleyeff 2005; Spector 2006). Kumar et

al. (2006) have included a few important Lean methods with the Six Sigma structure

for applying at an Indian SME, however “there is no standard framework for Lean

Six Sigma”. Also, Pepper and Spedding (2010) propose a combination method to

provide a theoretic basis. Clearly, this growing concern towards LSS has evidently


resulted in numerous attempts to create an integrated approach for achieving

continuous improvement (Albliwi et al. 2014).

LEAN AND SIX SIGMA IN HEALTHCARE 2.4

Overview 2.4.1

Increasing demands coupled with pressures on healthcare budgets to do more

with less (Medicine 2013; Porter and Lee 2013; Publishing and Oecd 2002) and

evidence of poor performance have directed healthcare organisations to look for

techniques to re-evaluate their operations strategy to improve quality, safety and

value in health service delivery (Ballé and Regnier 2007). However, healthcare

providers work under a selection of quality improvement strategies that insert a

pressure on operations rather than dealing with the issues in the current systems

(Cardin et al. 2003).To achieve a reduction in time and resource utilisation, it is

important to eliminate non-value-adding activities and focus on value-adding

activities in the production process (Mazzocato et al. 2012).

Increasingly, this examination for clarifications has ever more expanded further

than the scopes of healthcare systems to examine techniques and processes that have

been effectively in use in different industries (Sloan et al. 2014). Several practices of

process re-engineering and quality management that offer improved effectiveness

and competence that derived from the manufacturing business have been expanded

on and applied with quantifiable achievement to the service division (Radnor and

Boaden 2008). A 51% of these publications derived of the practices tailored to a

service setting stress on lean, considering the engineering strategy process as

amongst the most perceptible means from which healthcare improvements is reached

(Radnor et al. 2012). Consequently, there is a growing responsiveness of interest in

lean success-stories amongst experts in areas of academic and healthcare sectors and

of lean applications in healthcare fields (Al-Balushi et al. 2014).

In healthcare division, Al-Balushi et al. (2014) have come up with a lean

foreword by presenting an all-inclusive literature review on lean and lean healthcare.

They additionally recognize the ability to allow to a decentralised style of


management, and to carry out an end-to-end process examination as significant for

the successful appliance of effective lean philosophies in healthcare institutions in

conjunction with the well-known factors of reward and measurement systems,

training, communication, organisational culture and leadership that have formerly

been recognized in the broad management narrative as essential for effective change

management (Sloan et al. 2014).

Timmons and Nairn (2015) further research on the execution of lean

methodology in an emergency department examined the role of the businesses and

proficient status in the lean operation success. They established that the status of the

professional plan for doctors in the emergency department brings about more

professional eagerness and engagement than reported more often than not in the lean

implementation study in healthcare, signifying the suitability of proposals such as

lean techniques in healthcare might be greatly affected by the participants’ qualified

status. Moreover, the work of Hayes et al. (2014) focuses on the significance of

shared acknowledgment of the proficiency of participants in a successful lean rapid

improvement event. Whilst elaborating on the methods of an express improvement

situation, their report measures the remarkable benefits that might take place through

believing the participants’ professional know-how to work out their respective issues.

It also validates the lean methods’ applicability between and within pathology and

emergency hospital departments. Together with the measurable progress in process

times, a peaceful working environment was proposed by lean strategy and the

employees accounted for improved satisfaction by being able to implement control

over their work set-up (Hayes et al. 2014).

According to Sloan et al. (2014) the above mentioned researches point towards

the significance of important organizational factors along with the tools and

techniques for understanding and sustaining the lean strategy. Nonetheless, lean is

situation-specific and certain suppositions supporting lean implementation in

manufacturing industries are not applicable in healthcare organizations; and customer

definition and the ensuing conception of customer-value becomes another major

challenging task (Radnor et al. 2012; Sloan et al. 2014).


However, most of the studies reported in the literature seem to focus mainly on

the results of the lean approach without offering a detailed vision of the approach

itself. Yet, a structured and detailed approach is essential for other practitioners to

reproduce the results achieved in previous lean studies. Thus, it is necessary to

propose a framework to study the lean approaches from a methodological

perspective. In addition, in the United Kingdom, it has been recommended to

develop a roadmap for the development of Lean and Six Sigma for the national

health service, along with tools which support the roadmap (Antony and Kumar

2012). There are many studies reporting successful lean interventions; however

studies of lean often lack explicitly stated and appropriate research designs,

appropriate statistical tests, and outcome measures; and little has been reported about

failed attempts or barriers to application (Holden 2011; DelliFraine et al. 2010;

Mazzocato et al. 2010). Clearly, research on lean is still limited.

One way to improve this situation is for the healthcare sector to examine its

processes and to deliver care more efficiently and effectively within available

budgets. The philosophy of continuous improvement and the techniques from the

Lean Six Sigma stable have a role to play in assisting healthcare to deliver high

quality service within its current budget constraints. The main reasons why

healthcare professionals need to apply LSS, as mentioned by (Arthur and Books24x

2011), are:

healthcare delivery often involves complex processes that have evolved over

time and that are neither patient-focused nor clinician-friendly; and

poor turnaround times in emergency department and operating room; delays

in imaging department, laboratories, bed management; late discharge, long

patient lengths of stays, and poor patient outcomes leading to patient

dissatisfaction and physician dissatisfaction.


Patient flow improvement in emergency departments 2.4.2

The main role of the emergency department can be defined as the treatment of

patients who are critically ill or injured (Singh Gill 2012). Because the emergency

department occupies a central place in healthcare organisations, issues in other

components of the system may be relevant to how the ED operates (Yen and

Gorelick 2007). Hence, problems in the ED may strongly affect the public point of

view about the whole health care organisation (Mazzocato et al. 2012). Therefore, it

is clear that the emergency department is constantly under increased pressure to meet

community expectations of quality healthcare services.

Under this increased demand for healthcare services, emergency departments all over

the world are challenged with a variety of problems which affect patient flow.

Overcrowding, treatment delays, reduction in quality and safety of care, and

inefficient use of available resources are considered patient flow obstacles in the ED

(Campbell et al. 2004; Cooke et al. 2004; Lecky et al. 2014; Pines et al. 2011;

Richardson and Mountain 2009). People working in healthcare institutions become

proficient in building “workarounds” (Tucker et al. 2014) but the LST-categorized

eight wastes, i.e. latent talent, defective products, inventory, motion, over-processing,

waiting, transportation and over-production cause individuals and organisations

aggravation, displeasure and pain. Healthcare institutions, in eliminating on the eight

wastes, are modifying their working surroundings in ways which convey welcome,

unconstructive strengthening to their personnel (Sloan et al. 2014). All these kinds of

issues result from the waste that takes place in the ED process during the patient

journey. The key to improving the flow of patients in EDs is the reduction or

elimination of non-value-added activities (such as: length of stay and waiting times)

and the consequent streamlining of the process (Holden 2011).

In fact, hospitals are slow to implement change, their patient flows are

inefficient, and they need to increase patient and employee satisfaction and revenue

while decreasing costs and waiting times (Parr 2010). With respect to the cost of

care, the speed of service, crowding, and patient safety, the need for continuous

improvement processes in emergency departments is now commonly recognised,

since competition between hospitals has increased (Berger 2010; "Hospital-based


emergency care; at the breaking point" 2007; Kellermann 2006; Parr 2010; Smits et

al. 2009). Therefore, improving patient flow is a goal at many hospitals. Although

measurement is crucial to identifying and mitigating variations, measuring the

multidimensional aspects of flow and their impact on quality is difficult. Thus, there

is a need for an improved method to assess patient flow and improve quality by

tracking care processes simultaneously (Fieldston et al. 2014).

Little improvement has been achieved to address the patient flow obstacles that

lead to overcrowded EDs (Wilson and Nguyen 2004). Wilson and Nguyen report that

ten hospitals in the US participated in a yearlong Learning Network to find the best

practice that would solve emergency department issues. Through this study, the

hospitals realised that it is important to address the fact that emergency department

crowding is in fact a hospital-wide problem. In addition, formal improvement

methods need to be utilised to ensure success.

Nugus et al. (2014) state that models and strategies to develop the throughput

of emergency department patients have usually ignored the way individual staff

create patient flow, and the organisational approach to these demands. The creation

of a hospital-wide patient flow team with people from different departments to

manage the modification is necessary to implement patient flow changes that will

eliminate emergency department overcrowding (Wilson and Nguyen 2004).

However, it is still an important function of empirical sociological research to give

evidence that supports or challenges common sense hypotheses in terms of

management and leadership; solutions to ED overcrowding and boarding require

policies and programs to transform culture rather than depending on appeals to

efficiency imperatives alone (Nugus et al. 2014).

Hospitals are slow to implement changes, and, despite many efforts, scientific

knowledge remains limited on which strategies and systematic models can actually

improve patient flow in EDs. It is currently unknown which strategies provide the

best solutions to patient flow in the ED (Eitel et al. 2010). Increasing demand for ED

services impels researchers to look carefully at ED patient loads and crowding, with

further cooperation between emergency medicine researchers and operations


management experts to develop the available methods (Wiler et al. 2011). Thus,

Lean Six Sigma strategy will fill the gap.

Why Lean Six Sigma in ED? 2.4.3

Lean and six sigma have been effectively implemented in healthcare (de

Koning et al. 2006; Fillingham 2007; Kelly et al. 2007). The basic lean concepts are

the elimination of waste through the standardisation of processes, and the

participation of all employees in process improvement (Dickson, Singh, et al. 2009).

In addition, Six Sigma helps to quantify problems; it facilitates evidence-based

decisions (and so keeps time from being wasted on anecdotal evidence); it helps the

company understand variation and reduce it; and it identifies the root causes of

variation in order to find sustainable solutions (Lee-Lewandrowski et al. 2003).

Value and flow are the key concepts of lean thinking (Hogan et al. 2012).

Values in healthcare are the activities that enhance the quality of healthcare and

promote patient well-being so as to achieve better outcome. Therefore, in this

research field, value is specifically seen from the patient's perspective and is created

by eliminating waste (Han et al. 2007). Waste can be identified as any activities that

do not help patients or move them further from cure (Storrow et al. 2008). One of the

wastes in emergency departments is the time spent waiting; for example, the time

spent waiting to be seen or waiting for the next treatment. When waste is removed,

patients flow smoothly and continuously (Holden 2011). Thus, it is important to

understand the time and patient motions during their process in ED. Value Stream

Mapping (VSM) and A3 reports are objective communication tools and techniques

used to develop lean concepts (Asplin et al. 2003; Han et al. 2010; Holroyd et al.

2007; Weintraub et al. 2006). All specialists and staff can review them, which allows

for cross-departmental sharing of process changes and generates even more problem-

solving ideas (Hoot and Aronsky 2008).

Some studies have examined the application of the Lean and Six Sigma

principles in several areas of healthcare, but so far none has reported in-depth

statistical findings about individual projects. Hence, these studies have reported a

low level of demonstrable improvement (Michelen et al. 2006). As a result of lack an

accurate assessment, there is still a need for future work that will improve the


evidence base for understanding more about quality improvement QI approaches and

how to achieve sustainable improvement (Glasgow et al. 2010). Utilising both

methods simultaneously holds the promise of being able to address all types of

process problems, hence Lean Six Sigma. Proudlove et al. (2008) reported that there

is no overall accepted or integrated roadmap for implementing Lean Six Sigma.

However, it is a common practice to integrate lean tools in the six sigma

methodology of Define, Measure, Analyse, Improve and Control (DMAIC) (George

et al. 2011). To improve quality with efficiency and the outcomes of patients, lean

techniques are widely used in EDs (Holden 2011).

However, there is no supported evidence for the efficiency of lean techniques

in EDs (Vermeulen et al. 2014). Like any health care intervention, the

implementation of lean should be evidence based, with reasonable expectations of

benefits, proper evaluation, and an awareness of potential downsides (Gowen Iii et

al. 2008). Although the specific costs of this particular program were not available,

lean initiatives in general are not inexpensive, given the need for external

consultants, data collection tools, and staff time that must be assigned to quality

improvement teams (System 2006). In-patient/out-patient care at hospitals is

considered to be a major access block in healthcare systems (Jones and Filochowski

2006). Consequently, hospital based systems have been the subject of many studies

where efforts have been made to improve their performance (Ben-Tovim et al. 2008;

Dickson, Singh, et al. 2009; Kelly et al. 2007). However, there is a need to develop a

systematic methodology for lean six sigma implementation in healthcare system.

To summarise, the existing literature has not established a true empirical or

theoretical foundation for demonstrating the effectiveness of, or the goal-specific

improvements achieved by, the Lean and Six Sigma methodologies in healthcare

organisations (Cameron et al. 2002; Michelen et al. 2006). In addition, there are very

few research studies that empirically document how these two methods might be

integrated into one approach, or how they might achieve world-class results (Taj and

Morosan 2011). There is no Lean-Six Sigma integrated model available to


investigate and improve patient flow in EDs, and there is no effective evaluation

method to measure the performance efficiency of such a model.

LSS IN MANUFACTURING AND HEALTHCARE: MAJOR 2.5

DIFFERENCES

In a healthcare environment, it is debated that the patient rather than the

healthcare providers should define what creates value (Young et al. 2004). However,

integration of customer voice, including main customer (patient), staff, and process

will create more value. Based on this perspective, the literature provides evidence

that Lean thinking and Six Sigma offer significant improvement opportunities

hospital-wide, including in emergency departments (Dickson, Anguelov, et al. 2009;

Holden 2011; Martens et al. 2014; Ng et al. 2010; Parks et al. 2008). On the other

hand, it is claimed that lean manufacturing does not clearly transform to healthcare

systems, and critically requires a greater understanding and recognition of the

differences between lean manufacturing and healthcare work organisation (Radnor et

al. 2012; Winch and Henderson 2009). Focusing on providers’, institutions’, and

stakeholders’ needs instead of the patients’ needs has been considered in EDs and

most healthcare systems (Dart 2011; Millard 2011).

Generally, lean implementation has focused on categorizing value-adding

processes and eliminating waste from the system (Sloan et al. 2014). The main

difference is that lean thinking is designed to make products with defined

characteristics and quality, but in healthcare organisations and specifically in EDs,

the system deals with human beings (Al-Hakim 2014). The current literature has

critically highlighted the information about the process in ED (Chisholm et al. 2011;

Holden 2011), however the relations between waste in ED and information quality

has not been clearly considered in empirical study (Al-Hakim 2014). Because lean

thinking originated from manufacturing, the research into its application and

sustainability in healthcare is still limited and lacks an integrated research design,

appropriate analysis and outcome measures (Mazzocato et al. 2012).


EVALUATION OF LEAN SIX SIGMA IN HEALTHCARE SYSTEM 2.6

In lean philosophy as applied to manufacturing, measures provide a sense of

direction from the current state to the future state by aligning with business’s long-

term goals (Ramesh and Kodali 2011). The significance of the supporting structure

for successful adoption has increasingly been held up as critical (Hines and

Lethbridge 2008; Radnor 2010). There are grounds to argue that a process is not

becoming “leaner” if it does not display an improvement in important key

performance indicators (Wan and Frank Chen 2008).

Leanness

The tools for assessing leanness can be basically divided into two types,

qualitative and quantitative. Leanness is a tool to gain competitive advantage with

remarkable sustainable competitive results (Womack and Jones), but

misunderstanding for lean performance measurement is a significant reason that lean

practices have failed, thus it is important to measure lean management performance

(Behrouzi and Wong 2011). Some research in the literature (Bayou and Korvin 2008;

Goodson 2002; Singh, Garg and Sharma 2010) address the evaluating of leanness for

management systems and highlight the necessity for a combining measure of the

impacts of these methods.

It is clear that the current literature reviews on lean evaluation techniques have

advantages and limitations. There is no specific judgment which is the best method

to measure the performance of quality (Devlin et al. 2003). The recent lean

evaluation techniques concentrate on a various side of lean processes generally

without a comprehensive qualitative and quantitative assessment approach (Pakdil

and Leonard 2014). Thus, there is a need for further studies to develop qualitative

and quantitative methods concurrently.

Evaluation of effectiveness in healthcare 2.6.1

Lean implementation in healthcare has been increasingly reported in the

literature (Brandao de Souza 2009; Mazzocato et al. 2010; Poksinska 2010; Sobek

and Lang 2010; Winch and Henderson 2009; Young and McClean 2008) leaving,


however, the question of “how much Lean” has been implemented without accurate

answers. This is partly due to some misconception of what can be called a Lean

organisation (Womack and Jones 2003), focusing only in the “hard” side of Lean,

that is, tools and techniques, and not exploring the “soft” side that is behind a true

Lean culture (Badurdeen et al. 2011). On the other hand, the difficulties of lean

deployment assessment and suitable metrics (Neely 2007) in a sector are challenges

to be met in a universal performance evaluation system (Jean-François 2006;

Saltman et al. 2011). Moreover, a lean assessment instrument is never context free

(Radnor and Boaden 2008). Lean projects in healthcare should be SMART—

specific, measurable, action oriented, relevant, and timely (Stamatis 2011). Although

the relationship of lean management to better outcomes has been extensively tested

in other industries, studies of lean management in healthcare have primarily been

case studies or rapid cycle quality improvement efforts and have lacked rigorous

experimental methods (Mazzocato et al. 2010). Some hospitals have labelled

themselves as lean in their public relations campaigns, yet their quality indicators

have not reflected better quality than hospitals that did not make the claim. The Joint

Commission has advocated for lean methods to address safety and quality goals

(Joint Commission Resources, 2006), and the Agency for Healthcare Research and

Quality (AHRQ) has pointed to the need for solid evidence on the effects of lean

applications in health care, to eliminate unnecessary costs while improving quality

(Agency for Healthcare Research and Quality, 2009; Helfand & Balshem, 2009).

A systematic review of the literature by DelliFraine et al. (2010) looked at lean

management’s research to determine whether the research supported Lean/Six Sigma

as evidence-based management. Of the 34 studies included in the review, only 11

conducted any statistical analysis to test for significant improvements. The authors

concluded that there is little solid evidence of the effectiveness of Lean/Six Sigma,

and that current trends in evidence-based management are based on conceptual

arguments rather than empirical research. Measuring outcomes associated with lean

programs would help to provide solid evidence of effectiveness. There is literature on

the many stages and degrees of lean management in healthcare organisations or

nursing units, but little research on patient flow in emergency departments.

Furthermore, Qianmei and Manuel (2008) state that their survey of health-care


companies showed that 54 per cent of the surviving companies do not anticipate

implementing the Six Sigma strategy. Also, 62 per cent of Six Sigma and lean

initiatives failed as a result of lack of stakeholder acceptance (Glasgow et al. 2010).

To determine whether lean management improves the quality, safety, cost and

delivery of care, one must quantify the degree of lean penetration. However, there is

currently no instrument to measure the level of lean management presence, so it is

difficult to show the relationship between lean management and improvement in

healthcare outcomes. Using this measure of “leanness,” future studies can look at

outcomes such as quality, safety, cost and delivery of care and their relationships to

the amount of lean in a hospital or other healthcare organisations (Lagoe et al. 2003).

While there are academically sound measures of lean in other industries, there

is no measure in healthcare that targets lean improvements (leanness) in hospitals or

nursing units, the point of customer contact. Now, we can see while there is evidence

that supports our research focus on the need for Lean Six Sigma integration,

particularly for patient flow improvement in emergency departments, we lack an

appropriate, described methodology for this integrated approach to investigation of

the waste that affects patient flow in EDs. Therefore, there is a clear need for

evaluation of how this integration can effectively and continuously improve patient

flow, and more generally, the quality of healthcare services.

LIMITATIONS AND RESEARCH GAP 2.7

This research study considers the flow of patients through the emergency

department system as an important factor that affects and is a determinant of the

performance of healthcare delivery processes in a hospital system. Therefore, patient

flow must be considered in the investigation and improvement of emergency

department obstacles. The reviewed literature shows that Lean and Six Sigma have

been an active research area for many years. However, the absence of a systematic

integration framework including Lean thinking and Six Sigma methodology for

patient flow improvement in emergency departments still represents a big challenge.

The existing literature has not adequately established a true empirical or theoretical


foundation for effective and goal-specific improvements in patient flow problems by

applying Lean and Six sigma methodologies in healthcare organisations (DelliFraine

et al. 2010; Langabeer et al. 2009). Few researchers have empirically documented

how these two techniques can be integrated into one strategy, and how these

techniques can be leveraged to achieve world-class results (Antony 2011). No

research study discusses this integration to solve the problem of patient flow in

emergency departments. In addition, there are no specific national initiatives which

would encourage a search for an innovative approach to the reduction of crowding

improvement of patient flow in emergency departments in Saudi Arabia.

Furthermore, studies of the implementation of Lean and Six Sigma in several areas

of healthcare do not report in-depth statistical findings about individual projects, and

hence there is a paucity of evidence of improvements (DelliFraine et al. 2010).

Therefore, a better understanding of the effectiveness of Lean Six Sigma integration

is necessary and should be supported by a clear measurable evaluation method.

Thus, there is need to develop a framework that can be used to integrate the

Lean and Six Sigma techniques into one strategy for patient flow improvement in

emergency departments (Shahada and Alsyouf 2012).There are continual pressures

on healthcare costs coupled with growing needs on healthcare services, linked with

limited resources and evidence of poor performance which effect national and local

healthcare organisations (Medicine 2013; Porter and Lee 2013; Publishing and Oecd

2002). Public demand for increased quality coupled with the increased need to do

more with less has led healthcare organisation management teams to re-evaluate their

operations strategy (Ballé and Regnier 2007). Healthcare settings such as hospitals

these days operate under a barrage of improvement programs as a result, often

adding to the pressure of operations rather than dealing with problems in the existing

systems (Fillingham 2007). It is clear that healthcare organisations are complicated

dynamic systems that focus further on rising quality of care and meeting strict rules

(Ahmed et al. 2013). Therefore, the requirement for development in health care is

more obvious these days, and this demanding has directed healthcare organisations to

look for techniques to develop safety, value and quality in health service delivery

(Holden 2011; Sloan et al. 2014).


RESEARCH GAP ANALYSIS 2.8

The review of current literature conducted in this chapter has identified a

number of research gaps. Following are some of the fundamental issues emerging

from the literature review.

Only a limited number of studies have empirically documented how Lean

and Six Sigma techniques can be integrated into one strategy and how

these techniques can be leveraged to achieve world-class results (mainly in

manufacturing).

No research discusses this integration to solve the problem of patient flow

in emergency departments. The absence of a systematic integrated

framework, including the voice of process, voice of patients and the voice

of ED employees for patient flow improvement in EDs, presents a major

challenge.

Each concept and theory of Lean Six Sigma techniques is considered

separately, or does not combine these theories and concepts in a

comprehensive fashion in a single investigation or case study.

There is a lack of specific national initiatives to reduce crowding and

improve patient flow in emergency departments in Saudi Arabia.

While there are academically sound measures of lean in other industries,

there is no measure in healthcare that targets lean improvements (leanness)

in hospitals, particularly for patient flow in emergency departments.

The absence of identified quantitative and qualitative performance metrics

strongly suggests the need for a framework of performance metrics for

evaluation, in order to ascertain how this integration can effectively and

continuously improve patient flow and general healthcare services’

quality.

Methods to transform both qualitative and quantitative metrics into a

quantitative assessment, and measure leanness performance for patient


flow using Fuzzy membership functions, are limited in the existing

literature.

Chapter 3: Research Methodology 41

Research Methodology Chapter 3:

The previous two chapters discussed the underlying theoretical structure of this

study. This chapter discusses the research design and methodology. It presents the

appropriate methodology to investigate patient flow problems in emergency

departments by using the Lean strategy with the DMAIC approach of Six Sigma

methodology within the healthcare system.

An integrated framework for investigation of patient flow problems in

emergency departments is a relatively new topic, with limited data available on

applying integration between methods in the healthcare setting; therefore, an

induction methodology has been selected as an appropriate approach. Mixed methods

in an action research approach using a single case study are the chosen strategy to

achieve the research objectives. Finally, several techniques for qualitative and

quantitative data collection are employed in a concurrent triangulation design. These

tools include: personal observation, a questionnaire survey, semi-structured

interviews and a time and motion study for the primary data, and multiple source

data collection for the secondary data. A detailed discussion of these steps is

presented in the following sections.

INTRODUCTION 3.1

Irani et al. (1999) empasise the importance of having appropriate research

methodology based on the research problem in hand, related to either natural science

or social sciences, both with their corresponding features. However, in deciding how

to conduct research or to select its methods, there is no definitive rule regarding how

and what approach one should select for research, as it all depends on the nature and

scope of research, the sources of data, and the research questions and objectives (Bell

2010; Robson 2002). The research ‘onion’ model is an appropriate generic approach

to depict issues underlying the design of the research process in this study (Saunders

et al. 2012). AS shown in Figure 3.1, the layers of the research process are made up

of

42 Chapter 3: Research Methodology

o Research philosophy.

o Research approach.

o Research methodological choice.

o Research strategies.

o Research time horizon.

o Research techniques and procedures.

The highlighted red boxes in the diagram are the research elements that are

used to answer the research questions and meet the set of study objectives, which are

presented in Chapter 1.

Figure 3.1: The research ‘Onion’, (Saunders et al. 2012)


RESEARCH PHIOSOPHY 3.2

The identification of research philosophy is important, as it indicates the

opinions and attitudes which frame the manner in which knowledge is gathered,

constructed and analysed. Different research philosophies are widely addressed in the

literature, and therefore they are used for informing and guiding the investigative

nature of this research. As shown in Figure 3.1, positivism, realism, interpretivism

and pragmatism are the four main research philosophies in the literature.

To achieve the objective of this research, a mixture of these philosophies is

used. The main philosophy of this research can be identified as pragmatism, since

positivism and interpretivism are integrated to satisfy all the research objectives

simultaneously. Rossman and Wilson (1985) state that knowledge in pragmatism is

derived from actions, situations, and consequences rather than antecedent experience

or beliefs. It is essential to identify the most appropriate research method and data

collection techniques in a suitable research design to satisfy these research

objectives.

RESERCH APPROACH 3.3

Various academics have discussed the appropriate way to conduct research

according to the scientific approach taken by researchers throughout the literature.

There are three major research approaches to theory development: deduction,

induction and abduction. This study adopts a pragmatic philosophy, as mentioned

above, where positivism and interpretivism are integrated. Therefore, two main

approaches (deductive and inductive) are justified in detail due to their relationship

with pragmatism. The deduction approach is theory testing, and the inductive

approach is theory building; the deductive approach is usually based on positivism

and the inductive approach is usually based on interpretivism (Saunders et al. 2009).

In the inductive approach, there is no formal hypothesis, and the purpose of the study

may be to explore some area more thoroughly in order to develop some specific

hypothesis or prediction that can be tested in future research.

Inductive reasoning moves from specific observations to broader

generalisations and theories. In inductive reasoning, we begin with specific

observations and measures, begin to detect patterns and regularities, formulate some


tentative hypotheses that we can explore, and finally end up developing some general

conclusions or theories.

In this research study, there are no hypotheses, and “not all studies have

hypotheses, for sometimes a study is designed to be exploratory” (Trochim et al.

2015). As this research investigated patient flow issues in an emergency department

by using an integrated framework, the inductive approach has been considered

appropriate. An inductive research project is one that constructs a theory from a

number of observations made in a limited empirical study in the chosen area of

research (Yin 2009).

RESEARCH DESIGN 3.4

It is very important to have an appropriate research design, because the

research design determines the data type, the collection of data, the sample

methodology, the schedule, and the budget (Hair et al. 2003). The research design

basically facilitates the alignment of the planned methodology to the research

problems (Churchill and Iacobucci 2005). The research design is the set of guidelines

that form the connection between theory and research, ontological and

epistemological considerations, qualitative and quantitative research (Bryman and

Bell 2003). In case of healthcare situation where observation, participant observation

and secondary data analysis are part of collection information, the mixed method

research is the appropriate research design for this kind of study (Hicks et al. 2015).

Also, mixed method with single case study has been used with lean to solve the

problem of flow in emergency department at the Astrid Lindgren Children’s hospital

in Sweden (Mazzocato et al. 2012). The following sections present the terms of the

chosen methodology, research strategy, time horizon, research techniques and

procedures. An exploratory qualitative single case study research design was chosen

to conduct this study.

Choice of Research Methodology 3.4.1

Mixed method research with a concurrent triangulation strategy is adopted for

this study. This is also known as convergent parallel design, when quantitative and

qualitative data collection and analysis strands during the same phase of the research

process prioritise the methods equally, and keep the strands independent during

analysis, and then mix the results during the overall interpretation (Creswell and


Plano Clark 2011). This allows both sets of results to be interpreted to provide a

richer and more comprehensive response to the research questions, in comparison to

the use of a mono-method design. This is referred as a concurrent triangulation

design, as shown in Figure 3.2.

Figure 3.2: Concurrent triangulation Strategy, (Terrell 2012)

Research Strategies 3.4.2

This study was conducted under mixed methods research strategies, with

integration of action research in a single case study, as highlighted in Figure 3.1.The

study aims to address the main research question:

How can the patient flow in emergency departments and quality of

services be continuously improved with the integration of Lean Six Sigma

methodology?

To answer this question, the following sub-questions are will be addressed:

1. How can Lean thinking and Six Sigma methodology be integrated in

one combined approach for continuous quality improvement in patient

flow in emergency departments?

2. How can the root causes of overcrowding that impact patient flow in

emergency departments be determined by integrating voice of

customer and voice of process?


3. How can wastes affecting patient flow and quality of service in EDs

be identified by using Lean strategy and Six Sigma techniques?

4. How can patient flow improvement in EDs be developed based on

Lean Six Sigma adoption?

5. How can the effectiveness of integrating Lean Six Sigma in patient

flow be evaluated for continuous process improvement?

The appropriate research strategy was selected carefully to solve the research

questions by investigating the current existing knowledge and the associated

questions and objectives (Yin 2009). The aim was to examine and solve real world

problems in emergency departments, using an exploratory qualitative case study, and

employing the action approach of Lean Six Sigma strategy for patient flow

improvement in emergency departments. However, it is possible to integrate or use

more than one strategy, and it can be part of another strategy and should not be

thought of as being mutually exclusive (Saunders et al. 2009).

Case study

Case study designs are typically well suited for research questions focused on

“why” and “how” rather than on “what”, “how many” or “how often” (Dunn 2003).

The researcher can, within the case study research method, examine the processes,

events, persons, or things of interest to better understand a phenomenon in its real

context (Gall et al. 2003). Case study analysis is flexible and unobtrusive because it

allows data to be collected from the organisation as well as from the public domain

(Diesburg-Stanwood et al. 2004). The data accumulated allows the investigator "to

identify the schemes that were used or that could have been used to figure out

problems experienced in that particular situation" (Frey 1991). Furthermore, case

studies that analyse multiple aspects of a phenomenon within a single organisation

can furnish a deeper understanding of a theoretical phenomenon in an actual

application and thereby contribute to the generalisation of the findings in similar

settings (Yin 2014).

Typically, documentation, archival records, interviews, direct observation,

participant observations, and physical artefacts are six possible evidentiary sources

used in data collection for case studies (Diesburg-Stanwood et al. 2004). Evidentiary

sources used in this research study include: archival records, documentation, direct


and participant observations. A case study also can use a questionnaire to collect the

customer perspective in the study of lean in healthcare sittings (de Carvalho et al.

2014). For further study, the phenomenon under investigation can generate data that

can be represented in a quantitative fashion. Voice of the customer was collected in

this study by using a questionnaire survey to understand customers’ experiences and

requirements for improvement of the current system of the emergency department.

Action research

The researcher has chosen an action research process to develop and refine the

DMAIC Six Sigma methodologies within a lean strategy during the investigation of

patient flow problems in emergency departments for continuous quality

improvement. Coughlan and Coghlan (2002) state that action research has been

acknowledged as a valid methodology for research in operation management, with

better results than traditional research methods (Westbrook 1995). Participatory

action research, community-based study, co-operative enquiry, action science and

action learning are all terms describing the action approach which is commonly used

for improving conditions and practices in a range of healthcare environments

(Lingard et al. 2008; Whitehead et al. 2003). Extensive collaboration between

researchers and partners in action research must extend across each stage of research,

from identifying the problem to disseminating the results (Lingard et al. 2008). Thus,

it is clear that collaborations between practitioners and the researcher to address

problem situations produce better insights (Alaraki 2014; Al-Qahtani et al. 2012;

Amaral and Costa 2014; Kuo et al. 2013; Zandi 2013).

Lean Six Sigma Methodologies and the case study

Total quality management, business process reengineering, benchmarking, just-

in-time production scheduling and lean thinking have been the main contributors to

improvements in the manufacturing sector over recent decades (Rosmulder 2011).

More recently, these concepts have started to appear in the healthcare sector (Antony

and Kumar 2012; Bhat et al. 2014).

This research integrates Lean thinking with Six Sigma Methodology for patient

flow investigation and improvement in the emergency department in a Saudi Arabian

hospital. Thus, for Lean Six Sigma adoption in a real life context, which is new to the

healthcare system in Saudi Arabia, the case study methodology is appropriate

because the boundaries between phenomenon and context are not clearly evident


(Yin 2003). In addition, this methodology will be more powerful than other methods

to address a complex circumstance such as patient flow in the ED within the

complexity of Lean Six Sigma (Keen and Packwood 1995).

Research Techniques and Procedures 3.4.3

This research focuses on solving real world problems, with particular attention

to Lean Six Sigma strategy adoptions to improve the patient flow in the emergency

department in a Saudi Arabian hospital, and evaluation of the outcome. The research

most closely aligns with action research, including components of a case study for

using Lean Six Sigma strategy. An Integrated Lean Six Sigma approach has been

used as a road map, with procedures for data gathering, analysing, and improving

processes. As discussed early in section 3.4.1, this study uses a mixed method

research design, known as convergent design with concurrent triangulation strategy.

It involves the use of both quantitative and qualitative methods within a single phase

of data collection and analysis (a single phase research design). Different sources

have been used during data collection, such as: direct and participant observation,

patient questionnaires, unstructured interviews and conversations with emergency

department staff and quality department staff, recorded in note form.

The following flowchart in Figure 3.3 shows the basic procedures in

implementing this convergent design.


Figure 3.3: Flowchart of the basic procedures in implementation of a

convergent design, (Vilke et al. 2004)

DATA COLLECTION METHODS 3.5

It is essential to identify the methods in any research study during data

collection. Yin (1994) states that case studies contain several data collection

methods, such as questionnaires, interviews, text analysis and direct observation.

There are six different sources of evidence for data collection in case studies,

including: documents, archival records, interviews, direct observation, participant

observation and physical artefacts (Yin 2003). The triangulation method will be used

to validate and enhance the credibility of this research. This is done by using multiple

methods of collecting and analysing the data (Hastings 2010).

As a result of using some concepts from Lean strategy and Six Sigma

methodology, voice of customer (VOC) and voice of process (VOP) have been

collected in this research study. With quantitative and qualitative data sources,

appropriate methods and tools were selected in this study. A questionnaire survey

was used to collect the voice of patient (main customer) in the emergency

department. At the same time, archival records and documentation using A3 problem


solving sheets were used to collect the voice of ED staff (internal customer). To

understand the voice of the process in the emergency department, with the

cooperation of ED staff, direct and participant observation was conducted for process

mapping, with a plan for developing a future value stream map (VSM) during a

research field study in emergency department.

Therefore, this research study combined qualitative and quantitative methods in

the data collection process. A mixed method data collection approach consistent with

a single qualitative case study research design included: primary sources

(questionnaire, un-structured interview with conversation, and note-taking using A3

problem solving sheets and direct and participant observation) and secondary sources

(literature review, statistical report and documentation). The data collection sources

for this single case study are, in summary:

1. 1. Survey questionnaire to collect the voice of patient.

2. Archival records and documentation from a previous project.

3. Focus group with un-structured interviews and conversations with note

taking, and A3 problem solving sheets to collect the voice of staff.

4. Direct observation to collect the voice of process.

Survey Questionnaire 3.5.1

To determine the distribution of characteristics, attitudes or beliefs within a

sample population (Fink 2009), questionnaires are often used in a qualitative case

study (Marshall and Rossman 2006). The need to know why, who and how is the

motivation for surveys (McDaniel and Gates 2007).

Questionnaire and voice of the customer

As a part of Define/Identify Value (Define Phase), the voice of the main

customer (VOC) is a basic concept of Lean and Six Sigma tools which are used to

collect data. The voice of the external customer (patient) was collected through a

survey questionnaire regarding quality and patient satisfaction in emergency

department. A survey by questionnaire often aims to describe studies, confirm

studies or to explore and measure studies in order to achieve statistical validity (De

Vaus 2002; Filippini 1997). The present study is an exploratory/descriptive research

into a certain phenomenon; therefore the questionnaire was used to understand


participants’ perspectives and experience with the current system in an emergency

department.

Questionnaire design

The survey instrument was designed and developed based on findings from the

literature review, research experience with ED in Saudi Arabia, and other academic

studies. It was developed based on closed-ended questions for quantitative

measurement in descriptive analysis. This questionnaire dealt with emergency

department patients in one hospital. It aimed to get the voice of patients and

understand their needs in term of quality and satisfaction with current ED system.

The aim of our research is to investigate the current ED system from patients’

perspectives by determining their reasons for visiting the ED, by understanding their

needs and experience with quality and their satisfaction levels, and by categorising

the major problems in this emergency department.

A questionnaire in the form of a drop and pick approach tends to have a higher

response rate than other approaches such as a postal survey, especially for patients

who visit emergency departments. We conducted fieldwork visits to the emergency

department in Saudi Arabia between October 2012 and March 2013. As our objective

is to collect the voice of the patient, one principle of lean strategy as defined in the

Defined Phase, the questionnaire was developed and organised from two different

research studies, then collected in one form comprising five sections (see Appendix

G).

In the questionnaire, the first section covered the demographic information,

which included gender and education level, to be used in the descriptive analysis.

To understand patients’ reasons for visiting and attending EDs, and to identify

reasons and activities that are non-value-added and therefore a form of waste, some

literature and academic study reviews were conducted to develop the second section.

Based on Masso et al. (2007) study, ten reasons for visiting the emergency

department (ED) were identified and used in our questionnaire to get the voice of the

patient. These reasons have been investigated in the literature and have become

common themes, such as: availability of ED services, severity of the problem,

convenience, and better care provision by the hospital (Andersen and Gaudry 1984;

Boushy and Dubinsky 1999; Gill and Riley 1996; Rajpar et al. 2000; Rieffe et al.


1999; Sempere-Selva et al. 2001; Thomson et al. 1995; Walsh 1995). This part of the

questionnaire in our research study uses a 5 Likert scale, which indicates degree of

importance from 1-5, where 1 indicates Unimportant, 2 — Somewhat important, 3 —

Moderately important, 4 — Important and 5 — Very important).

Damanhouri (2002) research study designed a questionnaire to measure patient

satisfaction in terms of two main themes. The first theme explored the quality

concepts and the second one looked at all health services provided to the inpatients,

including internal and external environment, system and work procedures, medical

services, medical support services and other services considered as effective in

quality and total quality management. Three sections of our questionnaire were

developed and conducted to identify the patients’ needs and understand their

perception of the concept of quality in emergency departments, and determine their

satisfaction level while also assessing their agreement with some of the selected

problems. Damanhouri’s questionnaire collected the voice of the patient in the

healthcare system generally in Saudi Arabia, and supported our enquiry into some of

these concepts in the emergency department.

Our questionnaire selected error-free treatment and diagnosis, minimisation of

unnecessary tests and diagnosis, and short waiting times throughout the process of

treatment in ED to collect the voice of patients regarding the concept of quality.

These new concepts categorised Lean and Six Sigma principles as quality concepts.

There were some changes in section four of our questionnaire, compared to

Damanhouri (2002) questionnaire. This section in our questionnaire was changed to

determine the degree of patient satisfaction with the internal and external

environment, system and work procedures, medical services and medical support

services in the emergency department specifically, instead of determining the quality

level across the hospital, as in Damanhouri’s questionnaire. As a result of this

change, the scale also has been changed to be 1, representing very unsatisfied, to 5,

representing very satisfied. This change was made to understand the patient

satisfaction level with the current situation in the emergency department and identify

any activities that cause non-value or are categorised as waste and lead to patients’

un-satisfaction. Finally, the last section of our questionnaire explored patients’

agreement with selected problems in the emergency department, based on the

literature review, Damanhuori’s study, and research knowledge about current EDs, in


order to collect the voice of patients based on their experience with their current ED.

Therefore, data in questionnaire can be categorized into: customer (patients)

satisfaction measures, lean thinking principles measures and ED issues measures.

Archival Records and Documentation 3.5.2

Archival records are an important source of information that scholars in the

social sciences and humanities may rely on for case study research. When used

systematically together with information drawn from other independent sources,

archival records can shed light on the past and its relationship with current events,

and can help us to understand the activity, aspirations, and goals of individuals and

groups, and the organisational structure, mission, and objectives of associations,

organisations, and state institutions. There are many examples of archival records

such as: organisation records, maps and charts of the geographical characteristics of a

place, and survey data from previous studies about employees or participants (Yin

2009). In addition, documents play an explicit role in any data collection in doing

case studies. Documentation can take many forms, such as formal studies or

evaluation of the same case that the researcher is studying (Yin 2009). Based on an

unpublished previous project which was piloted by the researcher in the same

emergency department in Saudi Arabia at the end of 2009, a questionnaire survey for

ED staff and semi-structured interview with the quality department of the ED was

conducted for this study. The previous project (preliminary study) aimed to collect

the voice of staff and understand current emergency department problems based on

their experience.

The present study draws on the previous study (researcher preliminary work) to

support our findings, while it gathers more evidence in terms of ED staff experience,

ED staff satisfaction with bed capacity and medical equipment, and investigates

causes of waiting time from a staff perspective. Therefore, data in this phase can be

categorized into: customer (ED staff) satisfaction measures, cause of waiting times

measure and ED staff experience measures.

Un-structured interview with focus group 3.5.3

A central aim of this study is to investigate quality improvement through the

involvement of stakeholders during the project planning phase, as well as the project

participants’ knowledge, experience and perspectives on the issue. To obtain such


information, an interview seems to be very beneficial since it allows the researcher to

interact with the interview population and provides insights about their behaviour,

views, approaches and feelings (Patton and Patton 2002). Yin (2009) also stresses

that interviews are key sources of case study data.

To collect the voice of emergency staff, it was really important to use another

method beside the closed-ended questionnaire that has been used in the previous

project, to add more evidence and validate the staff perspective. Therefore, A3

Problem solving sheets were used to collect the voice of internal customers (ED

staff). With structured questions and unstructured interview procedures,

conversations with ED staff recorded in note form have been integrated. These

conversations took place in the emergency department with specific groups of

available and interested staff between October 2012 and March 2013, in Asir Central

Hospital. More details about the A3 problem solving sheets are in Chapter 4.

Direct observation 3.5.4

Such observation to understand the current situation serves as another source of

evidence in a case study and contributes to qualitative research having the advantage

of directness and being more powerful and meaningful (Yin 2009). Direct

observation was conducted during the file work study by the research between

October 2012 and March 2013. The researcher used direct observation to collect the

voice of process and get a clear picture of the current process in the emergency

department. Therefore, information about the activity and emergency department

layout was developed using process mapping, flow chart and value stream mapping

(VSM) to identify the voice of the process visually; more details about voice of the

process are presented in Chapter 4. It is clear that data in this section can be

categorised as a process mapping to identify activities, movement and times in ED.

Data collection procedures 3.5.5

The case study is a method that lends itself to examining new phenomena using

quantitative and qualitative data to find an answer to a research problem, especially

when a ‘how’ or ‘why’ question is being posed (Maxwell 1998; Yin 1981; Yin

2003). As mentioned above, a comprehensive case study was conducted at Asir

Central Hospital in Saudi Arabia. Case study research is necessary to examine issues

in their real-world contexts, and this is particularly important when the boundaries


between the phenomenon and context are not clearly evident (Alaraki 2014).

Additionally, the study took an action research approach, with the aim of solving

real-world problems, because improving patient flow in the ED is not only a

technical problem but essentially a social interaction problem between different

groups of stakeholders (Prasanna and Vinodh 2013). In action research, practitioners

and the researcher collaborate to address problem situations and simultaneously

produce better insights (Alaraki 2014; Al-Qahtani et al. 2012; Amaral and Costa

2014; Kuo et al. 2013; Zandi 2013).

To address the patient flow problem in a comprehensive manner, this study

used the qualitative method of archival records, documentation, direct and participant

observation to gather data on the current process, and the quantitative method of

questionnaires to collect data on the voice of the customer. During the observation,

process mapping and VSM was used to gain the voice of the process, and the A3

problem solving sheet and a questionnaire survey to incorporate the ED customers’

voice (patients and staff). Figure 3.4 shows how different data collection methods

were used and integrated to identify the root causes of ED patient flow problem. The

ultimate objective was to redesign the process with a new process map that either

minimises or completely eliminates the identified waste.


Figure 3.4: Research methodology

DATA ANALYSIS METHODS 3.6

This research study uses SPSS for statistical analysis of quantitative data, and

flowchart, process mapping, and cause and effect diagram for the qualitative data.

According to Creswell and Creswell (2005), triangulation is often used to indicate

that more than two methods are used in a study, with a view to double checking

results. Thus, triangulation through mixed methods facilitates validation of data from

more than two sources, through cross-verification. Such an approach enables the

application and combination of several research methodologies in the study of the

same phenomenon (Onwuegbuzie and Leech 2006). Triangulation was used in this

study to enable the combining of multiple theoretical models and empirical materials,

in order to overcome intrinsic biases and the problems inherent in single method,

single observer or single theory studies. Thus, mixed method research with a

concurrent triangulation strategy is adopted for this study. Several techniques were

Merge the two sets of results, interpret and compare the results


used for analysis of the data gathered. This section describes the procedures used to

analyse the quantitative and qualitative data respectively.

Population and Sample 3.6.1

It is clear that this questionnaire targets the voice of the main customer (VOC),

who is the patient visiting emergency department in a specific period during the

research field work study, in order to understand their needs and thereby, ultimately,

to increase their satisfaction. In addition, the voice of emergency staff, including

physicians, nurses, quality department and administrative staff was collected from

multiple data sources, as discussed previously and shown in figure 3.4. It was

important to choose the best sampling strategy for this research study. Sampling is a

process of choosing the most suitable representation of the population to form a

generalisation about the whole population (Goulding 2005). Creswell (2009)

suggests sampling those who can provide various perspectives of the problem under

investigation. Because the goal was to understand a complex, multi-voiced

experience, the sampling could not be random. Purposeful sampling is recommended

for qualitative case study research, which involves choosing subjects who are

exemplars of the phenomenon and possess the most relevant information to help the

researcher learn a substantial amount about the topic and answer the research

questions, in order to perform an intensive analysis (Creswell 2007; Miles and

Huberman 1994; Patton and Patton 2002; Polkinghorne 2005). Accordingly, this

study employed purposeful sampling to recruit participants. A purposeful approach is

well-suited to small-scale and in-depth studies (Ritchie and Lewis 2003).

The Asir region (population 1,640,632 in 2011) is located in the southwest of

Saudi Arabia and has an area of more than 80,000 km2 (Health 2012). Health

services delivery in the Asir region is provided by a network of 227 PHCs, 16

referral hospitals and one tertiary hospital, Asir Central Hospital. Asir Central

Hospital, with 500 beds, is run by the Ministry of Health and the College of

Medicine of King Khalid University, Abha. The workforce in MOH health centres in

the Asir Region includes 680 physicians, 1,379 nurses, 14 pharmacists and 701 allied

health personnel (Health 2012).


Quantitative analysis 3.6.2

The voice of the customer was gathered using a questionnaire survey among

patients in the ED of Asir Central Hospital. The first section of the questionnaire was

about the background of participants, and the others related to the patient’s reasons

for attending ED, their perception of quality concepts, their satisfaction with the

current healthcare system and what they saw as the main problems. The

questionnaire was distributed to 350 ED patients between October 2012 and March

2013. In total, 120 patients responded, a response rate of 34.29%. Data were then

transferred to a database and the aggregate data were analysed. Statistical analysis

was primarily descriptive, and the SPSS statistical package was used. The voice of

the patient, which was collected via the questionnaire, was analysed using SPSS 21,

following two main steps. The data was prepared and organised for analysis and then

processed. The SPSS data entry process started by coding the data numerically,

based on the 5 Likert scale used in our questionnaire. Many researchers have used

different scales for measurement of patient satisfaction and Total Quality

Management (TQM) elements. 4- or 5-point scales have been used widely (Saunders

et al. 2009), and are an ideal tool for calculating attitudes (Frankfort-Nachmias and

Nachmias 2000). The Likert scale was used to measure patients’ attitudes and

perceptions towards care and services.

Descriptive analysis

Descriptive statistics are appropriate to describe and compare variables from a

numerical perspective (Saunders et al. 2009). Descriptive statistical analysis,

particularly the measurement of central tendencies (mean, median), and the

measurement of variations (standard deviation) was undertaken. In our research

study, it was initially important to find the most important variable based on patient

perspectives, using frequencies and mean scores. However, in order to strengthen the

examination, a second round of analysis was commenced. In the second category of

statistical analysis, exploratory factor analysis (EFA) was conducted to examine the

extent to which these items cluster to form scales.

Exploratory factor analysis (EFA)

The procedure here was to conduct one or more exploratory factor analyses

(EFAs) with principal component extraction and (where appropriate) Varimax

rotation. In each case, two analyses are performed. The former considers the single


factor solution, the latter the multi-factorial solution for each of the sub-sections in

turn.

SPSS dimension reduction includes two tests of sampling adequacy. One of

these, the KMO, is somewhat equivalent to Cronbach’s Alpha. That is, values of

.800-1.00 are desirable and scores above .500 are consistent with these items being

factorable (Field 2005; Kaiser 1974). The other, Bartlett’s test of sphericity, reports

on the extent to which item correlations are 1, 0, rather than a broader range of

values (Pallant 2011). Here, a significant p value indicates that items are correlated at

a broader range of values (p<.001) appropriate for factor analysis (Dziuban and

Shirkey 1974).

Inferential statistics

To make judgments about a population on the basis of sample, inferential

statistics are used (Zikmund et al. 2012). Given that this survey includes two

demographic variables (gender, educational level), it makes some sense to enter these

into MANOVAs and ANOVAs as independent variables, with successive sets of

scale scores (multi-factor or single-factor) entered as the dependent variables. Also,

given that both single-factor and multi-factor solutions are available in a number of

cases, the single-factor solution is to be preferred, based on its use of the greatest

number of items.

Structural equation modelling

According to Ringle et al. (2012), SEM can be used to confirm and explore

proposed models which contain multiple dependent variables as well as the existence

of latent variables. In our study, the question here was the extent to which two

exogenous variables (reasons for attending the ED and concepts of quality) directly

or indirectly influenced four outcome measures of satisfaction with the ED health

services (environment, systems and work procedures, medical services, medical

support services). Here one might expect the level of satisfaction to be mediated or

moderated by three measures of problematic experiences with the ED (waiting time

problems, medical service problems, and staff care problems). Therefore SEM was

used to explore the relationships.


Qualitative analysis 3.6.3

As a result of different sources of data used in our research study to collect

qualitative data, descriptive analysis, thematic analysis with A3 problem solving

sheets, process mapping, and a cause and effect diagram were used in the process of

data interpretation.

Descriptive and thematic Analysis

The voice of staff was collected in the questionnaire and quantitatively

analysed using descriptive analysis in SPSS. Because the qualitative research

requires understanding and collecting diverse aspects and data, thematic analysis was

selected as a qualitative analysis tool. As mentioned by Marks and Yardley (2004),

thematic analysis gives a chance to understand the potential of any issue more

widely. Therefore, there is a possibility of linking the various concepts and opinions

of the learners and comparing these with the data that has been gathered in different

situations at different times during the project. In our research, we used A3 problem

solving sheets to collect the voice of staff, with integration of A3 sheets as themes

and patterns in thematic analysis.

An A3 problem-solving sheet was used to identify the patient flow problem in

the ED from the staff perspective, based on their working experience. A3 reports are

clear and objective communication tools designed for all specialists, and can be

reviewed by staff. This allows for cross-departmental sharing of process changes and

generates even more problem-solving ideas (Hoot and Aronsky 2008). Also, we used

visual analysis based on process mapping, a value stream map and a cause and effect

diagram used for more validity in our research findings.

RELAIABILITY AND VALIDITIY 3.7

According to Newman and Benz (1998), the research questions should dictate

the selection of research methods. Thus, researchers are encouraged to use the

methods most suitable to their research. By using a mixed methodology design, one

can strengthen the validity and reliability of research results if a single phenomenon

is under investigation (Frechtling and Sharp 1997); thus the quality of the research

study is increased. Also, using mixed methods allows researchers to benefit from the

strength of each type of data collection and minimise the weaknesses of any single

approach. Frechtling and Sharp (1997) also suggest that combining quantitative and


qualitative methods sharpens the understanding of research findings. Furthermore,

using a mixed method design may encourage people who have a stake in the research

to accept its results and conclusions (Frechtling and Sharp 1997). Therefore, mixed

methods designs have increasingly attracted the attention of researchers (Johnson et

al. 2007). The researcher employed a combined methodology to answer the current

research study’s questions. This mixed design helped the researcher to collect

stronger evidence and to gain multiple perspectives on the issues under investigation

(Dunn 2003).

However, the mixed method design has its drawbacks, as explained by

Creswell (1994), the mixed method design needs the researcher’s extensive time and

effort to use each method accurately, and he or she must be familiar with both

quantitative and qualitative methods. Creswell also states that the reporting

requirements may make research reports inappropriately lengthy for most journals.

However, qualitative and quantitative approaches can be integrated in different

stages of a study. They can be integrated in the data collection stage. Also, this

applies to data analysis and interpretation stages via, for instance, transforming

themes or codes into numbers (Creswell 2003). Thus, the strength of the findings can

be increased by means of cross-validation and convergence of the data obtained from

multiple sources of evidence (Kaplan and Duchon 1988).

Therefore, it is essential to pay attention to reliability and validity in order to

evaluate the quality of the research design. According to Yin (1994), reliability and

validity have a number of dimensions: construct validity, internal validity, and

external validity.

The extent to which correct operational measures are established for the

concepts being studied represents the construct validity. Yin (1994), and Marshall

and Rossman (1999) state that construct validity is achieved by using multiple

sources of evidence. In this research study, triangulation was employed to strengthen

construct validity by using multiple sources of data collection: questionnaires,

archival records and documentation, direct and participant observation, unstructured

interview and conversation with note taking. Therefore, looking for confirmation

from multiple sources results in more reliable findings.

The extent to which a causal relationship can be established, whereby certain

conditions are shown to lead to other conditions (as distinguished from spurious


relationships) represents internal validity, while establishing the domain to which a

study’s findings can be generalised represents external validity (Yin 1994).

Therefore, in the current research a number of validation and verification techniques

were performed during the study to validate the collected data from observations,

questionnaires, unstructured interview and conversation with note-taking. The

analysis of quantitative and qualitative data has also been validated with statistical

analysis and coding, including descriptive analysis, inferential analysis, SEM,

thematic analysis, interpretation, and process mapping, to ensure internal validity.

The questionnaire items were firstly examined and translated into Arabic, the mother

tongue of the participants, for the sake of clarity on the patients’ part. The researcher

used succinct and simple language to elicit the necessary responses. Translation from

English to Arabic was a major issue that had to be addressed in the process of

developing the questionnaire (Al-Juhani 1991). The researcher used a threefold

process for this. First, the questionnaire items were translated into Arabic. Then, the

Arabic version was back-translated into English without the use of the original as a

guide. Finally, the original version was then changed in light of the translation to

present an “equivalent translatable” version (Drenth and Groenendijk 1998). This is

called the decentring method of translation through which the original version is not

considered the sole criterion (Brislin 1986). According to Drenth and Groenendijk

(1998), by using the decentring method, “the translated version is a more equivalent

alternative to the original”. To increase the validity and reliability of this

questionnaire, a pilot test for the patient’s questionnaire was conducted and the final

draft version of the questionnaire was reviewed with the research supervisor. Then,

the researcher sent around 20 questionnaires via email to some people who live in

Abha City and who have experience with the ED at Asir Central Hospital. Ten

responded. Based on data obtained from the pilot study, some questions were

changed due to participants’ difficulty in understanding. Thus, the clarity of the

questionnaire was strengthened in the final draft.

This study was conducted as a single case study, which allows some degree of

generalisation (Dunn 2003; Eisenhardt and Graebner 2007). Only one emergency

department was selected for observation of the patient flow in one hospital in Saudi

Arabia; this may decrease the possibility of generalisation to other settings. There

could be variations in the level of efficiency of operations. Although similarity is


expected among different hospitals in the management of ED patient flow, there may

be limits to access to information or willingness of the case study group to

participate. It seems feasible to say that these findings may be used in hospitals

similar to Asir Central Hospital or in a broad sense in a healthcare system similar to

Saudi Arabia; however it is hard to determine the level of generalisability of these

findings. In inductive exploratory type research, it may need a number of

observations for a single case. However, as known when a number of studies are

increased; the validity and reliability will also increase. According to Gray (2013),

“to ensure a degree of reliability in inductive research, the researcher often takes

multiple cases or instances, though, for example, multiplying observations rather

than basing conclusions on one case”.

In fact, the characteristics and features of the ED are similar to those of other

hospital departments (e.g., intensive care unit, operating rooms, and radiology

department), namely, a high level of complexity, demand uncertainty, limited

resources, and high level of human interactions. The issues of capacity planning,

scheduling (staff, operations, and patients), demand planning, and resource allocation

are all common between these departments. In addition, addressing these issues

usually involves multiple, often conflicting, objectives such as reducing waiting time

for patients, increasing the efficiency, and achieving high levels of service quality.

Therefore, the survey was framed to investigate many of these problems, such as

capacity planning, staff scheduling, demand planning, and throughput analysis,

which are common to other healthcare facilities as well as other service sectors and

supply chain business processes. Therefore, it is believed that empirical evaluation of

the framework through all phases of the case study contributes towards increased

confidence in the transferability of findings to a broad range of healthcare settings.

Thus, it is clear that there are many factors that strengthen the study’s external

validity.

Despite the several contributions that this study is expected to make, some

limitations of the study should be noted. The study targeted patient flow in the ED

from arrival until discharge from the ED, rather than whole of hospital processes. In

addition, the presence of the researcher as an observer may lead the ED team

members to alter their behaviour, knowing that they are being observed; hence all the

possible inefficiencies may not be observed. Another obvious limitation of this study


is imposed by the set of selected research methods. Case studies as action research

(with observation, patient survey and focus group) and Lean Six Sigma

Methodology, which are the primary approaches employed in this research for data

collection and analysis purposes, pose certain restrictions on the way information

about studied phenomena will be discussed in next chapter.

ETHICAL CONSIDERATIONS 3.8

This research is conducted through a single case study, thus various ethical

issues arise during data collection in the setting, analysis and dissemination of

projects (Creswell 2007). These include issues of anonymity, privacy, and

confidentiality, as well as the possibility of deception of the participants, and the

probability of harm or risk to both the participants and the researcher (Moloney et al.

2006). Therefore, permission to conduct the study was obtained from the Human

Research and Ethics Committee at the Queensland University of Technology (No:

1200000331), the Directorate General of Asir Central Hospital, and the Directorate

General of Emergency Department in Asir Central Hospital in Abha city, Saudi

Arabia (please see Appendices).

Before collecting the voice of patients through the questionnaire, the voluntary

nature of participation was explained to them. Also, the returned completed

questionnaires indicated their consent to participate. To ensure anonymity, the

participants were advised of the protective procedures, whereby no names or other

identification information were needed on the questionnaires. In addition, they were

told that the descriptive and statistical findings that are reported in this thesis and in

any future publications will include no identifiable personal information.

Also in this study, emergency department staff was informed about the

presence of the researcher for patient flow observation. It is important to define the

boundaries of what is acceptable to observe, otherwise the participants may find their

actions are being controlled (Bryman 1988). The observations were conducted in

such a way as to keep the researcher’s participation in the situation to a minimum in

order to avoid influencing the staff. To avoid any kind of interruption during the

observation, the researcher concentrated on the processes and activities that take

place in emergency department which related to patient flow, without any discussion

with the staff during their shifts.


In addition, unstructured interviews and conversations with note-taking have

been conducted during staff lunch times or with an appointment based on their

availability. Before starting the conversations, they were informed about their

voluntary participation and that they could withdraw at any time without providing

any reasons. All significant findings from this conversation are presented in this

thesis and in future publications without revealing the participants’ identity.

Chapter 4: Integrated Lean Six Sigma Model for Patient Flow Improvement in EDs 67

Integrated Lean Six Sigma Chapter 4:

Model for Patient Flow

Improvement in EDs

The elimination of waste through the standardisation of processes and the

participation of all employees in process improvement are the basic lean concepts

(Dickson, Singh, et al. 2009). In addition, Six Sigma helps to quantify problems,

facilitates evidence-based decisions (this prevents wasting time on anecdotal

evidence), helps to understand and reduce variation, and identifies the root causes of

variation to find sustainable solutions (Kuo et al. 2011). However, studies of the

implementation of Lean and Six Sigma in several areas of a healthcare setting have

not reported in-depth statistical findings about individual projects, and hence provide

less evidence of improvements (DelliFraine et al. 2010). Thus, a better understanding

of the effectiveness of Six Sigma and Lean is necessary and should be supported by

clear, measurable evaluation variables. Thus, looking for a combination approach is

really important in this situation. The following section will present an integrated

Lean thinking and Six Sigma model for investigation and improvement of patient

flow in emergency departments in healthcare systems.

DEVELOPMENT OF LEAN SIX SIGMA METHODOLOGY IN 4.1

EMERGENCY DEPARTMENTS

Many of the new approaches recently applied in health care are motivated by

lean thinking, with a focus on layout improvement, reduction in waste elements,

continuous quality improvement, reduction of costs and participation of all co-

workers (Ahlstrom 2004; de Koning et al. 2006; Weinstock 2008). However, the

successful use of lean techniques to streamline ED patient flows and reduce journey

times has been reported with little evidence or detail (DelliFraine et al. 2010;

Langabeer et al. 2009; Poksinska 2010). Furthermore, absence of a systematic

integration framework including Lean thinking and Six Sigma methodology for

patient flow improvement in emergency departments still represents a big challenge.

Lean strategies aim to eliminate waste through the standardisation of processes

and the participation of all employees in process improvement (Dickson, Singh, et al.

68 Chapter 4: Integrated Lean Six Sigma Model for Patient Flow Improvement in EDs

2009; Ng et al. 2010; Teichgräber and de Bucourt 2012; Womack and Jones 2003).

In addition, Six Sigma helps to quantify problems; facilitates evidence-based

decisions (and so keeps time from being wasted on anecdotal evidence); helps the

company understand variation and reduce it; and identifies the root causes of

variation in order to find sustainable solutions (Lee-Lewandrowski et al. 2003).

Thus, the integration of two methodologies can achieve better results than either

method could achieve alone (Antony 2011). The existing literature has not

adequately established a true empirical or theoretical foundation for effectiveness

demonstration and goal-specific improvements of Lean and Six Sigma

methodologies in healthcare organisations (Cameron et al. 2002; Michelen et al.

2006). In addition, there are very limited research studies that empirically document

how these two methods can be integrated into one approach and how they can be

influenced to achieve world class results (Taj and Morosan 2011).

To understand the actual effectiveness of Lean and Six Sigma in healthcare,

any analysis should be supported by clear, measurable evaluation metrics. Therefore,

an Integrated Lean Six Sigma approach for patient flow improvement in hospital

emergency departments is proposed in this research study, and a framework was

developed to solve this kind of problem.

PROPOSED INTEGRATED LSS MODEL IN EDS 4.2

The framework proposes two different tools of Lean and Six Sigma approaches

to define and investigate the current process of patient flow in Emergency

Department through the voice of the customer (internal and external) and voice of the

process (process mapping). It then identifies a measurable performance metrics to

evaluate the findings for continuous quality improvement. The proposed model was

developed based on the integration of five Lean principles and the DMAIC process

of Six Sigma methodology. Womack and Jones (2003) identified the elimination of

waste, the identification of the value stream, the achievement of flow through the

process, pacing by a pull signal and the continuous pursuit of perfection as the five

main principles of lean thinking.

Also, the process uses five stages to question and control quality, under the

acronym DMAIC, which stands for Define, Measure, Analyse, Improve, and

Control. Define means defining the problem, clarity of objectives, costs, issues,


resource planning, and so on. Fish-bone flow charts and other such schematic

representations help throw light on the activities and resources required, and a

solution-based approach is adopted. The processes are then studied and tracked, and

all data are collected and processed during the Measure stage. Processes are

examined and reviews include the use of charts, graphs, and other pictorial statistical

representation of the data collected and analysed. Causal relationships are broken

down to ascertain the main reason for the problem in a particular process. Pareto

charts are also used for depicting earlier processes, changes effected, and time series.

These bar graphs show the effect that a particular measure has had on quality, costs,

or time and resources.

During the analyse stage, mathematical techniques are used for studying,

reviewing and analysing data. This depicts the processes and deviations and helps the

project teams to address key issues. Thus, the Six Sigma method helps analysts and

quality experts to address, monitor, and improve quality. While the Measure and

Analyse stages address data and information collection, in the Improve stage,

possible solutions are collated and deployed. The Control stage then verifies

solutions and strategies and measurement tools to gauge the effectiveness of these

solutions, and strategies are applied. The team leader then effects the validation and

verification of processes and solutions (George 2002).

Based on the previous integration of Lean principles with Six Sigma

methodology (DMAIC), a Lean Six Sigma model (LSS) has been proposed, as

shown in Figure 4.1. Details of the model are provided in the following sections and

further details can be found in Chapters 5, 6 and 7.

DETAILED PROCEDURES IN LSS MODEL 4.3

Lean Six Sigma is a combination of the conceptions of two productivity

improvement programs, Six Sigma methodology and Lean manufacturing. Six Sigma

is a quality improvement methodology. However, it does not particularly look at

waste that occurs in the process operations. On the other hand, Lean manufacturing is

a methodology that focuses on reducing waste and cycle time in such processes.

According to George (2002), Lean Six Sigma is a methodology that maximises value

by achieving the fastest rate of improvement in cost, customer satisfaction, quality,

process speed, and invested capital. Thus, integrated Lean Six Sigma methodology


can be used to eliminate waste, reduce variation and improve quality and customer

satisfaction in any process. The following figure summarises the integrated approach

and details of procedure that are described in the following sub-sections.

Figure 4.1: Conceptual framework of LSS for ED

LSS procedures

The Lean Six Sigma model for patient flow improvement was developed from

the literature review, with integration of Lean concepts and Six Sigma principles.

Clearly, it is necessary to look carefully at the strategies and procedures of the

proposed LSS Model during the case study investigation of patient flow in the ED.

To conduct this case study, DMAIC methodology and Lean principles have been


followed step by step in the data collection and analysis stages, in order to determine

patient flow problems in the emergency department, as shown in the previous figure.

Figure 4.2: Integrated Lean Six Sigma strategy and procedures

As shown in Figure 4.2, there are four main phases using LSS procedures in

this research study as follwing:

Define Phase:

To understand customer needs, this phase investigates the customer

requirement based on the following steps:

Step 1: Draft a project charter:

The primary deliverables to establish a project charter for this study include the

following items (Pyzdek 2003):

Business case. The director of the emergency department in Asir

central hospital in Abha city in 2012 was contacted, and an agreement


was approved to collect data in this hospital. Permission from the ED

director is attached in Appendix J.

Project goals and objectives. The initial objectives and goals for this

research study were formed based on the literature review and the

preliminary investigations for this hospital.

Milestones/time lines. An initial timeline was proposed to set clear

guidelines based on the research PhD candidature timeline.

Scope, constraints and assumptions. Based on the literature review

and the current situation in the emergency department, the research

was conducted with the objective of improving patient flow in the

emergency department.

Create a team. The team was created from the emergency department

staff and the quality department.

Step 2: identify the voice of the customer (VOC)/the customer perceptions.

In this step, both external and internal customers were fully identified, and their

needs investigated. Patients, process and ED staff were the main customers. See

sections 3.5.1- 3.5.4 for details.

Measure and Analysis Phase:

Step 1: Conduct process and data analysis: Analyse and categorise the non-

value-added activities in terms of the lean wastes in the hospital situation. Categorise

the factors that are critical to quality after examining the voice of the customer

(VOC) and the voice of process (VOP).

Step 2: Translate the VOC into measurable requirements: For example; waiting

time during patients’ treatment in ED (M1), layout of ED (M5) and waiting time

before examination by nurse (M6). More details are provided in Tables 7.7 and 7.9 in

Chapter 7.

Step 3: identify variables that are critical to quality (CTQ) based on mean

scores and statistical analysis of the measurable needs; CTQ variables are those

which represent important values in the quality of services, based on the perspectives

of customers (patients and ED staff).


Step 4: Build value stream map (VSM). This value stream map shows the

current processes. It helps in understanding the need for change and identifies wastes

taking place in the process. Also, based on the VSM for the current situation, VSMs

can be proposed for future situations to achieve a higher level of performance.

Step 5: Determine the significant root causes for non-value-added activities by

prioritising the waste/value to the customer. By using appropriate Lean and Six

Sigma tools which help in identifying these causes, a cause and effect diagram was

created in this research study.

Details are described in Chapters 5, 6 and 7.

After these steps, performance metrics were identified. These metrics were

determined based on customer needs using statistical analysis, and analysis of A3

problem solving sheets and value stream mapping analysis, to satisfy Lean Six Sigma

attributes. Then, a fuzzy logic model was proposed to assess the performance metrics

and make strategic suggestions for the healthcare provider.

Improvement Phase:

Based on brainstorming and research knowledge, integration and triangulation

have been carried out to evaluate the findings for voice of customer and voice of

process for the patient flow improvement in the emergency department. Also, the

research team discussed the current situation in order to create an improvement plan

to make the processes flow continuously and standardise work using lean

methodology. Recommendations for improvement are presented to eliminate the

waste based on the perspectives of the customer. Lean tools/methods have been

selected which help to reduce the root causes that impact patient flow in the

emergency department, such as VSM. These details are described in Chapter 7.

Control Phase:

For continuous quality improvement, it is necessary to control and monitor

some major variables. The following steps outline this control plan:

Step 1: Develop a control plan and performance metrics to make sure that the

solutions endure and to control these metrics for continuous improvement.

Step 2: Implement this plan to control the critical variables relating to

performance and assist in tracking the process performance after improvement.


LEAN SIX SIGMA METHODOLOGIES AND THE CASE STUDY 4.4

This research integrates Lean thinking with Six Sigma Methodology for patient

flow investigation and improvement in the emergency department in a Saudi Arabian

hospital. Lean Six Sigma combined with a case study in a real life context is new to

the healthcare system in Saudi Arabia, and is appropriate because the boundaries

between phenomenon and context are not clearly evident (Yin 2003). In addition, the

complexity of Lean Six Sigma integration as part of case study evaluations is

appropriate to address a complex circumstance such as patient flow in the ED (Keen

and Packwood 1995).

The research for this study most closely aligns with action research. An

Integrated Lean Six Sigma approach has been used as a road map and procedures for

data gathering, analysing, and improving processes in a real world situation. As a

result of using some concepts from Lean strategy and Six Sigma methodology, voice

of customer (VOC) and voice of process (VOP) have been collected in this research

study. A questionnaire survey was used to collect the VOC (main customer) in the

emergency department. At the same time, archival records and documentation with

A3 problem solving sheets were used to collect the voice of ED staff (internal

customer). To understand the voice of process in the emergency department, in

cooperation with ED staff, direct and participant observation was conducted for

process mapping, with the aim of developing a future value stream map (VSM)

during the research field study in the emergency department.

The following process shows the procedure for using Lean and Six Sigma tools

in collecting data from an ED:

1. Initially, define waste, identify measurement value, and investigate and

analyse the current patient flow by using Voice of the Customer (VOC) +

A3 Problem Sheet + Value Stream Mapping (VSM).

2. Use the tools shown in Figure 4.3 to obtain the perspectives of patients and

hospital staff and to observe and investigate current patient flow in the ED.

3. Based on VOC, A3 and VSM, investigate the current ED patient flow to

obtain patient and staff perspectives.

4. Determine performance metrics in terms of Lean-Six Sigma (LSS) attributes

and six dimensions of healthcare quality services.


Figure 4.3: Integrated lean six sigma concepts and tools

VOICE OF THE PROCESS 4.5

Lean strategies became very popular when the world noted that the quality and

efficiency of Toyota was significantly improved through the implementation of the

Toyota production system (Barratt et al. 2011). Lean strategy helps to eliminate

waste and control cost; it is best described as a combination of philosophy, process,

people and structured problem solving (Drohomeretski et al. 2013; Pepper and

Spedding 2010). In an organisational learning environment, lean methodology brings

all levels of stakeholders together in a multidisciplinary team with the goal of

continuous improvement (Vinodh et al. 2011). The voice of the process involves

determining how the process is currently performing and can be accomplished

through many tools and analytical techniques, such as value stream mapping (VSM)

(Johnson and Capasso 2012). Lean techniques using value stream mapping has been

illustrated in the literature review with various applications such as: manufacturing

and service sectors (Lian and Van Landeghem 2007; Piercy and Rich 2009). The

VSM technique also can be used in the health care system to identify the sources of

Lean Thinking

Specify Value

Identify the value

Flow

Pull

Perfect

Six Sigma

Define

Measure

Analysis

Improve

Control

Emergency Department Patient Flow

• Entry

• process

• Discharge LSS

VOC

A3

VSM


waste and then to redesign health care processes by eliminating or reducing these

types of waste (Dohan et al. 2014). The literature shoowed that lean application in

emergency department was successfully improving the patient flow by decreasing

the overcrowding and access block with general process changing in ED (Dickson,

Singh, et al. 2009; Holden 2011; King et al. 2006; Ng et al. 2010). VSM involves

observing the process and gathering information to develop a value stream map for

the current ED process. Recently, VSM has become a helpful tool in implementation

of lean strategy within emergency department (Cookson et al. 2011).

Information about the activity and emergency department layout was

developed using process mapping, flow chart and value stream mapping (VSM) to

identify the voice of the process visually.The goal was to collect data regarding the

time spent by patients in all phases of the process, from the time the patient enters the

front door to the point they are discharged or admitted to the hospital. This method

helps to define and categorise the patient flow in the ED. In addition, it helps to

identify the cause of main problems in patient flow in the ED. Observation was

concentrated initially on patient flow from arrival until discharge from the ED. A

time and motion study for the patient in each activity in the ED was conducted using

values stream map and A3 problem solving sheets. A map of the processes was

constructed to categorise patient flow and understand the current system. We

followed these steps to investigate the current patient flow in the ED:

Introduction of Lean thinking to ED staff (education): Individual

meetings were held with the ED director, nurse supervisor and some of the

ED staff working during shifts (physicians, nurses and technicians).

Initially, the ED director gave a brief description of the ED, introduced the

researcher to ED staff and spoke about quality concepts and the

importance of the proposed research in terms of ED improvement in Asir

Central Hospital. Documents introducing Lean thinking and Six Sigma in

healthcare and details of the proposed research were sent to interested

staff.

Observation process: The observation began with an initial tour with the

nurse supervisor to get a clear picture of the ED environment. The first

step of the observation was to define all activities and processes that take

place throughout the patient’s journey in the ED from entry to discharge.


The nurse supervisor introduced the researcher (the first author) to other

members of staff and asked them to be open and helpful regarding any

necessary information during the research process. Then, the researcher

began to inspect the current system, using paper and pen to sketch the

whole process in the ED and make notes, specifically on patient flow from

entry to discharge (for patients categorised under levels 3 and 4). The

researcher then designed a time study sheet for recording time spent by

patients at different stages between registration and triage and another

sheet to follow patient time from entry to discharge, based on ED

documentation (Appendices A1 and A2).

During this observation, the current ED layout was carefully analysed to

determine the effectiveness of ED layout on smoothness of patient flow (see chapters

6 and 7).

VOICE OF THE CUSTOMER 4.6

As part of Define/Identify Value (Define Phase), the voice of the main

customer (VOC) is a basic concept of Lean and Six Sigma tools which were used to

collect data. The voice of the external customer (patient) was collected through a

questionnaire survey regarding quality and patient satisfaction in the emergency

department. To collect the voice of the emergency staff (internal customer), it was

really important to use another method beside the closed-ended questionnaire, in

order to add more evidence and validate staff perspective. Therefore, A3 Problem

solving sheets were used to collect the voice of internal customer (ED staff). Both

structured questions and unstructured interview procedures and conversations with

ED staff recorded in notes have been integrated; these investigations took place in

the emergency department for a specific group of available and interested staff

between October 2012 and March 2013 in Asir Central Hospital. It is important to

study quality improvement through the involvement of stakeholders during the

project planning phase, acquiring information about the project participant’s

knowledge, experience and their perspective on the issue, in order to answer the

research questions.

A key aspect of the A3 sheet as a document is that it captures all the relevant

information on a single side of an 11 by 17 inch piece of paper. This forces the


person creating the A3 to be frugal in the allocation of paper space to communicate

the essential facts and logic supporting the countermeasures being proposed. A3

reports are clear and objective communication tools written for all specialists, and

can be reviewed by staff. This allows for cross-departmental sharing of process

changes and generates even more problem-solving ideas (Hoot and Aronsky 2008). It

is an effective communication tool enabling a number of people to cooperate, and

their judgements regarding a specific phenomenon to be collected. An A3 problem

solving sheet was used to identify the patient flow problems in the ED from the staff

perspective, based on their working experience.

To use this tool, the work took place in two phases:

Phase 1: Educate ED staff about lean concepts. In this stage, ED

physicians, nurses and technicians were given an introduction to lean

strategies in the healthcare system through individual and group meetings

with the ED director and nurse supervisor. Additionally, all staff involved

received an email communication about lean strategy, the A3 Problem

Solving Sheet, and VSM in healthcare.

Phase 2: ED staff provided feedback about the current system by

answering A3 Problem Solving Sheet questions. This phase was an

indirect observation based on staff working experiences and feedback in

the ED, collected using a lean tool (A3) to investigate the ED process. The

A3 Problem Solving Sheet included two different sheets: the investigation

sheet and the improvement plan sheet. In this phase, A3 Problem Solving

Sheets were distributed using a purposeful sampling technique to three

physicians, three nurses, three technicians and one quality specialist.

The following figures show an example of the A3 problem solving sheets that

were used to collect the voice of staff in the emergency department. More figures in

Appendix L.


Figure 4.4: Investigating sheet, page 1


Figure 4.5: Improvement sheet, page 1


WASTE IDENTIFICATION IN ED 4.7

As a result of collecting the voice of customer and voice of process, several

types of waste were identified. According to lean concepts, there are three different

types of activities: value-added activities, necessary non-value-added activities and

unnecessary non-value-added activities. Both the necessary and unnecessary non-

value-added activities are considered “waste” in lean concepts. Reduction or

elimination of non-value-added activities is the key to improving the flow of patients

in EDs. Table 4.1 presents the seven major wastes commonly identified in

manufacturing environments and their equivalents that take place in the health care

system and EDs (Bush 2007; Dohan et al. 2014; Jimmerson et al. 2005; Jimmerson

2009; Robinson et al. 2012; Womack and Miller 2005).

Table 4.1: The 7 Sources of Waste in Lean Healthcare

Type of Waste Definition/ Result of

Waiting Idle time; such as: waiting for patients, staff, results, prescriptions,

medicines or waiting for doctors discharge decision. (Waiting for more

processing).

Over-

processing

Repeated or duplicated information for the same task; such as ask for

patients’ details several times. (Unnecessary amount of work is

performed for a task).

Defects Errors as a result of incorrect delivery of medical services.

Over-

production

Unnecessary tasks are performed more times; such as: unnecessary tests

are requested from lab.

Motion/conve

yance

Unnecessary staff or patient movements, such as: walking or searching

for forms or equipment.

Inventory Disconnect between inventory stock and actual demand for materials,

resources or patients, such as waiting list or excess stock.

Confusion Doing work without clear instructions; uncertainty in current workflow.

Clearly, improved flow will increase patient satisfaction, reduce wastes (such

as length of stay, unnecessary waiting times and non-value added activities), then

eventually improve the quality of services in the emergency department and in the

hospital quality in general. To understand the actual effectiveness of Lean and Six

Sigma in healthcare, any analysis should be supported by clear, measurable

evaluation metrics. Therefore, an Integrated Lean Six Sigma approach for patient

flow improvement in hospital emergency departments was proposed and the

framework was developed to solve this kind of problem. Major findings of our

research study, based on the voice of customer and the voice of process, are

discussed in the following chapters.

Chapter 5: Patient Flow Problems and Voice of Customer (Patients) 83

Patient Flow Problems and Voice of Chapter 5:

Customer (Patients)

VOICE OF THE EXTERNAL CUSTOMER (PATIENTS) 5.1

To understand the needs of the main customer who receives the ED services, it is

essential to listen to their requirements. One of the most important techniques which are

related to the Integrated Lean Six Sigma Model (Define/Specify/Identify the value) is the

voice of the customer (VOC). This study classifies patients as external customers and ED

staff as internal customers. This chapter discusses and answers the second question of this

research study: how can waste affecting patient flow and quality of service in EDs be

identified by using Lean strategy and Six Sigma techniques?

The voice of the customer was gathered using a questionnaire survey among patients in

the Emergency Department (ED) of Asir Central Hospital. The first section of the

questionnaire was about the background of participants, and the second section related to the

patient’s reasons for attending ED. The third section collected the patients’ perceptions of

quality, and the fourth section canvassed their satisfaction with the current healthcare system,

and finally, what they saw as the main problems. Data were then transferred to a database and

the aggregate data were analysed. By using SPSS software, the statistical analysis was

conducted primarily with descriptive analysis, and also with exploratory factor analysis

(EFA), Inferential Statistics with MANOVA and ANOVA, and Structural Equation

Modelling (SEM). The results of this analysis are presented in detail in the following

sections.

DESCRIPTIVE ANALYSIS 5.2

The questionnaire was distributed to 350 ED patients who visited the emergency

department and voluntarily agreed to participate in this study during their waiting time in ED

between November 2012 and February 2013 in Asir Central Hospital. In total, 120 patients

completed the questionnaire, a response rate of 34.29%.

The participants were asked to indicate their gender and level of education. Of the 119

participants that responded to this question (one missing response) 55.5% (N=66) indicated

that they were male and 44.5% were female, as shown in Table 5.1.

84 Chapter 5: Patient Flow Problems and Voice of Customer (Patients)

Table 5.1: Descriptive statistics for patient’s gender

Frequency Percent Valid Percent Cumulative Percent

Valid

Female 53 44.2 44.5 44.5

Male 66 55.0 55.5 100.0

Total 119 99.2 100.0

Missing System 1 .8

Total 120 100.0

Of the 119 participants that responded, about 1/3 (33.6%) had completed primary,

intermediate or secondary school, another 17.6% had completed a Diploma, and another

46.2% had completed a Bachelor’s Degree. A further 2.5% had completed a higher education

degree of some kind.

With a view to subsequent analysis, education level was transformed into a four-

category variable where each category contained more than 10% of the participants.

Figure 5.1: Pie chart of transformed educational level variable (number, percentage

shown)


The distribution of educational levels by gender is noteworthy, with a majority of

females (19 patients) with BA or higher degree level qualifications, and a majority of males

(39 patients) with BA or higher degree qualifications as shown in Table 5.2.

Table 5.2: Description for the distribution of patient’s gender and education level

Gender Primary/Intermediate

level

Secondary

level

Diploma BA/higher

education

Total

Female 13 10 11 19 53

Male 7 10 10 39 66

Total 20 20 21 58 119

Reasons for Attending Emergency Department 5.2.1

Participants were asked to indicate the importance of each of 12 reasons for attending

an Emergency Department (ED), using a 5-point Likert importance scale (Unimportant [1],

somewhat important [2], moderately important [3], Important [4], Very important [5]). The

results are shown in Table 5.3.

Table 5.3: Descriptive statistics for importance to attend Emergency Department (ordered by mean)

Statistics N Mean Std.

Dev.

Skewness Kurtosis

No charge for lab and radiology tests or medicines at

ED

114 3.80 1.512 -1.041 -0.451

Able to see doctor and have tests done at ED 113 3.75 1.306 -0.850 -0.309

No charge to see doctor at ED 113 3.68 1.560 -0.847 -0.884

Prefer ED as can attend whenever want 114 3.67 1.461 -0.752 -0.854

Medical treatment better at ED 116 3.67 1.375 -0.653 -0.917

Health problem required immediate attention and

too urgent to wait

116 3.66 1.408 -0.675 -0.862

Health problem too serious for a PMC 113 3.27 1.530 -0.276 -1.454

PMC was closed 115 2.94 1.677 -0.016 -1.706

Family traditionally uses ED for health care 112 2.55 1.582 0.422 -1.387

Easier to get to ED than PMC 113 2.54 1.408 0.435 -1.154

Came to emergency to avoid difficulties getting

referral from PMC

113 2.43 1.586 0.527 -1.367

Wanted second opinion 111 2.41 1.424 0.523 -1.029

Table 5.3 lists descriptive statistics for the 12 reasons to attend an Emergency

Department, ordered by the magnitude of the mean. These mean values reflect the Likert

scale values used to generate them.


The mean scores between 2 and 3 reflect an average response in the interval between a

rating of 2 (Somewhat important) and 3 (Moderately important). As shown in Table 5.3, the

average rating for five of the 12 reasons lie in this interval:

Wanted second opinion.

Came to ED to avoid difficulties getting referral from primary medical care

centre.

Easier to get to ED than primary medical care centre.

Family traditionally uses ED for health care.

Primary medical care centre was closed.

In addition, the mean scores between 3 and 4 reflect an average response in the

moderately important (3) to Important (4) range. As shown in Table 5.3, the average rating

for the remaining seven reasons lies in this interval:

Health problem too serious for a PMC.

Health problem required immediate attention and too urgent to wait

Medical treatment better at ED.

Prefer ED as can attend whenever want.

No charge to see doctor at ED.

Able to see doctor and have tests done at ED.

No charge for lab and radiology tests or medicines at ED.

This section of the survey was conducted to understand the patients’ perceptions

regarding the most important reasons for visiting the emergency department, in order to

identify the value and non-value added activities and to define the kinds of waste that take

place in EDs as a result of misunderstandings by patients about the purpose of EDS and the

non-value of non-urgent cases to EDs,

These reasons can be divided into: access ease, financial ease, better treatment and

preferred venue. In the present study, 51.3% of patient participants indicated that better

treatment was an important or very important reason for visiting the emergency department.

In addition, 40.7% of participants indicated that financial ease was a very important reason to

visit the emergency department, instead of going for private hospital. Also, 36.6% of patients


indicated that ease of access was an important or very important reason to visit the

emergency department. Overall, 63.7% of participants indicated that ED is a preferred venue.

Finally, the values for skew and kurtosis were consistent with responses to the 12 reasons

being normally distributed (Skew & Kurtosis <±1.96).

Concept of Quality in Emergency Department Services 5.2.2

Participants were asked to indicate their level of agreement with each of 11 concepts of

quality in ED service items, using a 5-point Likert scale that measures level of agreement

(Strongly disagree [1], Disagree [2], Neutral [3], Agree [4], Strongly agree [5]).

Table 5.4: Descriptive statistics for agreement with 11 concepts of quality items in ED services (ordered by

mean)

Statistics N Mean Std.

Dev.

Skewness Kurtosis

Respect for patients 119 4.56 0.788 -1.691 1.822

Accurate diagnosis and proper treatment 120 4.49 0.926 -1.978 3.436

Use of modern technology in providing health services

in ED

119 4.36 1.023 -1.598 1.834

Expertise and efficiency of ED staff 117 4.35 1.028 -1.428 0.860

Serve max. number of patients possible 118 4.35 1.081 -1.806 2.500

Optimal utilization of available resources 116 4.35 0.962 -1.241 0.254

Minimising proportion of diseases, mortality &

disability within society

119 4.34 1.044 -1.532 1.528

Error free in treatment and diagnosis 119 4.27 1.177 -1.524 1.230

Availability of adequate test facilities 119 4.24 1.097 -1.284 0.482

Minimise unnecessary tests and diagnosis 119 4.20 1.132 -1.370 0.991

Short waiting times throughout process of treatment

in ED

120 3.96 1.434 -1.125 -0.175

Table 5.4 lists descriptive statistics for the 11 concepts of quality in ED service items,

ordered by the magnitude of the mean. As previously, these mean values reflect the Likert

scale values used to generate them. Thus, the mean scores between 3 and 4 reflect an average

response in the interval between a rating of 3 (Neutral) and 4 (Agree). As shown in the table,

the average rating for one of the 11 concepts of quality in ED service items lie in this

interval: short waiting times throughout process of treatment in emergency department.


rating of 4 (Agree) and 5 (Strongly agree). As shown in Table 5.4, the average rating for 10

of the 11 concept of quality in ED services items lie in this interval:

Minimize unnecessary tests and diagnosis.


Availability of adequate test facilities.

Error free in treatment and diagnosis.

Minimising proportion of diseases, mortality & disability within society.

Optimal utilization of available resources.

Serve max. number of patients possible.

Expertise and efficiency of ED staff.

Use of modern technology in providing health services in ED.

Accurate diagnosis and proper treatment.

Respect for patients.

As shown in Table 5.4, participants ranked the respect for patient as the most important

indicator for quality in emergency department services. Also, they were likely to agree that

minimising unnecessary tests, accuracy of diagnosis and short waiting times throughout the

process of treatment in ED are concepts of quality in ED services. Finally, with the

exception of responses to the statement re accurate diagnosis and proper treatment, values for

skew and kurtosis were consistent with responses to the 11 concepts of quality in ED service

items being normally distributed (Skew & Kurtosis <±1.96).

Satisfaction with Internal and External Environment in ED 5.2.3

Participants were asked to indicate level of satisfaction with each of six internal and

external environment items using a 5-point Likert scale that measures level of satisfaction

(Very unsatisfied [1], Unsatisfied [2], Neutral [3], Satisfied [4], Very satisfied [5]).

Table 5.5: Descriptive statistics for satisfaction with six internal and external environment items in ED (ordered

by mean)

Statistics N Mean Std. Dev. Skew Kurtosis

Cleanness of ED 119 3.31 1.056 -0.346 -0.548

ED accessibility 119 3.22 1.208 -0.519 -0.899

Clarity of signboards 119 3.12 1.243 -0.200 -1.070

Quiet location 119 2.47 1.199 0.400 -0.878

Layout of ED 119 2.25 1.159 0.622 -0.744

Comfort of waiting area 119 2.04 1.123 0.939 -0.108


Table 5.5 lists descriptive statistics for satisfaction about six internal and external

environment items, ordered by the magnitude of the mean. The mean scores between 2 and 3

reflect an average response in the interval between a rating of 2 (Unsatisfied) and 3 (Neutral).

As shown in Table 5.5, the average rating for three of the six internal and external

environment items lie in this interval:

Comfort of waiting area.

Layout of ED.

Quiet location.


rating of 3 (Neutral) and 4 (Satisfied). As shown in Table 5.5, the average rating for three of

the six internal and external environment items lie in this interval:

Clarity of signboards.

ED accessibility.

Cleanness of ED.

As shown in Table 5.5, the average satisfaction level with the internal and external

environment of the emergency department is ‘unsatisfied’ as indicated by the participants.

Finally, all values for skew and kurtosis were consistent with responses to the six internal and

external environment items being normally distributed (Skew & Kurtosis <±1.96).

Satisfaction with Systems and Procedures in ED 5.2.4

Participants were asked to indicate their level of satisfaction with each of eight systems

and procedures items, using a 5-point Likert scale that measures level of satisfaction (Very

unsatisfied [1], Unsatisfied [2], Neutral [3], Satisfied [4], Very satisfied [5]).

Table 5.6 list descriptive statistics for satisfaction with eight systems and procedures

items, ordered by the magnitude of the mean.


Table 5.6: Descriptive statistics for satisfaction with eight systems and procedures items in ED (ordered by

mean)


Treatment by reception of you and family 119 3.47 1.199 -0.740 -0.269

Doing right health services first time 117 3.14 1.129 -0.420 -0.512

Speed of locating medical file 119 3.04 1.203 -0.527 -0.902

Integrated services in ED 119 2.97 1.204 -0.010 -1.029

Co-operation in health services provided to you 119 2.84 1.179 -0.221 -1.050

Communication among employees to facilitate health

services

119 2.73 1.170 -0.170 -1.164

Effectiveness of system in dealing with patient’s

complaints

119 2.13 1.070 0.546 -0.460

Waiting time during treatment in ED 118 1.94 1.119 1.086 0.185

As shown in Table 5.6, the average rating for one of the eight systems and procedures

lies in this interval of 1 (Very unsatisfied) and 3 (Unsatisfied).

Waiting time during treatment in ED.

The average rating for four of the eight systems and procedure items lie in this interval

of 2 (Unsatisfied) and 3 (Neutral).

Effectiveness of system in dealing with patient’s complaints.

Communication among employees to facilitate health services.

Co-operation in health services provided to you.

Integrated services in ED.


rating of 3 (Neutral) and 4 (Satisfied). As shown in Table 5.6, the average rating for three of

the eight systems and procedure items lie in this interval:

Speed of locating medical file.

Doing right health services first time.

Treatment by reception of you and family.

Waiting time during treatment in ED was indicated by participants with the least

satisfaction level. In general, the average of satisfaction levels with the system and

procedures of the emergency department is unsatisfied, as indicated by participants. Finally,


all values for skew and kurtosis were consistent with responses to the eight systems and

procedures items being normally distributed (Skew & Kurtosis <±1.96).

Satisfaction with Medical Services in ED 5.2.5

Participants were asked to indicate their level of satisfaction with six medical service

items, using a 5-point Likert scale that measures level of satisfaction (Very unsatisfied [1],

Unsatisfied [2], Neutral [3], Satisfied [4], Very satisfied [5]).

Table 5.7: Descriptive statistics for satisfaction with six medical services items in ED (ordered by mean)


Doctor’s ability in prescribing proper treatment 118 3.69 1.002 -1.046 0.915

Accuracy of doctor’s diagnosis 118 3.56 0.992 -0.943 0.539

Doctor’s explanation about method of using

medication

118 3.35 1.157 -0.579 -0.636

Doctors’ use of understandable terms when

discussing case

117 3.32 1.209 -0.562 -0.666

Doctors spent adequate time to discuss case 118 2.58 1.277 0.347 -0.991

Waiting time before being diagnosed by doctor 118 2.14 1.183 0.866 -0.168

As shown in Table 5.7, the average rating for two of the six medical services items lie

in this interval between a rating of 2 (Unsatisfied) and 3 (Neutral).

Waiting time before being diagnosed by doctor.

Doctors spent adequate time to discuss case.


rating of 3 (Neutral) and 4 (Satisfied). The average rating for four of the medical support

services items lie in this interval:

Doctors’ use of understandable terms when discussing case.

Doctor’s explanation about method of using medication.

Accuracy of doctor’s diagnosis.

Doctor’s ability in prescribing proper treatment.

The participants were, on average, satisfied with the medical service related to doctor’s

capability in ED. However, they were likely to be unsatisfied with medical services that

related to the doctor’s use of time. Finally, all values for skew and kurtosis were consistent

with responses to the six medical services items being normally distributed (Skew & Kurtosis

<±1.96).


Satisfaction with Medical Support Services in ED 5.2.6

Table 5.8: Descriptive statistics for satisfaction with 13 medical support services items in ED (ordered by mean)


Pharmacist’s explanation about use of medicine 118 3.81 1.006 -1.133 1.273

Skills of radiology technicians in making required

radiology

118 3.52 0.748 -0.059 0.412

Nurses have done required medical check up 118 3.46 1.174 -0.799 -0.245

Accuracy of test report 118 3.43 0.901 -0.115 0.188

Skills of lab technicians in taking required specimen 118 3.39 0.970 -0.626 0.656

Accuracy of radiology report 118 3.38 0.886 -0.158 0.216

Ability of nurses to do right thing 118 3.22 1.185 -0.595 -0.805

Explanation of necessary procedures prior to

making radiology

117 3.14 1.074 -0.320 -0.366

Explanation of necessary procedures before lab

examination

117 3.12 1.044 -0.197 -0.527

Obtaining radiology results at promised time 118 2.94 1.149 0.015 -0.747

Obtaining lab results at promised time 118 2.83 1.172 0.174 -0.777

Availability of prescribed medicine in ED pharmacy 118 2.81 1.341 -0.047 -1.276

Waiting time before examination by nurse 116 2.46 1.347 0.375 -1.232

Table 5.8 lists descriptive statistics for satisfaction with 13 medical support service

items in the Emergency Department, ordered by the magnitude of the mean. These mean

values reflect the Likert scale values used to generate them.


rating of 2 (Unsatisfied) and 3 (Neutral). As shown in the table, the average rating for four of

the 13 medical support services items lie in this interval:

Waiting time before examination by nurse.

Availability of prescribed medicine in ED pharmacy.

Obtaining lab results at promised time.

Obtaining radiology results at promised time.

The average rating for eight of the 13 medical support services items lie between

Neutral (3) and Satisfied (4).

Explanation of necessary procedures before lab examination.

Explanation of necessary procedures prior to making radiology.

Ability of nurses to do right thing.


Accuracy of radiology report.

Skills of lab technicians in taking required specimen.

Accuracy of test report.

Nurses have done required medical check-up.

Skills of radiology technicians in making required radiology.

Pharmacist explanation about use of medicine.

Participants were unsatisfied with waiting time before examination by nurses. Also,

they were unsatisfied with length of time to obtain the results of lab tests or X-rays. In

addition, they indicted their satisfaction with the availability of prescribed medicines in ED

pharmacy and were likely to be satisfied with medical support staff capability in ED. All

values for skew and kurtosis were consistent with responses to the 13 medical support

services items being normally distributed (Skew & Kurtosis <±1.96).

Agreement about Waiting Time Problems 5.2.7

Participants were asked to indicate their level of agreement with six statements about

waiting times in the ED, using a 5-point Likert scale.

Table 5.9: Descriptive statistics for agreement about six waiting time items in ED (ordered by mean)


Waiting time before diagnosing by doctor was long 116 4.13 1.123 -1.384 1.106

Waiting time before examination by nurse was long 119 3.99 1.197 -1.070 0.130

Waiting time at reception was long 119 3.92 1.303 -1.027 -0.199

Waiting time during laboratory procedures was

long

118 3.68 1.161 -0.709 -0.158

Waiting time during radiology procedures was long 118 3.48 1.218 -0.393 -0.715

Waiting time at ED pharmacy was long 118 2.58 1.222 0.463 -0.755

Table 5.9 presents mean values of agreement about six waiting time items in the

Emergency Department, ordered by the magnitude of the mean. The mean scores between 2

and 3 reflect an average response in the interval between a rating of 2 (Disagree) and 3

(Neutral). The average rating for one of the six waiting time items lies in this interval:

Waiting time at ED Pharmacy was too long.



rating of 2 (Disagree) and 3 (Neutral). As shown in Table 5.9, the average rating for one of

the six waiting time items lies in this interval: Waiting time at ED Pharmacy was too long.


rating of 3 (Neutral) and 4 (Agree). The average rating for four of the six waiting time items

lie in this interval:

Waiting time during radiology procedures was long.

Waiting time during laboratory procedures was long.

Waiting time at reception was long.

Waiting time before examination by nurse was long.


rating of 4 (Agree) and 5 (Strongly agree). As shown in Table 5.9, the average rating for one

of the six waiting time items lies in this interval:

Waiting time before diagnosing by doctor was long.

Participants experienced that the waiting time before diagnosing by physicians was

longer than other waiting times during Ed treatment process. However, patients were likely to

disagree that waiting time at ED pharmacy was long. Finally, all values for skew and

kurtosis were consistent with responses to the six waiting time items being normally

distributed (Skew & Kurtosis <±1.96).

Agreement about Medical Services Problems 5.2.8

Table 5.10 lists descriptive statistics for agreement about four medical service items in

the Emergency Department, ordered by the magnitude of the mean.

Table 5.10: Descriptive statistics for agreement about four medical services items in ED (ordered by mean)

Statistics N Mean Std. Dev. Skew Kurtosi

s

The bed capacity at ED is not enough 119 4.12 1.091 -1.314 1.280

Some medicines not available in ED pharmacy 119 3.62 1.135 -0.343 -0.518

Some medical examinations not available in ED 119 3.45 1.006 0.177 -0.405

The medical devices sometimes did not function 119 3.13 0.920 0.326 0.998



rating of 3 (Neutral) and 4 (Agree) and the average rating for three of the four medical

services items lie in this interval:

The medical devices sometimes did not function

Some medical examinations not available in ED

Some medicines not available in ED pharmacy.

Similarly, as shown in Table 5.10, the average rating for one of the four medical

services items lies in this interval of 4 (Agree) and 5 (Strongly agree).

The bed capacity at ED is not enough.

Participants were most likely to agree that the bed capacity at ED is not enough, which

is the most important finding. All values for skew and kurtosis were consistent with

responses to the six waiting time items being normally distributed (Skew & Kurtosis <±1.96).

Agreement about ED Staff Care Problems 5.2.9

Participants were asked to indicate their level of agreement with three statements about

the level of care shown by ED staff.

Table 5.11: Descriptive statistics for agreement about three care of ED staff items (ordered by mean)


Sometimes necessary staff not available 118 3.98 1.078 -0.757 -0.225

Nurse did not take enough time for primary

examination

119 3.36 1.233 -0.336 -0.988

Doctor did not give comprehensive consultation

and diagnosis

119 3.33 1.187 -0.137 -0.933

Table 5.11 lists descriptive statistics for agreement about three care of ED staff items,

ordered by the magnitude of the mean.


rating of 3 (Neutral) and 4 (Agree) and the average rating for all three care of staff items lie

in this interval:

Doctor did not give comprehensive consultation and diagnosis

Nurse did not take enough time for primary examination

Sometime necessary staff not available.


The most important finding is that participants were like to agree that necessary staff

was not available sometimes in ED offices. Finally, all values for skew and kurtosis were

consistent with responses to the three items regarding care by ED staff being normally

distributed (Skew & Kurtosis <±1.96).

Summary: Participants were asked to state the level of importance, agreement or

satisfaction in reference to nine elements mentioned in the questionnaire (reasons for

attending ED, concept of quality in ED services, ED environment, ED systems and work

procedures, ED medical services, ED medical support services, problems with waiting times,

problems with medical services, problems with ED staff care). In addition, they were also

asked, based on their experience, to rate level of agreement about main problems experienced

in an ED. Results show that, according to the patients, long waiting time before seen by the

doctor, limited bed capacity and non-available of staff were main issues in ED.

EXPLORATORY FACTOR ANALYSIS (EFA) 5.3

Having completed descriptive analyses of nine subsets of items, in this section, we

examine the extent to which these items cluster to form scales. To understand a set of

constructs, we used an exploratory factor analysis, which is a useful technique (Field 2009).

The procedure for doing so here was to conduct one or more exploratory factor analyses

(EFAs) with Principal Component extraction and (where appropriate) Varimax rotation. In

each case, two analyses were performed. One considers the single factor solution, the other

the multi-factorial solution for each of the sub-sections in turn. SPSS Dimension Reduction

includes two tests of sampling adequacy. One of these, the KMO, is somewhat equivalent to

Cronbach’s Alpha. That is, values of .800–1.00 are desirable, and scores above .500 are

consistent with these items being factorable (Field 2005; Kaiser 1974). The other, Bartlett’s

test of sphericity, reports on the extent to which item correlations are 1, or 0, rather than a

broader range of values (Pallant 2011). Here, a significant p value indicates that items are

correlated at a broader range of values (p<.001) and appropriate for factor analysis (Dziuban

and Shirkey 1974).

Reasons for Attending an Emergency Department 5.3.1

Single-factor EFA

A single-factor EFA produced a solution where nine items loaded on a single factor at

levels in excess of .300, (KMO=.606), and the solution explained 26% of the cumulative

variance.


Table 5.12: Component matrix with nine reasons for attending an ED

Component Matrix Single-factor solution

No charge to see doctor at ED 0.653

Medical treatment better at ED 0.647

Easier to get to ED than PMC 0.644

Family traditionally uses ED for health care 0.634

Able to see doctor and have tests done at ED 0.548

PMC was closed -0.542

No charge for lab and radiology tests or medicines at ED 0.535

Prefer ED as can attend whenever want 0.504

Wanted second opinion 0.427

Cronbach’s alpha .746

As indicated in Table 5.12, the Cronbach’s Alpha score was in the .700–.800 range,

acceptable for a scale in the process of development. Based on the single-factor solution,

average scores were computed for use in the next section (Inferential statistics).

Multi-factor EFA

The multi-factor EFA produced a four-factor solution after excluding one item (it is

easier for me to get to the ED than a primary medical centre). Also, KMO=.567, and the

significant p value for the Bartlett’s test of sphericity (p<.001) indicates that items are

correlated at non-zero levels.

The 11 items loaded onto four factors, with three items loading on the factor, ease of

access, another two items loading on the factor, financial ease, two other items loaded on the

factor, better treatment, and four other items loaded on the factor, referred venue, where all

items loaded at levels in excess of .300, consistent with these 11 items expressing four simple

and distinct factors.

Table 5.13: Rotated component matrix with 11 reasons for attending an ED

Rotated Component Matrix Ease of

access

Financial

ease

Better

treatment

Preferred

venue

Able to see doctor and have tests done at ED 0.827

PMC was closed -0.783

Medical treatment better at ED 0.608 0.277

No charge for lab and radiology tests or medicines at

ED

0.926

No charge to see doctor at ED 0.910

Health problem required immediate attention and too

urgent to wait

0.908


Health problem too serious for a PMC 0.908

Wanted second opinion 0.782

Family traditionally uses ED for health care 0.737

Came to emergency to avoid difficulties getting

referral from PMC

0.603

Prefer ED as can attend whenever want 0.291 -0.254 0.473

Cronbach’s alpha .638 .865 .845 .591

As also indicated in Table 5.13, Cronbach’s Alpha scores were in the .500–.900 range,

acceptable for scales in the process of development. Based on this multi-factorial solution,

average scores were computed for use in the next section (Inferential statistics).

Concepts of quality in ED services 5.3.2

Single factor EFA

A single-factor EFA produced a solution where all 11 items loaded on a single factor at

levels in excess of .300, and the solution explained 62% of the cumulative variance. Also,

KMO=.834, and the significant p value for the Bartlett’s test of sphericity (p<.001) indicates

that items are correlated at non-zero levels.

Table 5.14: Component matrix with nine reasons for attending an ED


Expertise and efficiency of ED staff 0.849

Availability of adequate test facilities 0.848

Use of modern technology in providing health services in ED 0.835

Short waiting times throughout process of treatment in ED 0.831

Serve maximum number of patients possible 0.828

Optimal utilisation of available resources 0.807

Minimising proportion of diseases, mortality & disability within society 0.804

Respect for patients 0.762

Error free treatment and diagnosis 0.719

Minimise unnecessary tests and diagnosis 0.715

Accurate diagnosis and proper treatment 0.645


The Cronbach’s Alpha score was in the highly acceptable .900–1.00 range. Based on

this single-factor solution, average scores were computed for use in the next section

(inferential statistics).


Multi-factor EFA

The multi-factor EFA produced a two-factor solution after excluding six items. The

two-factor solution retained five of the 11 items and explained 81.7% of the cumulative

variance. Also, KMO=.653, and the significant p value for the Bartlett’s test of sphericity

(p<.001) indicates that items are correlated at non-zero levels.

Table 5.15: Rotated component matrix with five concepts of quality items

Rotated Component Matrix Quicker

service

Better

service

Short waiting times throughout process of treatment in ED 0.873 0.283

Error free treatment and diagnosis 0.855 0.267

Minimise unnecessary tests and diagnosis 0.842

Accurate diagnosis and proper treatment 0.914

Respect for patients 0.284 0.874

Cronbach’s alpha .861 .824

As indicated in the above table, the five items loaded onto two factors, with three items

loading on the factor, quicker service, and another two items loading on the factor, better

service, where all items loaded at levels in excess of .300, consistent with these five items

expressing two simple and distinct factors. Cronbach’s Alpha scores were in the acceptable

.800–.900 range. Based on this multi-factorial solution, average scores were computed for

use in the next section (inferential statistics).

Internal and external environment 5.3.3

Single factor EFA

Five of the six items loaded on a single factor at levels in excess of .300 (clarity of

signboards did not load at sufficient levels), and the solution explained 46% of the

cumulative variance. Also, KMO=.748, and the significant p value for the Bartlett’s test of

sphericity (p<.001) indicates that items are correlated at non-zero levels.

Table 5.16: Component matrix with five environment items


Layout of ED 0.827

Comfort of waiting area 0.821

Quiet location 0.796

ED accessibility 0.623


Cleanness of ED 0.614


The Cronbach’s Alpha score was in the acceptable .700–.800 range. Based on this

single-factor solution, average scores were computed for use in the next section (inferential

statistics).

Multi-factor EFA

The multi-factor EFA also produced a single-factor solution after excluding three items.

The single-factor solution retains three of the six items and explains 49.5% of the cumulative

variance. As stated previously, in the KMO test, values of .800–1.00 are desirable and scores

above .500 are consistent with these items being factorable (KMO=.615). The other,

Bartlett’s test of sphericity, reports on the extent to which item correlations are 1, 0, versus a

broader range of values. Here, the significant p value indicates that items are correlated at

non-zero levels (p<.001).

Table 5.17: Component matrix with three external environment items

Component Matrix External environment

Quiet location 0.885

Comfort of waiting area 0.798

ED accessibility 0.734


The three items loaded onto a single factor, External environment, where all items

loaded at levels in excess of .300. The Cronbach’s Alpha score was in the .500–.600 range,

minimally acceptable for a scale in the process of development. Based on this multi-factorial

solution, average scores were computed for use in the next section (inferential statistics).

Systems and work procedures 5.3.4

Single factor EFA

In a single-factor EFA, all eight items loaded on a single factor at levels in excess of

.300, and the solution explained 50.4% of the cumulative variance. KMO=.835, and the

significant p value for the Bartlett’s test of sphericity (p<.001) indicates that items are

correlated at non-zero levels.


Table 5.18: Component matrix with 8 systems and procedures items


Co-operation in health services provided to you 0.837

Treatment by reception of you and family 0.819

Communication among employees to facilitate health services 0.755

Doing right health services first time 0.745

Effectiveness of system in dealing with patient’s complaints 0.714

Speed of locating medical file 0.652

Waiting time during treatment in ED 0.596

Integrated services in ED 0.495


As shoiwn in Table 5.18, the Cronbach’s Alpha score was in the acceptable .800–.900

range. Based on this single-factor solution, average scores were computed for use in the next

section (inferential statistics).

Multi-factor EFA

Table 5.19: Component matrix with five efficient procedure items

Component Matrix Efficient procedures

Communication among employees to facilitate health services 0.785

Effectiveness of system in dealing with patient’s complaints 0.784

Waiting time during treatment in ED 0.703

Speed in locating medical file 0.672

Integrated services in ED 0.365

Cronbach’s Alpha .687

The multi-factor EFA also produced a single-factor solution after excluding three items.

The single-factor solution retains five of the eight items and explained 49.5% of the

cumulative variance. Also, KMO=.679, and the significant p value for the Bartlett’s test of

sphericity (p<.001) indicates that items are correlated at non-zero levels.

The Cronbach’s Alpha score was in the .600–.700 range, acceptable for a scale in the

process of development.

Medical services 5.3.5

Single factor EFA

A single-factor EFA produced a solution where all six items loaded on a single factor at

levels in excess of .300, and the solution explained 48.9% of the cumulative variance. Also,




Table 5.20: Component matrix with six medical services items


Doctor’s ability in prescribing proper treatment 0.892

Doctor’s use of understandable terms when discussing case 0.824

Accuracy of doctor’s diagnosis 0.800

Doctor’s explanation about method of using medication 0.648

Doctors spent adequate time to discuss case 0.468

Waiting time before being diagnosed by doctor 0.426


The Cronbach’s Alpha score was in the .700–.800 range, which is considered

acceptable for a scale in the process of development.

Multi-factor EFA

The multi-factor EFA produced a two-factor solution that retains all six items and

explained 70% of the cumulative variance. Also, KMO=.707, and the significant p value for

the Bartlett’s test of sphericity (p<.001) indicates that items are correlated at non-zero levels.

Table 5.21: Component matrix with six medical services items

Rotated Component Matrix Doctor’s

capability

Doctor’s use

of time

Doctor’s ability in prescribing proper treatment 0.831 0.329

Accuracy of doctor’s diagnosis 0.812

Doctor’s explanation about method of using medication 0.800

Doctor’s use of understandable terms when discussing case 0.788 0.260

Doctors spent adequate time to discuss case 0.830

Waiting time before being diagnosed by doctor 0.806


Six items loaded onto two factors, with four of the six items loaded onto the factor,

doctor’s capability, and another two items loading onto the factor, doctor’s use of time, where

all items loaded at levels in excess of .300 and Cronbach’s Alpha scores were in the .500–

.900 range.


Medical support services 5.3.6

Single factor EFA

A single-factor EFA produced a solution where all 13 items loaded on a single factor at

levels in excess of .300, and the solution explained 32% of the cumulative variance. Also,



Table 5.22: Component matrix with 13 medical support service items


Explanation of necessary procedures prior to radiology 0.727

Accuracy of test report 0.722

Ability of nurses to do right thing 0.637

Explanation of necessary procedures before lab examination 0.633

Nurses have done required medical check up 0.631

Obtaining radiology results at promised time 0.62

Accuracy of radiology report 0.568

Skills of lab technicians in taking required specimen 0.562

Obtaining lab results at promised time 0.496

Skills of radiology technicians in making required radiology 0.462

Waiting time before examination by nurse 0.437

Availability of prescribed medicine in ED pharmacy 0.368

Pharmacist explanation about use of medicine 0.332


As indicated in Table 5.22, the Cronbach’s Alpha score was in the acceptable .800–

.900 range. Based on this single-factor solution, average scores were computed for use in the

next section (inferential statistics).

Multi-factor EFA

The multi-factor EFA produced a two-factor solution that retains four of the 13 items

and explains 75.6% of the cumulative variance. Also, KMO=.535, and the significant p value

for the Bartlett’s test of sphericity (p<.001) indicates that items are correlated at non-zero

levels.

Table 5.23: Rotated component matrix with six medical services items

Rotated Component Matrix Radiology services Laboratory services

Skills of radiology technicians in required radiology 0.883

Accuracy of radiology report 0.869

Skills of lab technicians in taking required specimen

0.864


Explanation of necessary procedures before lab

examination 0.839


As indicated in Table 5.23, the four items loaded onto two factors, with two of the four

items loading onto the factor, radiology services, and another two items loading onto the

factor, laboratory services, where all items loaded at levels in excess of .300. Cronbach’s

Alpha scores were in the .600–.800 range, acceptable for a scale in the process of

development. Based on this multi-factorial solution, average scores were computed for use in

the next section (inferential statistics).

Long Waiting time 5.3.7

Single factor EFA

Five of the six items loaded on a single factor at levels in excess of .300, and the

solution explained 53.7% of the cumulative variance. Also, KMO=.777, and the significant p

value for the Bartlett’s test of sphericity (p<.001) indicates that items are correlated at non-

zero levels.

Table 5.24: Component matrix with five waiting time problem items


Waiting time before diagnosing by doctor was long 0.862

Waiting time before examination by nurse was long 0.844

Waiting time at reception was long 0.758

Waiting time during laboratory procedures was long 0.754

Waiting time during radiology procedures was long 0.738


The Cronbach’s Alpha score was high and in the range of .800–.900.

Multi-factor EFA

Table 5.25: Component matrix with three waiting time problem items

Component Matrix Primary care waiting times

Waiting time before examination by nurse was long 0.917

Waiting time before diagnosing by doctor was long 0.898

Waiting time at reception was long 0.818



The multi-factor EFA produced a single-factor solution that retains three of the six

items and explains 77% of the cumulative variance. Also, KMO=.691, and the significant p

value for the Bartlett’s test of sphericity (p<.001) indicates that items are correlated at non-

zero levels. Cronbach’s Alpha score was in the acceptable .800–.900 interval. Based on this

multi-factorial solution, average scores were computed for use in the next section (inferential

statistics).

Medical service problems 5.3.8

Single factor EFA

Table 5.26: Component matrix with four medical service problem items


Some medical examinations not available in ED 0.786

The bed capacity at ED is not enough 0.727

The medical devices sometimes did not function 0.643

Some medicines not available in ED pharmacy 0.630


All four items loaded on a single factor at levels in excess of .300, and the solution

explained 49% of the cumulative variance. And with KMO=.566, and the significant p value

for the Bartlett’s test of sphericity (p<.001). Cronbach’s Alpha score was in the .600–.700

range, acceptable for a scale in the process of development. Based on this single-factor

solution, average scores were computed for use in the next section (inferential statistics).

Multi-factor EFA

The multi-factor EFA produced a two-factor solution that retains all four items and

explains 77% of the cumulative variance. Also, KMO=.566, and the significant p value for

the Bartlett’s test of sphericity (p<.001) indicates that items are correlated at non-zero levels.

Table 5.27: Rotated component matrix with four medical service problem items

Rotated Component Matrix Treatment problems Resource problems

The medical devices sometimes did not function 0.899

Some medical examinations not available in ED 0.838 0.265

Some medicines not available in ED pharmacy

0.867

The bed capacity at ED is not enough

0.839



As indicated in the above table, all four items loaded onto two factors, with two items

loading on the factor, treatment problems, and the other two items loading on the factor,

resource problems, where all items loaded at levels in excess of .300. Cronbach’s Alpha

scores were in the .600–.800 interval, acceptable for scales in the process of development.

Based on this multi-factorial solution, average scores were computed for use in the next

section (inferential statistics).

ED staff care 5.3.9

Single factor EFA

A single-factor EFA produced a solution where all three items loaded on a single factor

at levels in excess of .300, and the solution explained 62.2% of the cumulative variance.

Also, KMO=.636, and the significant p value for the Bartlett’s test of sphericity (p<.001)

indicates that items are correlated at non-zero levels.

Table 5.28: Component matrix with three ED staff health care items


Nurse did not take enough time for primary examination 0.846

Doctor did not give comprehensive consultation and diagnosis 0.800

Sometime necessary staff not available 0.716


As indicated in Table 5.28, the Cronbach’s Alpha score was in the .600–.700 range,

acceptable for scales in process of development. Based on this single-factor solution, average

scores were computed for use in the next section (inferential statistics).

Multi-factor EFA

The multi-factor EFA produced a single-factor solution that retained all three items and

produced outcomes identical to those for the single-factor solution. Based on this, average

scores were only computed for the single-factor solution.

INFERENTIAL STATISTICS 5.4

Nonparametric correlation matrices (Spearman’s Rho) indicate that none of the nine

single scale solutions were collinearly correlated (.9–1.0), nor were any of the multifactor

solutions. This gives some assurance that it is in order to make the assumption that single and

multi-factorial solutions reported earlier, and the scales based on them, represent distinct

aspects of the participant responses.


Given that this survey includes two demographic variables (gender, educational level),

it makes some sense to enter these into MANOVAs and ANOVAs as independent variables,

with successive sets of scale scores (multi-factor or single-factor) entered as the dependent

variables. Also, given that both single-factor and multi-factor solutions are available in a

number of cases, the single-factor solution is to be preferred, based on its use of the greatest

number of items. However, the multifactor solutions provide a fall-back position should the

preferred analysis fail to identify significant effects or interactions.

Reasons for attending ED 5.4.1

Table 5.29: ANOVA with reasons scale score as DV and with gender and educational level as IVs

Source Type III SS df_trtmt df_error MS F Sig.

Gender 3.443 1 101 3.443 4.894 0.029

Education Level (4gps) 1.646 3 101 0.549 0.780 0.508

Gender * Education Level (4gps) 1.537 3 101 0.512 0.728 0.538

As indicated in Table 5.29, the main effect for gender was significant in that males

were significantly more positive in their responses than were females.

Concept of quality in ED services 5.4.2

Table 5.30: ANOVA with concepts of quality scale score as DV and gender and educational level as IVs


Gender 0.298 1 104 0.298 0.451 0.503



As indicated in Table 5.30, the main effects for gender and educational level and the

interaction between these were uniformly non-significant. A follow-up MANOVA with the

two subscale scores as the DV and with gender and educational level as IVs also did not

result in significant main effects or interactions.

Experience with internal and external environment in ED 5.4.3

Table 5.31: ANOVA with environment scale score as DV and gender and educational level as IVs


Gender 0.070 1 110 0.070 0.093 0.761





interaction between these were uniformly non-significant. A follow-up ANOVA with the

concept of external environment as the DV and with gender and educational level as IVs also

did not result in significant main effects or interactions.

Experience with systems and procedures in ED 5.4.4

Table 5.32: ANOVA with systems and procedures scale score as DV and gender and educational level as IVs


Gender 0.376 1 107 0.376 0.637 0.426



As indicated in Table 5.32, the main effects for educational level and the interaction

between educational level and gender were both significant.

Figure 5.2: Main effect of educational level on systems and procedures scale score

(Standard error shown)

As illustrated by Figure 5.2, participants with diploma levels of education were

significantly less positive in their average response to the systems and procedures scale score


than those with primary/intermediary or secondary education and responses verged on

significance for those with BA/higher degrees.

While the main effect for gender is non-significant (Table 5.32), as illustrated in Figure

5.3, male-female differences vary significantly at differing educational levels (outcomes for

ANOVAs examining gender differences with file partitioned by level of education) such that

males with primary/intermediate levels of education levels obtained significantly higher

(more positive) scores than females at this level on this scale, while females with diplomas

obtained higher (more positive) scores than did males at this level on this scale, and females

with BAs/higher degrees obtained significantly higher scores (more positive) scores than did

males at this level on this scale.

Figure 5.3: Interaction between gender and educational level in relation to systems and

procedures scale score (standard error shown)

Experience with medical services in ED 5.4.5

Table 5.33: ANOVA with medical services scale score as DV and gender and educational level as IVs


Gender 0.561 1 108 0.561 0.981 0.324





interaction between these were all non-significant. A follow-up MANOVA with the two

subscale scores as the DV and with gender and educational level as IVs also did not result in

significant main effects or interactions.

Experience with medical support services in ED 5.4.6

Table 5.34: ANOVA with medical support services scale score as DV and gender and educational level as IVs


Gender 2.808 1 106 2.808 9.474 0.003



As indicated in Table 5.34, the main effects for gender and the interaction between

gender and education level were both significant. The main effect for gender was such that

females registered higher (more positive) scores on the medical support services scale.

While the main effect for educational level is non-significant (Table 5.34), as illustrated

in Figure 5.4, male-female differences vary significantly at differing educational levels

(outcomes for ANOVAs examining gender differences with file partitioned by level of

education) such that females with diplomas obtained higher (more positive) scores than did

males at this level on this scale and females with BAs/higher degrees also obtained

significantly higher scores (more positive) scores than did males at this level on this scale.


Figure 5.4: Interaction between gender and educational level in relation to medical

support services scale score (standard error shown)

Problems with waiting time in ED 5.4.7

Figure 5.5: Main effect for educational level in relation to waiting time problems scale

(standard error shown)


As illustrated in Figure 5.5, participants with BA or higher educational qualifications

were significantly less likely than participants with diplomas or primary/intermediate level

qualifications to agree that waiting times were a problem.

Table 5.35: ANOVA with waiting times problems scale score as DV and gender and educational level as IVs


Gender 2.662 1 106 2.662 3.264 0.074



As indicated in Table 5.35, while the effect for gender trended towards significance and

the interaction between gender and educational level was non-significant, the main effect for

educational level was significant.

Problems with medical services in ED 5.4.8

Table 5.36: ANOVA with medical service problems scale score as DV and gender and educational level as IVs


Gender 1.064 1 110 1.064 2.165 0.144




interaction between these were uniformly non-significant. A follow-up MANOVA with the

two subscale scores as the DV and with gender and educational level as IVs also did not

result in significant main effects or interactions.

Problems with care by ED staff 5.4.9

Table 5.37: ANOVA with care by ED staff scale score as DV and gender and educational level as IVs


Gender 1.580 1 109 1.580 1.950 0.165




interaction between these were uniformly non-significant. Given that the multi-factorial

solution was identical to the single-factor solution, a follow-up ANOVA was not undertaken.


STRUCTURAL EQUATION MODELLING 5.5

In our research study, there were no hypotheses to test. We just continued with some

advanced statistical analysis to investigate and explore some relationships between the

suggested variables. Structural Equation Modelling (SEM) has become one of the key

choices for researchers as a method of data analysis because it has a number of advantages

(Hair et al. 2010). One of the benefits of SEM is that it outlines the fit of a model to describe

the entire set of relationships. Also, it has the ability to distinguish between direct and

indirect relationships amongst the variables. To perform the SEM, several computer

programs can be used such as: AMOS, RAMONA, SEPATH, and others (Hair et al. 2010).

The question here was the extent to which two exogenous variables (reasons for

attending the ED and concepts of quality) directly or indirectly influenced four outcome

measures of satisfaction with the ED health services (environment, systems and work

procedures, medical services, medical support services). Here one might expect the level of

satisfaction to be mediated or moderated by three measures of problematic experiences with

the ED (waiting time problems, medical service problems, and staff care problems).

In the present study, the method adopted was as follows:

An initial almost saturated model (bidirectional pathways not included) was

constructed and used as a starting point for a regression based imputation to

replace missing values (AMOS Imputation).

The outcome from that saturated model with missing values replaced was used to

examine multivariate outliers (Mahalanobis Distances: non-significant and without

gaps in the values of any consequence) and univariate outliers (all nine scales were

acceptably distributed); regression values (11 non-significant unidirectional

associations removed from model); and covariances (non-significant bidirectional

association between the two exogenous variables fixed as per regression weight).

At this point, Modification Indices were examined and based on these, the

bidirectional associations between the four outcome measures and between the

three mediating/moderating measures were added.

Based on the outcomes from this modified model, one further unidirectional

association (previously removed) was reinstated.


According to Hair et al. (2010), items with a low factor loading can be dropped from

the model without serious consequences as long as a construct retains a sufficient number of

indicators. Each construct should be measured by at least three indicators (Hair et al. 2010).

The resulting measurement model contains one fixed non-significant association, but all other

associations (unidirectional, bidirectional) are significant.

Because there is no single statistical significance test that identifies a correct model fit

(Rivers and Glover 2008), it is necessary to evaluate model fit on the basis of multiple fit

indices. Hair et al. (2010), introduced a variety of fit indices with an acceptable range which

can be used as a guideline for structural equation modelling to help avoid making such errors.

The most commonly reported multi-fit indices are: CFI, GFI, NFI and NNFI ; Chi-square

test, RMSEA, CFI and SRMR . Al Solaiman (2014), in his research study has summarised

the multi-fit indices with level ranges and fits as shown in Table 5.38.

Table 5.38: The multi-fit indices with level ranges and fits


In terms of fit values, the final model (Figure 5.6) better meets the rigorous criteria for

such models. The overall Chi-Square is non-significant (p=.200) and the Chi-square value

divided by the degrees of freedom approximates one (1.297).

AMOS provides a series of absolute fit indexes that directly assess how well a priori

model reproduces the sample data, including:

The root mean square residual (RMR), which compares the sample variances &

covariances from their estimates, with values in the 0.00-0.05 ranges considered

acceptable (RMR=. 05).

The standardised root mean square residual (SRMR), with values in the 0.00-0.08

range considered acceptable (SRMR=.072).

The Goodness of fit index (GFI), with values in the .95-1.00 range considered

acceptable (GFI=.968).

AMOS provides a series of baseline comparisons that compare the given model with an

alternative model, including:

Comparative Fit Index (CFI), with values in the .95-1.00 range considered

acceptable (CFI=.993).

Normed Fit Index (NFI), with values in the .95-1.00 range considered acceptable

(NFI=.970).

AMOS provides a series of parsimony-adjusted measures that penalise for lack of

parsimony, including:

The root mean square of approximation (RMSEA), a measure that takes model

complexity into account, with values in the 0.00-0.08 range considered acceptable

(RMSEA=.05) Also, the associated PCLOSE value is non-significant (p=.453), a

desirable outcome.

For more details regarding the procedures of SEM, see Appendix K.


Figure 5.6: Measurement model with all significant pathways (unidirectional, bidirectional) between the nine ED related scale scores


Table 5.39: Regression Weights

Estimate S.E. C.R. P

WaitingTimesSingleFactorAv <--- ConceptsOfQualityAv .308 .089 3.471 ***

MedicalServicesPblmSingleFa

ctorSolnAv <--- Reasons9Av .147 .058 2.534 .011

MedicalServicesPblmSingleFa

ctorSolnAv <--- ConceptsOfQualityAv -.237 .062 -3.808 ***

EnvironmentAv <--- ConceptsOfQualityAv -.357 .065 -5.501 ***

SystemsProceduresAv <--- WaitingTimesSingleFacto

rAv .160 .051 3.165 .002

SystemsProceduresAv <--- MedicalServicesPblmSing

leFactorSolnAv -.375 .078 -4.798 ***

MedicalServicesAv <--- MedicalServicesPblmSing

leFactorSolnAv -.327 .082 -4.006 ***

MedicalSupportServicesAv <--- MedicalServicesPblmSing

leFactorSolnAv -.166 .068 -2.443 .015

MedicalSupportServicesAv <--- EDStaffHlthCareAv -.351 .052 -6.683 ***

MedicalServicesAv <--- EDStaffHlthCareAv -.479 .067 -7.115 ***

EnvironmentAv <--- EDStaffHlthCareAv -.556 .064 -8.698 ***

SystemsProceduresAv <--- EDStaffHlthCareAv -.539 .066 -8.128 ***

MedicalServicesAv <--- WaitingTimesSingleFacto

rAv .136 .049 2.788 .005

Table5.40: Standardized Regression Weights

Estimate

WaitingTimesSingleFactor

Av <--- ConceptsOfQualityAv .264

MedicalServicesPblmSingl

eFactorSolnAv <--- Reasons9Av .174

MedicalServicesPblmSingl

eFactorSolnAv <--- ConceptsOfQualityAv -.274

EnvironmentAv <--- ConceptsOfQualityAv -.332

SystemsProceduresAv <--- WaitingTimesSingleFactorAv .184

SystemsProceduresAv <--- MedicalServicesPblmSingleFactor

SolnAv -.318

MedicalServicesAv <--- MedicalServicesPblmSingleFactor

SolnAv -.294

MedicalSupportServicesAv <--- MedicalServicesPblmSingleFactor

SolnAv -.194

MedicalSupportServicesAv <--- EDStaffHlthCareAv -.540

MedicalServicesAv <--- EDStaffHlthCareAv -.568


EnvironmentAv <--- EDStaffHlthCareAv -.588

SystemsProceduresAv <--- EDStaffHlthCareAv -.603

MedicalServicesAv <--- WaitingTimesSingleFactorAv .165

INTERPRETATION OF THE FINAL MODEL 5.6

How do these relationships explain the hospital situation? The questionnaire

aimed to investigate the current ED system in Asir Hospital and collect the patient’s

perceptions to identify the most significant patient flow issues in emergency

department. The current hospital situation can be described in this model based on

thirteen relationships, as shown in Figure 5.6.

The significant direct unidirectional pathways from concepts of quality in ED

services scale include the following:

Negative association with scores on Environment scale

Positive association with scores on waiting times problem scale

Negative association with scores on medical service problems scale

As shown in Table 5.4, there is a clear positive relationship with scores on

waiting times. Participants were likely to agree that a short waiting time throughout

the process of treatment in the ED is a concept of quality in ED services. Also, factor

analysis shows three items loading on the factor of quicker service, with all items

loaded at levels in excess of .300, as shown in Table 5.15. Cronbach’s Alpha scores

were in the acceptable .800–.900 range.

In addition, there is a significant direct unidirectional pathway from reasons for

attending ED scale, including the following:

Positive association with scores on medical services problems scale.

As shown in Table 5.3 and Table 5.13, patients were visiting ED in terms of

four factors, which are related to: ease of access, financial ease, better treatment and

preferred venue. In the present study, 51.3% of patient participants indicated that

better treatment is an important to a very important reason for visiting the emergency

department. In addition, 40.7% of participants indicated that financial ease is a very

important reason to visit the emergency department. Also, 36.6% of patients

indicated that ease of access is an important to a very important reason to visit the


emergency department. Patients considered medical service problems in terms of two

main factors which are related to treatment problems and resources problems, as

shown in Table 5.27.

Furthermore, the significant direct unidirectional pathways from waiting time

problems scale include the following:

Positive association with scores on systems and procedures scale.

Positive association with scores on medical services scale.

As shown in Table 5.

The significant direct unidirectional pathways from the medical service


Negative association with scores on systems and procedures scale.

Negative association with medical services scale.

Negative association with medical support services scale.

Finally, the significant direct unidirectional pathways from medical service


Negative association with scores on environment scale.

Negative association with scores on systems and procedures scale.

Negative association with scores on medical services scale.

Negative association with scores on medical support services scale.

In the SEM, we can see that quality of life negatively predicts environment scale, but

we cannot see negative correlations between environmental scale and ED services.

Where one scale correlates negatively or regresses negatively on another, then the

methodological reason is that higher scores on one scale are in opposite direction to

higher scores on another scale.

Based on the lack of both direct and indirect effects for either of the exogenous

variables, it is not possible to compare the relative strength of direct vs. indirect

effects for these variables (the unidirectional effects are either direct or indirect but

not both). It follows then that the three measures of ED related problems (waiting


times, medical services, staff care) mediate rather than moderate the effects of the

two exogenous measures.

For example:

Waiting time problem scores mediate the effect of concepts of quality

scores on the outcome scores for systems and procedures and on medical

services.

Medical service problem scores mediate the effect of reasons of attending

ED on the outcome scores of systems and procedures, medical services,

and medical support services.

As shown on Figure 5.6, there is a clear positive relationship between scores on

waiting times and concept of quality in ED services. Patients were likely to

support that a short waiting time throughout the process of treatment in the

ED is a concept of quality in ED services as shown in Table 5.4. In

addition, there is a positive relationship with scores on medical services

problem scale and reasons for visiting ED scale as shown in Figure 5.6.

Ease of access, financial ease, better treatment and preferred venue are the

main factors which are related to reasons for visiting ED as shown in

Table 5.3 and Table 5.13. Also, treatment problems and resources

problems are two main factors which have been considered by participants

in terms medical service problem, as shown in Table 5.27.

Furthermore, there is a positive relationship between waiting time with system

and work procedures. As shown in Table 5.19, it is clear that when the ED

system has efficient procedures including good communication with

employees, short waiting time during ED treatments and high response to

patients’ complaints, the problem of waiting time in ED will be solved.

Also, a positive relationship in scores of waiting time problems scale and

medical services scale if found. That means when the capability of doctors

is increased then waiting times in ED will be decreased.

Summary

A total of 120 participants completed a patient survey sampling their

Emergency Department (ED) experiences. Of the 120, 55% (N=66) were male. Of


the 120, almost half (49%, N=58) had completed a BA or higher degree, and another

1/3 (33.6%) had completed primary, intermediate or secondary school.

Participants were asked to express level of importance, agreement or

satisfaction in relation to nine elements (reasons for attending ED, concept of quality

in ED services, ED environment, ED systems and work procedures, ED medical

services, ED medical support services, problems with waiting times, problems with

medical services, problems with ED staff care). When asked their reasons for

attending an ED, they were most likely to list the lack of charges for laboratory and

radiology tests or medicines at the ED as most important. When asked about

concepts of quality in relation to an ED, they were most likely to agree about respect

for patients and accurate diagnosis and treatment. When asked about the internal and

external environment in an ED, they were most likely to be satisfied by the cleanness

of the ED. When asked about systems and work procedures in an ED, they were most

likely to be satisfied by the treatment of them and their family by reception. When

asked about medical services in an ED, they were most likely to be satisfied by the

doctor’s ability to prescribe proper treatment. When asked about medical support

services, they were most likely to be satisfied by the pharmacist’s explanation about

the use of medicines. Participants were also asked, based on their experience, to rate

level of agreement about problems experienced in an ED. When asked about waiting

time problems, they were most likely to agree that the waiting time before diagnosis

by the doctor was too long. When asked about medical service problems, they were

most likely to agree that the bed capacity at the ED was not enough. When asked

about ED staff care problems, they were most likely to agree that sometimes staff

were not available.

Exploratory factor analysis (EFA) was conducted to identify single and multi-

factor clusters for the items comprising each of the nine elements. After computing

scale scores based on these factor solutions, ANOVAs and MANOVAs were

conducted to examine the effect of the demographic profile of participants on

responses to specific scales or sub-scales. Results of interest here include: males

were more likely to rate reasons for attending the ED as important than were

females; participants with primary, intermediate or secondary level education were

more likely to be satisfied with systems and procedures than females at their level,

but females with BAs or higher education degrees were more likely to be satisfied


with systems and procedures than males at their level; females were more likely than

males to be satisfied with medical support services than males; females with

diplomas or BAs or higher degrees were more likely to be satisfied with medical

support services than males at their level; participants with BAs or higher education

degrees were less likely than those at lower education levels to agree that waiting

times were a problem.

Structural equation modelling (SEM) was used to examine associations

between the nine identified scales via a measurement model in which two exogenous

variables (reasons for attending an ED, concepts of quality in ED services) predicted

satisfaction ratings for four outcome variables (environment, systems and work

procedures, medical services, medical support services). Another three variables

where participants rated ED problems (waiting times, medical services, care of ED

staff) were entered as moderating or mediating variables. After excluding non-

significant linkages, the model converged with highly satisfactory fit values in which

associations between exogenous and outcome variables typically were explained by

one of the three intermediary variables (especially waiting times and medical

services).

Chapter 6: Patient Flow Problems: Voice of the Process and Voice of the internal Customer 123

Patient Flow Problems: Voice of the Chapter 6:

Process and Voice of the internal

Customer

VOICE OF THE INTERNAL CUSTOMER (ED STAFF) 6.1

In this chapter, two different approaches to collecting the voice of emergency

department staff are reported; an exploratory questionnaire survey and A3 Problem Solving

Sheets were used.

DESCRIPTIVE ANALYSIS 6.2

As discussed in Chapter 3, this study employed purposeful sampling to recruit

participants form emergency department. About 25 out of 28 of ED staff participated to

complete an explanatory questionnaire regarding their experience in ED, their level of

satisfaction with bed capacity and medical equipment, and their explanations of the causes of

long waiting times in ED. The next section will present some important findings from the ED

staff questionnaire survey.

Years of ED staff’s Experience 6.2.1

Figure 6.1: ED Staff’s Experience

124 Chapter 6: Patient Flow Problems: Voice of the Process and Voice of the internal Customer

One of the most significant factors in the quality of services is the staff’s experience.

As shown in Figure 6.1, 32% of participants have less than one year of experience at ED,

while 36% have between 1-3 years’ experience in ED, and 32% of the ED staff have more

than 3 years’ experience.

ED Staff’s Satisfaction with Bed Capacity 6.2.2

As shown in the following chart (Figure 6.2), 32% of the ED employees are somewhat

dissatisfied with the bed capacity at ED, while 28% of them are very satisfied with capacity.

Figure 6.2: ED Staff’s Satisfaction with Bed Capacity

ED Staff’s Satisfaction with Medical Equipment 6.2.3

As presented in Figure 6.3, 48% of ED employees are somewhat satisfied with medical

equipment that are available at ED in Asir Central Hospital. However, 16% of the

participants are somewhat dissatisfied with these instruments and 4% of participants are very

dissatisfied.


Figure 6.3: ED Staff’s Satisfaction with Medical Equipment

Causes of Waiting Time from ED Staff Perspectives 6.2.4

Overcrowding by the Family of Patients

The responses of the ED staff reveal that 56% of participants indicated that

overcrowding by the families of patients is a major cause of long waiting times in ED. Figure

6.4 shows the responses of ED staff to this factor.

Figure 6.4: ED staff’s perceptions of overcrowding by families of patients at ED

Shortage of ED Nurses

52% of ED participants specified shortage of nurses in ED as another major cause of

long waiting times in the patient journey in ED. Also, 20% classified this factor as a moderate

cause for long waiting times in ED. Figure 6.5 shows the participants’ responses to the

problem of the shortage of ED nurses.

4%

16%

12%

48%

20%

Medical Equipment

Very dissatisfied

Somewhat dissatisfied

Neutral

Somewhat Satisfied

Very Satisfied

Not acause

Sever MajorModerat

eMinor

Frequency 1 2 14 5 3

Percent 4 8 56 20 12

0

10

20

30

40

50

60

Overcrowding By Family of Patients


Figure 6.5: Staff Perceptions of Shortage of Nurses at ED

Patients’ Awareness

Figure 6.6 shows that 40% of ED staff consider that patients’ lack of understanding

about the differences between urgent and normal cases represents a major cause for long

waiting times at ED.

Figure 6.6: Staff Perceptions of Patients’ Awareness at ED

Shortage of ED Physicians

As shown in Figure 6.7, 48% of ED staff indicated that a shortage of physicians is a

moderate cause for long waiting times at the emergency department, while 28% of them

believe that this is a major reason.

52%

28%

20%

Shortage of Nurses

Major

Moderate

Minor

Severe Major Moderate Minor

Frequency 5 10 7 3

Percent 20 40 28 12

0

5

10

15

20

25

30

35

40

45

Patients' Awareness


Figure 6.7: Staff Perceptions of Shortage of Physicians at ED

Insufficient Facilities

Finally, 48% of ED staff indicated that insufficient facilities are a moderate cause for

long waiting times at ED in Asir Central Hospital, while 20% of the emergency department’s

staff say that insufficient facilities are a major cause, as shown in Figure 6.8.

Figure 6.8: Staff perceptions of Insufficient Facilities at ED

A3 PROBLEM SOLVING SHEETS 6.3

As mentioned previously, this study identified ED staff as an internal customer. This

section discusses and answers the second question of this research study: how can wastes

0

10

20

30

40

50

Sever Major Moderate

Minor

Frequency 3 7 12 3

Percent 12 28 48 12

Shortage of Physicians

0 10 20 30 40 50 60

Not a cause

Severe

Major

Moderate

Minor

Not a cause Severe Major Moderate Minor

Percent 16 12 20 48 4

Frequency 4 3 5 12 1

Insufficient Facilities


affecting patient flow and quality of service in ED be identified by using Lean strategy and

Six Sigma techniques? Following the steps outlined in LSS, in Stage 1 (Define/Identify

Value), the voice of the customer (VOC)/the customer value was identified. A3 Problem

Solving Sheets were used to collect data from ED staff. It was a collaborative participation

between the researcher and ED staff. As a part of an action research project, the A3 process

teaches the participants how to identify problems through the observation process. Then, a

value stream map is drawn, which helps to identify patient flow problems through

visualisation. Once a value stream map is completed, the root cause of a problem by using the

‘five why’s’ process can be identified. Then, a cause and effect diagram is drawn by

researcher.

The following section presents the analysis of A3 problem solving sheets filled out by

staff participants in ED. The A3 report addresses a specific problem in a systematic fashion.

Problem Identification 6.3.1

ED staff who participated in this research indicated that overcrowding is a major

problem which directly impacts on patient flow in the emergency department in Asir Central

Hospital in Saudi Arabia.

Importance of the Problem: How it affects hospital’s goals or values? 6.3.2

EDs all over the world are high risk and high stress environments. If capacity is

exceeded, there is a high possibility for errors. Overcrowding has a profound impact on care

quality, cost of service and community trust and confidence. The patients might also leave

without being seen.

Because of overcrowding, customer privacy, staff honesty and staff loyalty cannot be

properly maintained, according to the emergency department staff in Asir Central Hospital.

Thus, it cannot become the best organised and professional ED if it cannot provide patient-

centred care and ensure patient safety and ethical and effective care. The, patients will be

treated at other hospitals, which harms the reputation of ED quality healthcare services.

Current Conditions 6.3.3

To understand the current working system in the emergency department in terms of

case organisation, a current process flow diagram was developed, as presented in Figure 6.9.

Feedback from the ED staff was collected and utilised in developing this visualisation


diagram. The following blocks show the patient flow process and activities from the ED staff

perspectives:

Figure 6.9: Spaghetti diagram for current patient flow in Asir Central Hospital ED

Key Problems noted:

Based on the A3 problem solving sheet analysis, the following significant issues were

identified by ED staff:

A shortage in staff numbers with appropriate training and experience.

Non-organised infrastructure and non-available resources.

Lack of trained triage nurses.

A shortage in specialiy physicians who are working with the team to make daily

rounds in the emergency department.

Non-involvement of the specialty senior physicians in decision-making and continuity

of care.


Lack of inpatient bed capacity.

Lack of rules/guidelines or/and consenting treatment for admission and discharge

from ED.

Ineffective security system to control patients’ family relatives (visitors).

Lack of reliable, efficient, effective, patient-centred primary health centre (PHC) for

the patient. These problems are a result of miscommunication between the healthcare

provider and patients.

Poor community medicine services.

Lack of quality primary care.

Layout design.

Quantified measures of the extent of the problems:

With the non-availability of documented data the appropriate measures are as

following:

Throughput turnaround time of ED patients should be 360 minutes.

Turnaround time of boarded patients to IW/MW should be 48 hours.

Turnaround time of CT- BRDW report for patient should be 120 minutes.

Root Cause Analysis 6.3.4

Based on ED staff perspectives, a number of factors that cause ED overcrowding have

been identified, and can be classified into:

Staffing: ED staff participants mentioned that quality trained staff in ED are not

available because of the lack of job benefits in the emergency department (career safety). In

addition to that, financial empowering of ED staff is necessary to stimulate, motivate and

make them committed to the task. Also, enhanced rewards can help to increase the attraction

rate of ED staff. Staff suggested that a formal training program will be an easier way to

increase career safety and help them to attract and retain the residents. Furthermore, dynamic

groups and active nurses are needed who can help with their improved knowledge, skills and

attitude to deliver quality day-to-day emergency care. As well as, daily physicians’ rounds in

wards and the ED by the senior specialty doctor is a requirement to facilitate and break the


gridlock of overcrowding. However, senior ED trained staff are available and they must be

used efficiently.

Patients: Another key point which causes overcrowding from ED staff’s perspective is

the culture of patients and their family. As they mentioned, unreasonable expectations of the

patients’ family and community cannot be easily overlooked. Additionally, a high percentage

of patients who are attending an emergency department are categorised as non-urgent cases

and can be treated in primary care centres.

Resources: ED staff recognised that resources are a significant variable in

overcrowding. Bed capacity is one major problem that causes long waiting times for patient

admission. It is necessary that stocking and renewal of devices and equipment are provided

on time. Unorganised infrastructure and a complex ED layout design increase overcrowding.

Communication: ED staff recognised that miscommunication between physicians,

nurses, technicians, administration, the quality department and bed management is a really

big issue in any healthcare environment. There is a gap in the relationship between hospital

departments due to silos in the work setting. In addition, another avenue of

miscommunication has been raised between ED staff and patients — the misunderstanding of

some medical terms or process, which may exacerbate the overcrowding issue.

Education and training program: in the light of the ED staff’s perception of the

overcrowding issue, the lack of quality training and education for ED staff is one significant

factor that increases patient flow problems in the emergency department. This issue is a result

of quality department shortcomings in spreading the culture and knowledge of quality

management in hospital departments and the community as well.

Administration and management skills: The absence of appropriate documentation

and audits for patients’ data from entry to discharge undermines any plan for continuous

quality improvement. Indeed, time study and patient categorisation records are not tracked

during patient flow in the Asir emergency department. In fact, the rules and guidelines of

patient treatments are not applied during admission and discharge from the ED. In addition,

security personnel skills are very weak, which impacts on patient flow in the ED and causes

overcrowding, because there is no controlled management for workplace security.

Primary healthcare centres: The primary medical centres are suffering from poor

resources and shortage of trained staff. The quality of care at these centres does not satisfy


community expectations, thus patients tend to visit the emergency department, which causes

the ED to be overcrowded with non-urgent patients.

The next section will analyse the second phase of the survey, using A3 problem solving

sheets (improvement plan sheet). The improvement was considered from the perspective of

ED staff. In this section we will analyse the following headings: target condition, diagram of

how the proposed process will work, specific countermeasures noted, and measurable targets.

The remaining parts of the survey (Improvement Plan and Follow-Up) will be discussed in

separate sections particularly in the discussion section.

Target Condition 6.3.5

Diagram of how proposed process will work

Figure 6.10: Spaghetti diagram for proposed improved patient flow in Asir Central

Hospital ED

Specific countermeasures noted

In this study, the ED staff provided some valuable suggestions to improve the current

situation, as listed below:


Employ quality staff;

Upgrade well qualified senior physicians;

Decrease the throughput of ED turnaround time to 150-180 minutes from 240-360

minutes;

Decrease the boarding time of admitted patients on ICU/IMCU/, to 10-12 hours,

instead of 48-71 hours.

Measurable targets (quantity and time)

Based on ED staff viewpoints, there are many essential variables that should be

considered as measurable targets to improve patient flow in ED, such as:

Median time from ED attendance to ED departure for admission: aim: 150-180

minutes).

Door to doctor time in RR: aim: 15 minutes.

Door to CT scan and report time: aim: 45 minutes.

Door to admission time: aim: 60 minutes.

VOICE OF THE PROCESS 6.4

Efforts to reduce or eliminate non-value-added activities are the key point to improve

the system’s flow. Process mapping with value stream mapping and A3 problem solving

methods have become a favoured visualisation technique to model, evaluate and improve

work and process flow in the healthcare industry. Actually, emergency department crowding

has been identified as a challenge to the Ministry of Health in Saudi Arabia, especially at the

referral hospitals. Only a few studies have been conducted in this area in developing

countries, particularly in Saudi Arabia. This section reports the value stream mapping (VSM)

analysis based on observation of the current system, and exploration of the A3 problem

solving sheets used, as discussed in the previous section. As VSM is involved in all of the

process steps of patient flow in ED, both value-added and non-value-added activities are

analysed, using VSM and A3 problem solving sheets as visual tools to help see the hidden

waste and sources of waste. A Current State Map (CSM) is drawn to document how things

actually operate on the ED floor. Then, cause and effect diagram determines the findings

regarding reasons for overcrowding, which significantly affects patient flow in the ED. Then,


a future plan is proposed to design a lean process flow achieved through eliminating the root

causes of waste and through process improvements.

Observation and voice of the process 6.4.1

We followed two main steps to investigate the current state of patient flow in the ED.

1. Introduction of lean thinking to ED staff (education): Individual meetings were

held with the ED director, nurse supervisor and some of the ED staff (physicians,

nurses and technicians). Initially, the ED director gave a brief description of the ED,

introduced the researcher to ED staff and spoke about quality concepts and the

importance of the proposed research in terms of ED improvement in Asir Central

Hospital. Documents introducing Lean thinking and Six Sigma in health care and

proposed research details were sent to interested staff.

2. Observation process: The observation began with an initial tour with the nurse

supervisor to get a clear picture of the ED environment. The first step of the

observation was to define all activities and processes that take place throughout the

patient journey in the ED from entry to discharge. The nurse supervisor introduced the

researcher to other members of staff and asked them to be open and helpful during the

research process, providing any necessary information. Then, the researcher began to

inspect the current system, using paper and pen to sketch the whole process in the ED

and make notes, specifically on patient flow from entry to discharge (for patients

categorised under levels 3 and 4). Patients can be categorised into 5 levels, based on

the standard of triage levels. Our study focus on patients who categorised under level

3 and 4. The researcher then designed a time study sheet based on ED documentation

to record time spent by patients at different stages between registration and triage, and

another sheet to follow patient time from entry to discharge (see Appendices 1 and 2).

During this observation, the current ED layout was carefully analysed to determine the

impact of ED layout on patient flow.

Analysis of the emergency department in Asir Central Hospital 6.4.2

There are 15 beds in the intensive care unit (ICU), 35 in the intermediate care unit, 17

in the cardiac care unit, and 10 in the paediatric care unit. In total, the ED has 77 beds in

different units. Care is provided by an ICU-based team of critical care physicians, nurses,

pharmacists, respiratory therapists and other health professionals.


The functional analysis of the service delivery protocol, based on emergency

department current procedures, can be classified into the following areas of care delivery at

the Asir Central Hospital ED as shown in Figure 6.11.

1. Arrival

2. Registration/ Reception

3. Triage station

4. Critical care unit (Resuscitation rooms, RR), levels 1 and 2 who comes from

ambulance entrance

5. Intermediate care unit (Observation room, male and female), levels 3 and 4

6. Urgent room, level 5 in different area not near the ED

7. Diagnostic testing

8. Follow-up treatment

9. Patient’s discharge from the ED/admission into in-patient management department


Figure 6.11: Functional analysis of patient flow in the Asir Central Hospital emergency

department (ED)

Development of ED process flow map 6.4.3

Understanding the current situation in the ED process requires, first of all, an

understanding of how the process is perceived by different stakeholders (voice of the

customer), and how the process is currently performing (voice of the process) (Johnson and

Capasso 2012). An extremely useful first step in starting lean is the mapping of the process

using a process map (Figure 6.12). The amount of waste in the system can be assessed by

using VSM. The VSM documents the time in each step and quantifies the amount of value-

added and non-value-added (waste) time in each step.


Staff feedback and the A3 problem sheet 6.4.4

To understand ED staff perspectives, A3 problem sheets (investigation and

improvement sheets) were collected, to be integrated with observation in order to develop a

process map for the current system, based on the research observation and staff feedback. The

following phases were conducted:

Phase 1: Educate ED staff about lean concepts. In this stage, ED physicians, nurses

and technicians were given an introduction to lean strategies in the health care system

through individual and group meetings with the ED director and nurse supervisor.

Additionally, all staff involved received an email communication about lean strategy,

the A3 Problem Solving Sheet and VSM in health care.

In this phase, the researcher met with the ED director, the ED nurse supervisor, the

quality specialist, ED physicians, ED nurses and ED technicians to discuss the problems of

patient flow and how to solve these problems through lean concepts and methods.

Phase 2: ED staff provided feedback about the current system by answering the A3

Problem Solving Sheet questions. This phase was an indirect observation based on

staff working experiences and feedback in the ED, collected using a lean tool (A3) to

investigate the ED process. The A3 Problem Solving Sheet included two different

sheets: the investigation sheet and the improvement plan sheet. In this phase, A3

Problem Solving Sheets were distributed to 3 physicians, 3 nurses, 3 technicians and

one quality specialist.

Based on initial investigation and observation, a detailed process map was created for

patient flow in the ED, from arrival until discharge (Figure 6.12). There were a total of 10

main steps in the process map, including decision points and waiting times.

Development of the current VSM 6.4.5

A detailed understanding was gained through developing the process mapping with the

help of ED staff, followed by the preparation of a value stream using direct observation and

the A3 Problem Solving Sheets.

Based on participant observation of the patient flow in ED by the ED staff and the

researcher, the general process map used to illustrate general emergency department flow,

mainly for level 3 and 4 patients, is shown in Figure 6.12 It is clear that there are 10 main

process steps in ED which are listed in Table 6.1.


Table 6.1: Main steps/activities during patient flow in emergency department

Steps Process/Procedures Description

1 Patient arrivals Walk-in or by ambulance.

2 Queue/ Waiting Usually there is a queue to get a number.

3 Reception Information registrations, medical file and get a number.

4 Waiting room Before triage, waiting to be triaged by nurses.

5 Triage Initial assessments by one nurse to identify patient level

6 Waiting room Before seen by physician, waiting for medical treatment.

7 Physician assessment Go through triage assessment, then start diagnosis, ask for further

examinations such as: lab test or x-ray test.

8 LAB/X-ray examination Specialist will start examination if no patients in the waiting list.

9 Waiting results Results might take several hours to a few days based on examination

required.

10 Decision making

for/discharge or bed

admission

Final stage: ED physician or consultation will decide if patient needs

admission for follow-up treatment or discharge to home or referral to

another hospital.


Figure 6.12: Patient flow in Asir Central Hospital emergency department (ED) (process map)


Following the observation and mapping of the process, the researcher decided

to do an initial time study for patient flow from registration (reception) through the

triage system to examine the effectiveness of the current triage system in the ED (see

Appendix A and B). Several findings were made. First, the average patient waiting

time between registration and triage was found to be 4.17 minutes during shift No.1

(morning shift: 07:00AM–03:00PM). Second, the average patient waiting time after

registration until triage was 23.85 minutes during shift No. 2 (afternoon shift:

03:00PM–11:00PM). Finally, the average patient waiting time after registration until

triage was 22.29 minutes during shift No. 3 (night shift: 11:00PM–07:00AM) (See

Appendix A and B).

Figure 6.13: Waiting time after registration and before triage

For one sample of 32 patients, which is selected randomly from one day of

observations, it is clear in the previous figure how the waiting time before triage is

dramatically increased from 2 minutes at 00:15AM to 81 minutes at 03:30 AM, then

back to less than 10 minutes until 13:42 PM when it increases again to more than 40

minutes. This increase in waiting time before triage can be seen as a result of the

increasing number of patients who visit ED in that time in relation to the shortage in

the number of highly skilled triage nurses at that time. In addition, a shortage in the

number of physicians who work on this shift might be another reason which impacts

on the waiting time during the process of treatment in emergency department.

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Chapter 6: Patient Flow Problems: Voice of the Process and Voice of the internal Customer

141

Initially there was a plan to observe patients of all levels with appropriate

numbers of samples to represent the flow of ED patients in Asir Central Hospital.

However, the complexity of the ED system and difficulties relating to privacy and

patient culture in Saudi Arabia narrowed our focus to the patients who directly come

to registration and are classified as level 3 and 4.

Therefore, a time interval study for patient flow in ED was conducted to

understand the length of stay for patients in levels 3 and 4 from entry to discharge in

ED. In this case, times were collected based on patients’ files and documentation that

was produced by ED staff, and are shown in the following graph. This sample was

randomly selected from one day’s observations, which covered three different shifts.

There was a lot of data missing for the patient files, such as time intervals and patient

levels. Thus, one sample was chosen and is represented in Figure 6.14.

Figure 6.14: patients’ length of stay

In a sample of 36 patients, which was selected randomly, the length of stay

(LOS) in the emergency department evidently fluctuates. As shown in Figure 6.14,

the LOS ranges from 3 minutes to 202 minutes, depending on patient status and time

of shifts. The patient who only stayed 3 minutes arrived at 00:31 AM, was triaged at

00:32 AM, then seen and discharged by physician at 00:33 AM, which may be

because the patient’s status was not urgent, and he/she was directed by the physician

to the level 5 treatment room. Discharge here means that the patient was discharged

47

0 0 6 3

40 15

41 27

16

126

84

25

112

26 40

202

42 37

64 73

5 5 3 7 32

6 4 6 7 8 13 9

77

5 0

50

100

150

200

250

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

Patients LOS in ED

Series1


form level 3 or 4 into level 5 treatment rooms, which are located in a separate section

from level 3 and 4 rooms.

It is clear that length of stay and waiting times as a result of ED overcrowding

are essential issues that affect patient satisfaction in the emergency department. As

described in Chapter 5, the voice of the patients was gathered through a

questionnaire survey. Here, voice of patients regarding waiting time is presented in

Table 6.2.

Table 6.2: Waiting times during patient flow in ED process from patients’ perspectives

Waiting Times in ED process Voice of the patient (sample of 120)

A. Waiting time at reception was long 46.2% STRONGLY AGREE and 26.9% agreeing with

that problem.

B. Waiting time before examination by

nurse was long

45.4% STRONGLY AGREE and 28.6% agree.

C. Waiting time before diagnosing by

doctor was long


D. Waiting time during laboratory

procedures was long

28% STRONGLY AGREE and 33.1%.agree.

E. Waiting time during radiology

procedures was long


F. Waiting time at ED pharmacy was

long


In addition, two different variables that impact patient flow in the ED and

cause long waiting times are related to bed capacity and availability of ED staff in

their office. About 47.9% of 120 patients strongly agree that the bed capacity at ED

is not enough and 28.6% of them agree. Also, 43.2% strongly agree that sometimes

necessary staff are not available and 22.9% agree (Al Owad et al. 2014).

ED Layout Considerations 6.4.6

As a part of lean methodology within the visualisation process, the researcher

observed the emergency department layout to capture the voice of the process and

understand the current system for patient flow. As mentioned in chapter 5, the mean

Chapter 6: Patient Flow Problems: Voice of the Process and Voice of the internal Customer

143

score of satisfaction levels ranges from 1 to 5 (very unsatisfied–very satisfied); an

average response to the layout of ED was 2.25. Thus, it is clear that the emergency

department layout is not satisfactory to the ED customers, and is another important

variable that impacts on patient flow and causes ED overcrowding. Therefore,

integration of lean tools (voice of the customer and voice of the process) were used

to identify the sources of waste as a part of the ED layout and its effect on patient

flow in ED.

Actually, the layout of the ED in Asir Central Hospital is not designed in a way

that can help to smooth the patients’ flow during their journey in hospital. There are

a lot of interactions between staff, facilities and patients. The main issues are:

ED entrance;

Location of reception desk;

Location of triage room;

Location of waiting area;

Location of observation room;

Design of patient pathway;

Location of materials and equipment;

Design of signs and clear guidelines for all ED process and activities.

The following diagram shows the ED layout in Asir Central Hospital:


Figure 6.14: current layout of the emergency department

Figure 6.15: Current layout of the emergency department


Summary

In this chapter, the voice of the customer and the voice of process are collected

and analysed. The study has used two different approaches to collect the voice of

emergency department staff (VOC). An exploratory questionnaire survey and A3

Problem Solving Sheets are used in gathering the voice of ED staff. Also, voice of

process was collected by observation and the process map was developed and

integrated with the analysis of A3 problem solving sheets.

A total of 25 participating staff completed an explanatory questionnaire

regarding their experience in ED, their satisfaction with bed capacity and medical

equipment, and their explanation for the causes of long waiting times in ED. 32% of

the ED staff who participated have experience of more than three years in the ED.

Also, 32% of the ED staff are somewhat dissatisfied with the bed capacity at the ED.

However, 48% of the ED staff are somewhat satisfied with the medical equipment

that is available in the emergency department. Participants were asked to rate the

causes of long waiting times in the emergency department. 56% of the participants

indicated that overcrowding by the families of patients is a major cause of long waits

in emergency department. 52% of ED staff participants indicated that a shortage of

nurses in ED is another major cause of long wait times affecting the patient journey

in emergency department. While 48% of ED staff indicated that shortage of

physicians is a moderate cause for waiting time in ED, 28 % of them thought that

shortage of physicians is a major reason. Finally, 48% of ED staff indicated that

insufficient facilities are a moderate cause of waiting time in emergency department.

Next, an A3 problem-solving sheet (investigation sheet) was used to identify

the patient flow problem in the ED from the staff’s perspective, based on their

working experience. A3 reports are clear and objective communication tools written

for all specialists, and can be reviewed by staff. It was a collaborative exercise

between the researcher and ED staff. The A3 process, a form of action research,

teaches the participants how to identify problems through the observation process.

Participants were taught to draw a value stream map after reporting their observation,

a process which helps to identify patient flow problems through visualisation. Once a

value stream map was completed, the participants were taught to identify the root

cause of a problem by using the ‘five why’ process. The A3 report addresses

overcrowding in ED as a specific problem in a systematic fashion based on ED staff


perspectives. Analysis of root causes was conducted and classified into several

factors which are related to staff, patients, resources, communication, education and

training skills, administration and management skills, and primary healthcare centres.

After that, the second part of the A3 problem solving sheet (improvement plan sheet)

identified the ED staff’s perspectives of an improvement plan by diagramming how

the proposed process should work; it also provided some valuable suggestions to

improve the current situation in the emergency department.

Finally, a detailed understanding was gained through developing the process map

with the help of ED staff, followed by the preparation of a value stream using direct

observation and the A3 Problem Solving Sheets findings. Based on initial

investigations and observations, a detailed process map was created for patient flow

in the ED from arrival until discharge, including activities and patient levels. During

the observation phase, the current ED layout was carefully analysed to determine the

effectiveness of ED layout on the smoothness of patient flow.

Chapter 7: Major Findings Discussion and Patient Flow Improvement Plan 147

Major Findings Discussion and Chapter 7:

Patient Flow Improvement Plan

INTRODUCTION 7.1

Firstly, this chapter will integrate, discuss and summarise the significant findings of this

study, as presented in the previous chapters (Chapters 4, 5 and 6). Secondly, it will propose a

plan for patient flow improvement based on the current situation. Then it will define

important performance matrices based on VOC and VOP for continuous quality

improvement. Finally, these performance metrics will be linked to the fuzzy logic model

proposed in section 7.8, to help hospital decision makers evaluate the overall performance in

ED patient flow management for continuous quality improvement.

The first thing to emphasise is that the Integrated Lean Six Sigma Approach proposed

in Chapter 4 aims to solve a real-world problem (ED overcrowding) and improve patient flow

by conducting action research with a single case study. To understand the actual effectiveness

of Lean Six Sigma in healthcare, any analysis should be supported by clear, measurable

evaluation matrices. Therefore, the voices of the external and internal customer, A3 problem

solving sheets and value stream mapping (VSM) were used, integrating Lean and Six Sigma

methodology to investigate the ED overcrowding problem. Thus, an Integrated Lean Six

Sigma approach was proposed within action research methodology to investigate the current

system, define the waste and identify performance metrics in a model for improving patient

flow and quality of care in EDs.

Certainly it is necessary to recognise the patients’ needs if we are to increase their

satisfaction and improve the quality of care in emergency departments specifically, and in

hospitals in general. Chapter 5 explored and identified the quality of current ED services

from the patients’ viewpoint. The voice of the main customer was collected and analysed by

focusing mainly on their reasons for visiting/choosing an emergency department, their

satisfaction levels, the main problems in the emergency department and the quality of the

healthcare service in the current system.

Chapter 6 investigated the patient flow problem in the emergency department from the

ED staff perspectives and the process standpoint. Three different methods were used to

collect necessary information. The voice of the internal customer (front staff) was gathered

148 Chapter 7: Major Findings Discussion and Patient Flow Improvement Plan

by using an exploratory questionnaire survey which investigated staff’s experiences, their

satisfaction with the current system, and the main reasons for waiting times in the emergency

department. Then, A3 problem solving sheets were utilised for a collaborative participation

between the researcher and ED staff to study the patient flow problems through an

observation process. The process map for the current system was developed from field

observations to understand the current ED layout and patient flow process. Finally, a cause

and effect diagram was developed to discuss the significant factors which cause emergency

department overcrowding.

In the recent time, there have been rising concerns worldwide about the absence of

patient focus healthcare perspectives. One of the ways to deal with this and concurrently

ensuring a better quality of care is by embracing and integrating different lean thinking

aspects. The main concept of the lean strategy is eliminating unnecessary waste and

maximising value to the customer. Thus, before any kind of lean implementation it is

essential to understand the current situation and how future improvements can be made based

on patient value. In this thesis, the voice of the patients, the voice of staff and the voice of the

process were collected to illustrate how a patient-centric perspective can be combined with

lean thinking principles to facilitate the delivery of better quality, patient-centred healthcare

delivery.

By applying the Integrated Lean Six Sigma approach using multiple data collection

methods and techniques, emergency department overcrowding was found to be the result of

various factors which directly impact on patient flow in emergency departments. In the

following sections, the major findings are reported and discussed in relation to the available

literature.

VOICE OF PATIENTS AS LEAN STRATEGY 7.2

Voice of Customer (VOC) was used to identify waste from the patients’ perspectives

and to evaluate the quality of services based on patient feedback. According to Womack and

Jones (2003) and as suggested in the Integrated Lean Six Sigma Model described in Chapter

4, value can only be defined by the customer. It is argued that the patient should define what

creates value in health care, because the main mission of healthcare is to treat and cure

patients who are the end-consumers in the care process. In addition, the quality of a service

can be measured in terms of the level of satisfaction of the recipients of that service. This


necessitates the collection of views and feedback from clients who are able to make clear

judgments.

The voice of the main customer (patients) was gathered by conducting an exploratory

questionnaire survey to understand the current situation and determine their perspectives

based on their experience with the current system in the emergency department. Figure 7.1

shows the main factors that were extracted from the voice of patient.

Figure 7.1: Main factors which are explored from the voice of patient

Reasons for visiting ED and relevant waste 7.2.1

One of the most important principles of Lean and Six Sigma is understanding the

patients’ needs. In terms of reasons for attending ED, over-utilisation of emergency room

services by patients with non-urgent complaints is a global problem. It results in a waste of

resources, stress among emergency room staff and an increase in waiting times for patients

requiring attention (Siddiqui and Ogbeide 2002). Similar to the findings of other nations,

inappropriate utilisation of the emergency department is a big problem in the Saudi

community, where it impacts patient flow and causes emergency department overcrowding.

Generally, looking for healthcare services is the main reason for deciding to visit the

ED (Bhat et al. 2014). Our findings indicate that multiple factors influence patients’

preference in the Saudi context for attending a tertiary ED instead of primary care, including

Voice of Patients

Agreement level about main

problems in ED

Satisfaction level based on experiance

with the ED

Concept of quality in Emergency

Department services

Reasons for attending an Emergency Department


ease of access, financial ease, better treatment and preferred venue. Findings of our research

study show “no charge for lab and radiology tests or medicines at ED” as attracting the

highest mean score among patients’ responses to moderately important and important reasons

for attending ED. The following reason was being “able to see doctor and have tests done at

ED”. In addition, “no charge to see doctor at ED” was another important variable leading

patients to visit ED. Finally, “prefer ED as can attend whenever want” and “medical

treatment better at ED” are also described by patients as important factors in their decision to

come to ED. These findings are similar to another study in Saudi Arabia hospital which

mentioned the lack of a regular primary care provider or source of healthcare, perceived

convenience and access to care on the same day, and the perception that they will get better

care and access to specific treatments and investigations as patients’ reasons for visiting EDs

(Alyasin and Douglas 2014). In our study findings, most of the patients were from levels four

and five. It is clear that any reasons not directly relevant to the primary functions of an ED

will create a kind of waste, which will impact on patient flow in the ED. Therefore, such

reasons will be classified as non-value-added activities which are called waste-based in lean

thinking.

In the literature on ED around the world, emergency department crowding has been

identified as a challenge to ministries of health, especially at the referral hospitals (Pines et al.

2011). Every citizen in Saudi Arabia has access to unlimited, free medical care. The Saudi

Ministry of Health provides PHC services through a network of health care centres

throughout the country. Over the past 20 years, the government has provided support to new

projects to ensure that health services are accessible to all people at all levels of care

primary, secondary, and tertiary. The number of PHC centres rose from 1,640 in 1989 to

1,905 in 2006. In 2006, the total number of hospitals increased to 386, with 54,724 beds. In

2006, there were more than 31 million visits to PHC and over 15 million ED visits. However,

many patients prefer going directly to tertiary hospitals instead of accessing the primary care

centre and the community hospitals, with the assumption that they will get better care at the

tertiary hospitals. A total of 70% of EDs have reported more than 100,000 annual visits. It is

a big challenge that there are no specific national initiatives to reduce crowding in EDs.

In Saudi Arabia, increasing utilisation of EDs for non-urgent problems is the leading

cause of overcrowding (Qureshi 2010). Although Saudi citizens have access to unlimited,

free medical care through a network of primary healthcare centres (PHCCs) throughout the

country, Middle Eastern prevalence studies have found between 59.4% (Siddiqui and


Ogbeide 2002) and 88.7% (Shakhatreh et al. 2003) of patients presenting to EDs are

categorised as non-urgent. This can result in prolonged waiting times and delayed

intervention for more acutely ill patients (Elkum et al. 2009). One study examining trends in

ED utilisation over a three year period in a hospital in the Eastern region of Saudi Arabia

found that the number of visits increased by approximately 30% and of these, approximately

60% of patients presented with non-urgent conditions with multiple visits to the ED

(Rehmani and Norain 2007).

Recent systematic reviews of other countries have identified younger age, convenience

of the ED compared with alternatives, not having a regular physician or source of healthcare,

and negative perceptions about alternatives such as primary care providers all play a role in

driving non-urgent ED use (Bhat et al. 2014; Pepper and Spedding 2010), but these factors

may not generalise to the unique features of the Middle Eastern healthcare system. Possible

contributors to overcrowding suggested in the Middle Eastern literature include: the desire to

receive care on the same day, the possibility of having laboratory tests and other

investigations which are not provided in PHCCs, the lack of trust in primary care services,

and convenience for patients who prefer medical treatment that is available 24/7 (Malmbrandt

and Åhlström 2013; Qureshi 2010; Rehmani and Norain 2007).

It is clear that any reasons for attendance to EDs not demonstrated as relevant to their

primary functions will directly contibute to a kind of waste, which impacts on patient flow.

Therefore, these will be classified as non-value-added activities, which are called waste-

based in lean thinking.

Trends in ED utilisation in Asir Central Hospital in KSA

Nearly 60%-70% of patients in the Eastern region of Saudi Arabia used the ED

exclusively, and were categorised under level 4 and 5 conditions (Rehmani and Norain 2007).

These results are similar to the only other study published from the Kingdom, which showed

that 59.4% of the patients had primary care or non-urgent problems (Siddiqui and Ogbeide

2002). Factors that may contribute to the use of ED for less/non-urgent care include

convenience, limited access to primary care, limited availability of social supports, and

similar caregiver patterns of seeking healthcare (Janicke et al. 2001; Krakau and Hassler

1999; Minkovitz et al. 2002). This increase in the number of patients visiting ED with

primary care problems results in increased waiting time for urgent cases.


There is a significant relationship between the reasons for visiting emergency

departments and patient flow issues. Non-urgent patient visits represent 70% of emergency

department patients. This increase in ED utilisation without appropriate categorization for

patients will directly affect patient flow and cause overcrowding. This study found that there

is no effective triage system in ED at Asir Central Hospital. In addition, Pines et al. (2011)

and Alyasin and Douglas (2014) state that there are no specific national initiatives to reduce

ED crowding and non-urgent attendance in Saudi Arabia. To solve this problem, appropriate

and efficient systems should be developed to improve patient flow in emergency

departments. Therefore, Lean strategy and Six Sigma are employed in this study, using the

voice of patients to understand their needs, and to explore the impact of those reasons on

patient flow and how these reasons can negatively impact on quality and satisfaction. Thus,

interrogating the voice of patients who visit ED has given significant insights to the current

situation. Also, the involvement of patient perspectives increases the chance of solving

patient flow problems. Therefore, the voice of the customer, based on lean thinking (VOC) is

the main source for understanding the current system in this research study.

Clearly, our research study shows that there are limitations to current understandings of

the voice of the patient, which underlines the necessity for developing and improving an

initial categorisation system which helps to eliminate non-urgent patients and increase the

efficient use of the ED system. In addition, there is a need for health education of such groups

of patients (community education sessions) to increase their awareness and contribute to the

search for alternative solutions. Furthermore, there is a need for a more effective triage

system with specialist staff, and an electronic system combined with a training program for

registration staff and triage staff.

Concept of quality in Emergency Department services 7.2.2

Values in healthcare are the activities that enhance the quality of healthcare and

promote patient well-being so as to achieve better outcomes. Lean management is a method

to find out the non-value-added and time-wasting processes so as to streamline the patient

flow in emergency departments. Thus, value and flow are the main concepts of lean thinking.

Healthcare by nature is based on patient-centred outcomes whereby value to patient is the

ultimate goal. Value is worth its while when it satisfies patient’s needs at a specific cost and

time. Therefore, specific value is seen from the patient’s perspective and it is created by

eliminating waste. It is necessary to understand the patient’s standpoint in terms of the

concept of quality in the emergency department, in order to create value for the customer.


In patients’ responses to the survey of their standpoints regarding their expectations and

understandings of the concept of quality, they clarified their concept of value in the ED

process. Descriptive statistical analysis indicated that the concept of quality for patients

focuses on the following elements:

1. Respect for patients

2. Accurate diagnosis and proper treatment

3. Use of modern technology in providing health services in ED

4. Expertise and efficiency of ED staff

5. Service to maximum number of patients possible

6. Optimal utilisation of available resources

7. Minimise proportion of diseases, mortality and disability within society

8. Error-free treatment and diagnosis

9. Minimise unnecessary tests and diagnosis

10. Availability of adequate test facilities

11. Short waiting times throughout process of treatment in ED.

In addition, the factor analysis method indicated that the concept of quality from the

viewpoint of patients includes two factors. These factors are quicker services and better

services. The first factor includes short waiting times throughout the process of treatment in

ED, error free treatment and diagnoses, and minim unnecessary tests and diagnosis. The

second factor includes accurate diagnosis and treatment and respect for patients.

Previous studies of (Alyasin and Douglas 2014; Pakdil and Leonard 2014) suggest that

many patients believed they would be assessed, diagnosed and treated by more skilled

healthcare workers in the ED, with access to more technologically advanced equipment

compared to primary health care centres. Applying lean thinking concepts, our study looked

for value from the patient’s viewpoint, as identified by other studies, that EDs should provide

their needs or satisfy their expectations quickly, efficiently and with little waste (Young et al.

2004).

Despite the Saudi government committing enormous resources to improving primary

health services over the past decades and providing free healthcare to all citizens and


expatriates working in the public sector, there are key challenges for the Saudi healthcare

system which put pressure on EDs and tertiary services (Almalki et al. 2011; Johnson and

Capasso 2012). Generally, there is a lack of a significant concern for the patient flow

problem, particularly the overcrowding in emergency departments in the Saudi Arabian

healthcare system (Alyasin and Douglas 2014; Pines et al. 2011). According to (Young and

McClean 2008), the absence of a single customer with a compelling view of value is perhaps

the most important challenge in healthcare.

Today, our health care system consistently loses its way. Instead of focusing on value

to the patient, it seems to concentrate on amenities and hospital profits, or on cost cutting and

efficiency at the expense of patient care. These issues are important, but without the patient

focus, they lead to additional waste and pain for the patient.

Lean thinking is a bottom-up revolution in health care that creates value for the patient

while increasing efficiency, improving quality, and reducing cost (Dart 2011). It is important

to listen to the voice of the main customer (patients) in order to understand the current

situation and what creates value for the customer. The patient’s perspective of the concept of

quality in ED is clearly concerning and relevant to the main concept of lean thinking. Of the

five main elements that are related to better and quicker treatment factors, the main challenge

is to eliminate waste and respect the customer. This study offers an approach to creating

value from the patient’s perspective in emergency departments in Saudi Arabia.

Patient Satisfaction and Healthcare Service Quality 7.2.3

Our survey of the voice of the patient ascertained satisfaction levels with some

elements of the internal and external environment, systems and procedures, medical services

and medical support services in the emergency department. Our findings show that patients’

perspectives of the most important quality factors which impact on patient flow in Asir

Central Hospital’s ED are: “waiting time during patients treatment in the ED”; “comfort of

the waiting area”; “waiting time before being diagnosed by the doctor”; “effectiveness of the

system when dealing with the patient’s complaints”; “layout of the ED”; “waiting time before

examination by the nurse”; and “quiet location”. The findings are similar to findings in other

surveys of the patient's standpoint, which confirmed that the processes are time-consuming

and the visit is unpleasant, and satisfying patients need with improving patient flow are key

factors affecting healthcare quality (Chan et al. 2014). So, “If you want to see what your


patients experience, accompany them during their entire stay. What a great way to get the

true voice of the customer” (Al‐Borie and Damanhouri 2013).

On the other hand, the least important quality factors which rated acceptable

satisfaction levels in the view of the patients who responded are: “pharmacist’s explanation

of how to use the medicine”; “doctor’s ability in prescribing the proper treatment”; “skills of

radiology technicians in required radiology”; “reception’s treatment of you and your family”;

“nurses have done the required medical check-up”; and “accuracy of the test report”.

In the Saudi region, patient satisfaction has always remained a priority issue for the

health care authorities. The recent initiative of the Saudi health setup to attain Joint

Commission International (JCI) accreditation has further increased the importance of

improving patient satisfaction. Previous Saudi studies focusing on the patient satisfaction

issue were more or less oriented to the physician or the health care setup (Al-Doghaither and

Saeed 2000; Al-Doghaither et al. 2003; Al-Faris et al. 1996). However, none of these surveys

has assessed patient satisfaction with the emergency department services. It is essential to

document the consumer satisfaction level to improve the quality of care in the Saudi health

care system, because of the increasing interest in JCI accreditation. However, in regard to the

situation in Abha (the south-western region), there is no study that focuses on patient flow

issues and patient satisfaction improvement within the emergency department services.

One study, focused on factors influencing patient choice of hospital in Riyadh (Al-

Doghaither et al. 2003), found that patients who spend more than two hours in the ED report

have less overall satisfaction with their visit than those who are there less than two hours.

Thus, it is important to address overcrowding generally and serve the critical patients

thorough the ED system so that they are treated and discharged from the ED in the shortest

time needed to maintain high quality care (Al‐Borie and Damanhouri 2013). It is important to

meet patient needs and satisfy their expectations.

Overcrowding and long waits in EDs have been the focus of attention of studies, as the

discussion continues on how best to provide high-quality care in an efficient, cost-effective

manner. The US Institute of Medicine (IOM) report of June 2006, "Hospital-Based

Emergency Care: At the Breaking Point" underscores the importance of the critical

challenges faced by EDs in the United States, including overcrowding, ambulance diversions,

and inefficient patient flow and hospital operations. According to the report,


By smoothing the inherent peaks and valleys in patient flow, and eliminating the

artificial variability that unnecessarily impairs patient flow, hospitals can improve

patient safety and quality while simultaneously reducing hospital waste and cost.

Anyone who has spent time in the ED with a loved one or friend has, for the most

part, encountered long waits and frustration in trying to get through the process. (Al‐

Borie and Damanhouri 2013)

The current ED patient flow can be evaluated based on patient’s perspectives regarding

the quality of services in six dimensions, as shown in Table 7.1:

Table 7.1: Dimensions of quality in healthcare services

Dimension Definition

Tangibles Physical facilities, equipment and appearance of contact personnel

Responsiveness Willingness to help customers and provide prompt service

Reliability Ability to perform the promised service reliably and accurately

Assurance Knowledge and courtesy of employees and their ability to inspire trust

Empathy Provision of caring, individualised attention to consumers

Professionalism Experience, skills and innovation of hospital staff

In our study, the mean score of the 33 elements (variables) of patient’s perspectives of

quality of services is 2.99, which is the average of the overall quality dimensions showing

satisfactory results on the Likert five-point scale, with Cronbach’s Alpha value .929 for

reliability analysis. However, the categorisation of critical dimensions can be ranked as

follows, based on the mean score of each element in each dimension.

1. Mean responsiveness = 2.61

2. Tangibles = 2.75

3. Assurance = 2.95

4. Empathy = 3.1

5. Professionalism = 3.36

6. Reliability = 3.41


It is clear that in our findings, the best dimension of service quality in the emergency

department was reliability. This dimension includes pharmacist’s explanation of how to use

the medicine, accuracy of doctor’s diagnosis, required medical check-up by nurses, and

accuracy of the radiology report. On the other hand, the worst dimensions of service quality

in this emergency department was responsiveness, followed by tangibles. These dimensions

include the waiting time during treatment in ED, the effectiveness of the ED system when

dealing with the patient’s complaints, waiting time before diagnosed by physician, and the

comfort of the waiting area and layout of the ED.

Our findings are unlike one research that was carried out in Saudi Arabia by means of

an operational method, wherein SERVQUAL was made use of to understand and enumerate

numerous factors determining satisfaction of patients with the service quality of hospitals

(Al‐Borie and Damanhouri 2013). Their study established that in public hospitals, the

tangibles including staff appearance were the better service quality measurement and the

subsequent ones were expedient and reachable location, up-to-the-minute technology and

equipment. The worst services of public hospital were managing hospitals and medical

specialism, but employee collaboration with private hospital quality was greater than the

levels of public hospital (Al‐Borie and Damanhouri 2013).

Major problems from patient perspectives 7.2.4

Patients’ opinions (VOC) and agreement levels about waiting times at different stages,

medical services and ED staff care were gathered to measure and categorise the major

problems during patients’ journey in the emergency department. We found that 48.3% of

participating patients strongly agreed that the waiting time before diagnosis by the physician

was too long. 46.2% of participants strongly agreed that the waiting time at reception was too

long. 47.9% of participant’s patients strongly agreed that the bed capacity at the ED is not

enough, and 43.2% also strongly agreed that administration staffs sometimes are not available

in their office in ED. In keeping with the King Faisal Specialist Hospital and Research Centre

study, more than half of the patients waited over 6 hours and 15 % of them waited over a day

in the ED following a judgment to admit (Elkum et al. 2009). The most important reasons of

identified congestion included setbacks in releasing inpatients (90%), admitted patients’

length of stay (LOS) in the ED (70%), deficiency in admitting patient beds (70%), rise in ED

patients’ volume (60%), and while the patient is in the ED, disposition plan delay (60%).


VOICE OF STAFF AND PROCESS AS LEAN STRATEGY 7.3

Involvement of the staff and process voices in investigation into the procedures

affecting patient flow in the ED is necessary to understand the current situation from the

practitioners’ perspectives. The involvement of employees will improve some specific

operations management practices, then develop the performance of the system (Cookson et

al. 2011). Therefore, it is important to integrate the operations management and HRM for

successful results in lean techniques and quality improvement. Clearly, listening to the voice

of staff and process is the key to continuous improvement and the gridline for success. This

section reports on our findings from the semi-structured interview surveying voice of staff

and process observation in the ED in Asir Hospital. In our research, we collect the voice of

ED staff and the voice of process as discussed in Chapter 3 and 4 then integrated the major

findings from these data in chapter 6. The following points are the important factors which

affect patient flow in ED based on voice of Ed staff:

Years of ED staff’s experience

32% of the participants ED staff have experience in EDs that is more than three years.

ED staff’s satisfaction with bed Capacity

32% of participants ED staff are somewhat dissatisfied with the bed capacity at ED.

Overcrowding by the families of patients

56% of participations indicated that overcrowding by the families of patients is a major

cause of long waiting times in ED.

Shortage of ED nurses

52% of ED staff participation specified the shortage of nurses in ED as another major

cause of long waiting times in during patient journey in ED.

Patients’ awareness

Patients’ lack of understanding of the differences between urgent and normal cases was

rated by 20% of participant staff as severe and by 40% as a major cause of long waiting times

at ED in Asir Central Hospital.


Shortage of ED physicians

48% of ED staff indicated that shortage of physicians is a moderate cause for long

waiting times at the emergency department, while 28% of them believed that shortage of

physician was a major reason.

Insufficient facilities

48% of ED staff indicated that insufficient facilities are a moderate cause for long wait

times at ED in Asir Central Hospital, while 20% of ED staff held that insufficient facilities

are a major cause.

ROOT CAUSES OF NON-VALUE-ADDED ACTIVITIES 7.4

One important factor in gaining control over an organisation is understanding its basic

processes by observing the current system and gathering feedback from the perspective of

frontline staff. Industrial engineering practices like Lean Six Sigma are increasingly being

applied in healthcare organisations to increase safety, efficiency and quality (Bertholey et al.

2009; Fillingham 2007; Kate et al. 2004; Radnor and Boaden 2008). Specifically, the Toyota

production system is frequently applied in optimising workflow in health care services

(Brown and Duthe 2009; Burkitt et al. 2009; Casey et al. 2009; Melanson et al. 2009;

Rutledge et al. 2010; Stapleton et al. 2009; Young and Wachter 2009). The value stream

mapping technique has recently been used in daily trauma care, nursing and EDs (Barnes et

al. 2008; Braaten and Bellhouse 2007; Ng et al. 2010). The main goal of this study was to

investigate the most significant problems that impact patient flow at an ED, taking the voice

of the customer and the voice of the process into consideration together. ED overcrowding

was found to be a significant issue in this research from internal and external customer

perspectives, similar to other studies, where 10 directors of ED in Riyadh were surveyed and

half of them reported overcrowding is always a problem in ED, while 40% stated it was often

a problem (Tashkandy et al. 2008).

As shown in Figure 7.1, a cause and effect diagram was developed based on the

integration of observation, the voice of the customer, process mapping and A3 Problem

Solving Sheets. This integration as achieved through the researcher’s participation with ED

staff in an intensive observation of the current system, and data collection to determine the

patient flow problem, the voice of the process and staff perspectives. This study, using an

innovative method, uncovered that ED overcrowding is the result of many factors, as shown

in Figure 7.1.


Figure 7.2: Root causes of overcrowding: Cause and effect diagram for overcrowding in the emergency department (ED)

ED

Overcrowding

Shortage of quality

trained staff

Unorganized

data recording

Non-efficient

complaints system

Unnecessary movement

and intervention of

security personnel in ED

Shortage of

senior speciality

nurses in triage

Lack of

Experience

Lack of interest

among senior

consultants about

ED quality

Lack of

quality trained

staff Lack of daily

speciality

senior rounds

Shortage of

physicians

Lack of

infrastructure and

resources

Layout

design

Bed

capacity

Patients’ family

tradition

Lack of

awareness

about ED cases

No formal training

program for ED and

hospital staff

No specific team

for quality

improvement in

ED

Miscommunication

with medical staff

in ED

Lack of education

program for community

and staff

Physicians Nurses Administrative

ED Facilities Patients Quality Department

Shortage

of nurses

Lack of

Experience

Lack of quality

trained staff


Root causes of overcrowding 7.4.1

The investigation for this study shows that the causes of overcrowding can be

grouped into six major groups, based on the sources of waste, namely the quality

department, ED facilities, patients, physicians, nurses and administrative procedures

(Figure 7.2).

Quality department

In the A3 Problem Solving sheet, the ED staff mentioned that the quality

department is one factor that contributes to overcrowding in the ED. Owing to the

lack of formal training for hospital and ED staff, miscommunication between ED

employees is frequent and quality strategies are not effectively implemented.

Additionally, there is no focus on team creation and teamwork culture, which are

important for a collaborative environment such as the ED. The lack of education and

community support programmes were other issues that need to be addressed.

The resistance from hospital staff to the acceptance of continuous quality

improvement concepts represents a significant barrier to the improvement of the

quality of services. Furthermore, most ED staff had relatively little experience. Thus,

the quality department should offer a training programme for hospital staff to

improve their skills and efficiency. Additionally, it is important to provide special

programmes for the community to increase their awareness of quality concepts.

ED facilities

Based on participant observation and A3 investigation sheets completed with

ED staff, inefficient layout design, inadequate bed capacity and unavailability of

resources were found to be important factors that led directly to ED overcrowding.

The quality director stated that the bed capacity in the ED was sufficient, but that in

individual wards it was not. For instance, when a patient needs to be admitted and

assigned a bed in the hospital, he or she has to wait for a long time for the bed to be

issued. In some cases, the administration has to bring in a new bed from outside the

hospital. Additionally, the waiting area is too small and has only a few chairs for

patients. Therefore, the layout of the waiting area is a significant problem and the

hospital should improve the space and increase the waiting room capacity.


ED layout

Based on observation, it is clear that the ED layout has an impact on the patient

flow process. The intersection between the ambulance and pedestrian entrances

disturbs patient flow. Moreover, the design of ED rooms, reception area, triage,

waiting area and administration office obviously does not support smooth patient

flow during the journey through the ED. Moreover, the patient path track is not clear

to patients. Signs need to be clarified. Additionally, a redesign of the ED layout

based on process mapping would reduce the waste that takes place there.

Reception and triage system intersections

Starting the registration in reception and then waiting for triage creates a kind

of conflict between queuing and priority. These processes need to be changed in

terms of the order and the repeated work (information recording). A lot of paperwork

completed in the ED constitutes a waste of time and resources.

Bed capacity

The complexities of the healthcare environment present unique challenges that

do not exist in most manufacturing systems, specifically in terms of measurement

and workforce psychology (Laureani et al. 2013). The development of an integrated

bed management system with a digital monitoring screen in the ED would keep ED

staff informed, and help to manage patient flow.

Quality staff, ED staff, the ED director and the hospital director, as well as the

finance and resources directors, need to be involved in the process. They can then

develop a weekly, monthly and annual improvement plan with specific actions and

responsibilities assigned to each participating staff member.

Patients

Overcrowding by patients’ family members represents a significant problem

that directly affects the patient flow at ED. It is important to increase the level of

awareness through educational programmes among the patients. Lack of

understanding of the difference between urgent and non-urgent cases is another cause

for overcrowding.

Clearly, many patients prefer going directly to tertiary hospitals instead of

accessing the primary care centre and the community hospitals, with the assumption


that they will get better care at the tertiary hospitals. There is an increasing demand

on ED services with more than 100,000 annual visits for most EDs in Saudi Arabia

(Pines et al. 2011). However, there are no specific national initiatives to reduce

overcrowding. The most important causes of overcrowding identified were delays in

discharging inpatients (90%), lack of admitting inpatient beds (70%), length of stay

LOS of admitted patients in the ED (70%), increase in the volume of ED patients

(60%), and delay in disposition plan while the patient is in the ED (60%). According

to the King Faisal Specialist Hospital and Research centre study, 15% of patients

waited more than a day in the ED and more than half of them waited more than six

hours after a decision to admit (Elkum et al. 2011).

Physicians

Shortage of physicians working in the ED is a significant issue that leads to

overcrowding. Some patients indicated that the waiting time for a specialist to

diagnose their cases was too long, and they were left without treatment while the

diagnosis was being made. Additionally, experience plays an important role in time

management. However, there is a shortage of experienced and quality trained

physicians at the ED.

Nurses

The shortage of nurses is another cause for long waiting times, because there

will be no assistance for physicians when the number of nurses is too low.

Additionally, the experience and quality of nurses at an ED is another factor that

helps to reduce the time spent in the examination room.

Administrative

The ED staff and quality specialist indicated that badly-organised

documentation from the administration staff is one issue that leads to missing data

and information. The inefficient complaints system is a major problem, because it

causes a late response to process improvements. A quality training programme in

management and administration should be organised under the quality management

department to improve administrative skills.


MODEL FOR FUTURE IMPROVEMENT IN PATIENT FLOW 7.5

Based on the sources of waste identified, some steps for improvement have

already been identified and discussed. The challenge of implementing the lean

system involves training the workers to think in terms of the overall system, not just

their own work steps. This kind of teamwork will provide maximum efficiency and

quality, and thus increase internal and external customer satisfaction (Ng et al. 2010).

Based on the ED observations in this study, it is recommended to develop a

method or tool for time recording of the patient flow from entry through discharge

from the ED (to home or admission). It is significant to set up an objectives with

associated metrics that are simple and measurable (Al Owad et al. 2014). It is

likewise significant to be aware of the balanced metrics concept that allow for

numerous aspects of enhancement, like improvement in patient satisfaction,

improvement in quality, cost reduction and process time reduction. Several

organisations put great effort in data collection. It is impractical to comprehend

which step ups are functioning and which require additional alteration in the absence

of regular updates (Johnson and Capasso 2012). For this reason, a suitable time

recording sheet was developed based on observation of the current system in the ED,

as a tool for continuous quality improvement (see Appendix C). Additionally, EDs

need to apply short- and long-term contingency plans to avoid any sudden change in

the number of ED visitors.

Initial value stream mapping (VSM) (Figure 7.3), demonstrates that the triage

system should take place before the patient registration process does. This will help

to eliminate the time wasted in the ED registration process, which categorises each

patient to the appropriate level and then issues different queue numbers for each

level.

As emergency departments are treated as gatekeepers for the hospital system

and are under continuous pressure to serve all patients better, it is natural that

hospitals are concerned about their process and timely delivery of their services, and

are looking for the most effective ways to improve patient flow. Therefore,

possibilities for addressing the problem of patient flow need to be investigated and

waste causes identified, using an integrated approach that examines and visualises

patient flow and engages all customers involved in the ED. However, little existing

research has focused on the integration of lean strategies and patient flow problems


in the ED, particularly in developing countries. To the best of our knowledge, no

studies have been conducted in developing countries in this area.

VSM is an effective tool for continuous quality improvement. However, there

is currently no systematic approach in EDs for data collection, process mapping and

waste identification. In our research we demonstrate that integration of the voice of

the customer, the voice of the process and the A3 Problem Solving Sheets is an

effective lean tool to identify and eliminate waste in EDs. The appropriate integration

of fundamental factors, engaged frontline workers, long-term leadership obligations,

an understanding of patients’ requirements and the implementation of lean strategies

could continuously improve patient flow, health care service and growth in the ED.

Therefore, our research illustrates the benefits of integrating the concept of lean tools

such as the voice of the process and the voice of the staff and patients in EDs in the

identification of areas that can be improved by eliminating waste and thereby

achieving superior health care operations in practice.

Future value stream mapping 7.5.1

This section will aim to propose the countermeasures that can assess the

problems and reduce the wastes in the patient work flow. The solution will depend

on two significant aspects in the current value stream mapping. The first one is the

flow method between the area, if it is push or pull. Addressing this issue will help to

reduce the number of patients in these areas. As result of that, the staff will be able to

work more professionally and give the patient the right time needed for caring. For

example, the movement from the registration area to the level 3 and level 4 areas is

the pushing method. Therefore, these areas suffer from a number of patients that

exceed their abilities. This affects the provided services and keeps the staff confused

about who has the most urgent situation. The second aspect is the communication

method, which is manually implemented in the flow process of the ED system. This

leads to an increase in waiting times because paperwork needs time to do, and that

can cause other problems such as giving conflicting information to the patients.

Therefore, it important to create a continuous flow and establish a pull system

process instead of push system to improve patient flow in the emergency department.

The current value stream for patient flow in ED is shown in Figure 7.3.


Figure 7.3: Current value stream map for patient flow in emergency department (ED)


Creating continuous flow

To create continuous flow, the future value stream map must control the

movement between ED areas depending on effective communication methods. Also,

it is essential to reorganise the layout of the ED system by putting the triage system

before the registration system because this kind of rearrangement will reduce the

movement and activities intersection during the treatment process. This kind of

improvement in the ED layout system will improve communication inside the ED

process and eliminate unnecessary activities. As discussed in section 7.5, it is clear

that the triage system is a big challenge in EDs, and correct categorisation for

patients will eliminate non-value-added activities that could be produced as a result

of an unorganised triage system. Then, this type of communication improvement will

allow the registration system to see the availability of bed and staff in level 3 and 4

areas before pushing the patients to them. Clearly, converting to an electronic system

in the ED process will reduce the time consumed by manual communication.

Establishing pull system

The pull movement can solve the problems facing the patients under categories

3 and 4. The push movement from registration to triage then to level 3 and 4 areas

critically affects patient flow in the ED and increases the pressure on whole ED

system. Thus, it is necessary to reorganise the registration and triage system to avoid

this issue and create a pull system in emergency department.

Proposed future value stream map

In the ED, it is obvious that the value stream map is complicated and difficult

to analyse. As a result, the ED is dealing with patients with different situations that

may change suddenly. Moreover, there is a preference in the system that the service

provided for the patient depends on the emergency level and its consequences.

Therefore, there are many different flow lines created depending on the patient

requirements. It is difficult to draw a fixed value stream map for the ED.

EDs need to apply short- and long-term contingency plans to avoid any sudden

change in the number of ED visitors. The VSM demonstrates that the triage system

should take place before the patient registration process. This will help to eliminate

the time wasted in the ED in registration by categorising each patient to the

appropriate level and then issuing different queue numbers for each level. Therefore,


important information including time intervals, patients’ categorisation levels, patient

volume and other related information should recorded using an electronic system,

and be available for creating a future stream map. Then, it will be easy to control and

monitor these data for continuous improvement. It is clear that two main aspects that

need to be improved are using an electronic information flow instead of a manual

information flow, and using a pull flow instead of a push one, to avoid too many

patients in areas of the ED. The improvement in this map can be applied to all the

other lines.

The future value stream map will propose that triage system be reorganised so

that it precedes the registration system in emergency department. In addition, the

number of experienced triage nurses should be two nurses in each shift instead of

one.

INTEGRATION SYSTEM FOR LEAN STRATEGY 7.6

IMPLEMENTATION

This study demonstrates that integration of the voice of the customer, the voice

of the process and the A3 Problem Solving Sheets provides an effective lean tool to

identify and eliminate waste in EDs. The appropriate integration of fundamental

factors, engaged frontline workers, long-term leadership obligations, an

understanding of patients’ requirements and the implementation of lean strategies

could continuously improve patient flow, healthcare service and growth in the ED.

This study illustrates the benefits of integrating the concept of lean tools in the

identification of areas that can be improved by eliminating waste in order to achieve

superior healthcare operations in practice.

Some critical success factors in installing a successful lean program are

discussed in the literature. Firstly, the hospital’s executive members must clearly

commit to the lean transformation (Naik et al. 2012) and set clear and measurable

goals (Imai 1986). Mazzocato et al. (2010) argue that management should be

engaged in the continuous improvement process and more importantly, lean must

embrace a holistic approach. Mazzocato et al. (2010) emphasise that lean is not just

about implementing tools or methods. Rather, organisations often limit their success

themselves when they choose a specific technique to solve a particular problem,

instead of creating a program which addresses several problems simultaneously.

Mann (2010) and Bercaw (2013) argue that all leaders, from top management to the


leaders of a particular department, must be involved in the change and, according to

Mann, should spend some time addressing lean. Strategic directions must be given

by them and their direct participation is required (Bhasin 2012; Sobek 2011).

Secondly, it is essential to understand how lean can transform structure and

work processes and how it affects patients and employees (Holden 2011). To Sobek

(2011) a multidisciplinary team is a critical success factor, while Naik et al. (2012)

point out that all shifts must be involved and stress that implementation of new

processes outside peak hours should be considered.

Thirdly, the way people interact is also identified as a key factor. Sobek (2011)

point in particular to the importance of the way people communicate.

Communication with all participants of the lean project must be ongoing and on a

frequent basis. Naik et al. (2012) specifically underline the importance of clear

communication at the beginning of a project.

Fourthly, the importance of training is mentioned. For Poksinska (2010),

training can be provided to a group of employees. Naik et al. (2012) mention the

importance of developing lean champions. Bhasin (2012) identifies insufficient

supervisory, workforce and managerial skills as the main barriers, which can all be

addressed with training.

Fifthly, “widespread involvement” (Sobek 2011) are needed, or as Naik et al.

(2012) put it “outside stakeholders commitments was also important”. This statement

is supported by Hines (2011) and Laureani et al. (2013). For example, (Naik et al.

2012; Sobek and Lang 2010) identify associated processes such as radiology and

blood testing as well as the staff involved as critical to a successful lean

implementation. Finally, Sobek (2011) and Hines (2011) found that problem solving

and standardization of processes are other key issues.

Our research study integrates the voices of patient, staff and process to

investigate patient flow problems in the emergency department. In addition, we

introduce a roadmap for applying lean in emergency departments, and also suggest

an evaluation method for continuous improvement applying lean thinking, a new

technique in healthcare practice in Saudi Arabia.


Roadmap for change using Lean strategy approach 7.6.1

Introducing lean processes in a hospital challenges leaders, doctors physicians

and nurses at their daily work, and this is taking place in an environment where staff

are already at their limit (Fillingham 2007). Furthermore, not only sociotechnical

aspects influence the performance of staff, but also operational aspects such as new

technology (Joosten et al. 2009). As previously found, lean is not a tool or method to

be applied quickly (Mazzocato et al. 2010). Neither is lean thinking able to solve all

problems immediately. Nevertheless, some organisations apply lean with the

intention of finding rapid solutions, and are, not surprisingly, successful, as there is

waste generated everywhere in manufacturing and in healthcare. However, it can be

argued that those quick fixes are not supporting the organisation’s long term future

and as a result it can be predicted that those organisations will fall back to the

problematic stage. The roadmap presented in this chapter is designed to avoid this

disappointing outcome.

The designed roadmap considers the aspects which have so far been discussed

in the literature review and will guide those interested to apply lean in their ED. For

those who have already started a lean initiative, this roadmap will help them to keep

on track and recall the commitment made.

The following steps explain the roadmap for a proposed strategy for Lean Six

Sigma implementation in emergency departments to improve patient flow:

1. Get Ready

In the first step only the management of the organisation is involved. This may

differ from other roadmaps, but as previously discussed, management’s united

participation and powerful support for lean is crucial.

Table 7.2: Roadmap stage 1 (get ready)

Task Who Description

Identify

urgency

MGMT The management accepts that changes are inevitable.

This could be a result of an increasing error rate, a

survey or frequently changing staff. This step is based

on Kotter (1995) change process.


Create a

vision

MGMT It is understood that management discusses the current

and future state of the ED. A written vision needs to be

generated. This step is also based on Kotter (1995)

change process theory.

Agreement

on long term

involvement

MGMT The next step includes defining a strategy on how to

reach the formulated vision and add it to the

organisation’s strategic plan (Burgess and Radnor 2013;

Sobek 2011).

Taking off

the MGMT

hat

MGMT All of management must agree to commit its support and

to participate on a regular basis in the project. While

participating, it is important that management see

themselves as equals to any member of the workforce

(Bercaw 2013).

Define

worthiness

MGMT Management is asked to define how much money this

initiative is worth and consequently approve these funds.

This step is based on Bhasin (2012) findings.

Passing the

gate

ALL If management supports all of the above mentioned

points, the project should be started.

2. Set-up

Since management has agreed on starting a lean initiative and funds are

available, the process involving key stakeholders can start. Literature identifies

middle management (MMA) as the biggest obstacles (Marchwinski 2007) for a

successful lean implementation. As a result, they are addressed foremost in the set-up

step.

Table 7.3: Roadmap stage 2 (set-up)


Create sense

of urgency

MGMT The management is asked to outline on a fact basis that

lean has the potential to make significant improvements


among

MMA

in an ED. Ask for questions and identify hidden

resistance. Based on Kotter (1995) and Poole and

Mazur (2010).

Training for

MMA

MGMT/

MMA

The training provided by an external expert must

address particular ED situations. This step is based on

Fillingham (2007) and Bhasin (2012) findings. It is

recommended that MGMT participates fully in the

event. A simulation game creates a practical

understanding of process optimisation.

Address

resistance

within

middle

managers

MGMT Ask for feedback and the applicability to an ED.

Identify concerns and address these. It is MGMT’s task

to demonstrate unity, support and willingness to carry

some of the burden. This step is based on Marchwinski

(2007).

MMA

choose

project

manager

MGMT/

MMA

The first step for MGMT to take their hat off; based on

Bercaw (2013). Let middle management select the

project manager and their direction and path. However,

the project manager is preferably an MMA with

transformational leadership qualities.

3. Roll out

Now that a Project Manager (PM) is selected and resistance amongst middle

management is extinguished, it is time to address all workforces in the ED and select

the lean team.

Table 7.4: Roadmap stage 3 (roll out)


Create sense

of urgency

among

MGMT It is MGMT’s obligation to outline why the

initiative was started, why they made a

commitment and what they identified as a possible

outcome. This step is based on Kotter (1995)


workforce change theory.

Wide spread

lean

PM The project manager presents the project to all

staff. MGMT is present in unison at the event.

Along with the general information, mass

education is provided.

Create sense

Project is

worth doing

MMA This step is based on (Obara et al. 2012) findings.

A trusted middle manager, supported by the

management, must outline that lean’s goals are not

to reduce staff, but to improve patient and staff

satisfaction.

Improve

value-

adding

thinking

MMA Provide training to staff so that they can

distinguish between value-adding and non-value-

adding tasks.

Select team PM/ALL Create a multidisciplinary team among the

workforce. Make sure that all shifts are included

(Naik et al. 2012). Be aware not to select preferred

team members or include or exclude people as a

result of their rank (Rich et al. 2006).

Building the

team

Team This is according to (Tuckman 1965) leadership

model as the norming stage, where the team

evaluates how best to work together. They accept

the differences between themselves and see

opportunities in the differences for their upcoming

work. Define involvement and expertise above

day-to-day roles.

Set ‘smart’

goals

Team/MGMT As a result of the team lacking lean experience

and likelihood that the team is too ambitious

(Hoskins 2010), goals must be selected carefully.

Smart in this context means clever and humble.

Agree on ALL Middle managers, clinicians and the team must


risk

management

agree on how to identify and mitigate risks for

new processes. This is a very critical issue for an

ED. This step is based on Chiarini and Bracci

(2013) findings.

4. Perform

The performing stage is where shifts start to improve the ED but do not forget

that the lean team coordinates all activities. However, it is important to recall that

Lean’s intention is to improve patient flow by considering value-added. In addition,

the lean concepts ask for small steps to be taken (Cudney 2014). This is especially

important in an ED in order to control risks that arise.

Table 7.5: Roadmap stage 4 (perform)


Focus only

on tasks

within the

department

Team In order not to create chaos or resistance among

other

departments all tasks completed must be within

the ED.

Ask shifts

what to do

first and let

them work

on a chosen

task

Team Provide shifts the opportunity to choose and work

on a task which hinders them most from being

efficient. Support their feeling that it is their work

what counts.

Create

quick

wins

Team/MMA This step is based on Radnor and Walley (2008)

and Kotter (1995) findings. Make sure shifts are

effective so that they can achieve a quick win.

Provide feedback by recognising that it has

changed for the better.

Create

vision

Team This step is based on Sobek (2011) findings. Set-

up an information board at a central location in


management order to improve communication among all staff.

Bulletin other shifts’ work so that they know

where changes are considered and which results

are expected. Introduce, for example, a weekly

10-minute meeting in front of the board.

Show

interest

MMA/MGMT If management can from time to time attend the

10 minute meeting, they honour achieved results

and show that they, as initially committed,

support lean; based on Naik et al. (2012).

Address

resistance

MGMT Take the opportunity to go Gemba walking

(Heifetz and Laurie 2009).

VSM Team Conduct the first value stream mapping in the ED

by observing the work performed and making

notes about tasks completed and flows involved.

Assess risks MMA/Team Prior to implementation of any changes analyse

the risks of the newly designed process, mitigate

risks or create intervention plans. This step is

based on Chiarini and Bracci (2013) findings.

Share risk MGMT Inform the management about possible risks and

actions planned. The intention of this step is not

that MGMT carries the risk, but that they are

informed about the change in the process.

Training on

new

processes

Team/Shifts Provide training about the newly designed

process to staff and explain possible risks and

how to react to those. Ask for feedback and

concerns. Take them seriously. It may require an

additional redesign of the process (Bhasin 2012;

Naik et al. 2012; Sobek 2011).

Implement

new

Team/Shifts Implement the first new process under

supervision and observe efficiency. Use the

Deming circle (PDCA) to further understand and


processes improve the new process.

Standardise

new

processes

ALL Set the new process as the standard. Make sure all

staff have received training on the new process.

As some may be away repeat training on a regular

basis (Hines 2011).

Take those

on board

who fall

back to old

ways

Team Now that several changes have been made to the

ED environment, it is important to monitor the

effectiveness of those. Some participants tend

quietly to do things the way they have done it for

years (Bercaw 2013). Do not disapprove their

way of doing things, but show them the benefits

of new processes and provide leadership.

Address

cross

department

issues

Team Having experienced how best to optimise

processes within the ED and once the team has

reached a level of maturity, it is now time to

include processes which go beyond the

department.

Optimise

Layout

Team Make only small changes to the layout. Do not

plan any major construction activities. Recall:

Lean is executing small but persistent steps.

5. Sustain

Now that the ED is frequently having lean events and staff have understood

that lean can add value to a patient’s treatment, it is time to move on to a stage where

the main focus is to consolidate the lean initiative. Leaders, for example top and

middle management but also clinicians, are the drivers for this stage. The theory

applied is based on the change management process where it is mentioned that

change stops if victory is declared too early (Kotter 1995; Reijula and Tommelein

2012).


Table 7.6: Roadmap stage 5 (sustain)


Reward staff MGMT It is important that management recognise achieved

results and respond with adequate rewards. Any rewards

must be in accordance with organisation policy. For

example, it may be a sponsored barbeque in order to

improve team building.

Adjust

strategy

MGMT By considering the achieved result, the business strategy

may need to be adjusted. Recall and renew the

commitment. This step is based on Bhasin (2012),

Radnor and Walley (2008) and Sobek (2011)

requirements.

Set KPI’s MGMT

MMA

In order to achieve ongoing results among middle

management and clinicians, KPI’s are adequate steering

elements (Balding 2005). Now it is time to set new

KPIs. Keep staff busy but do not assign additional tasks

if staff are working hard on lean topics.

Retraining

staff

MMA/

Team

There are two reasons for training. Firstly, as a result of

a process redesign whereby a role has changed, and

secondly, to consolidate lean. Both types of training

have to be provided on a predefined regular basis

(Bercaw 2013).

Gemba

walking

MGMT

MMA

They are asked to go to the ED and talk to people,

honour achieved results, support their lean thinking and

identify and address any suspected fear of change

(Heifetz and Laurie 2009).

Optimise

Layout

Team After all, the layout may be a hindering factor for further

improvements and needs to be addr(Heifetz and Laurie

2009)essed or in case the organisation plans a major

upgrade in the ED, it is recommended to proactively


participate in the development process of a new design.

The designed roadmap, wherein all steps are identified as issues in the

literature, follows a top down approach where all management is asked to participate.

Due to the fact that middle managers are identified as obstacles (Marchwinski 2007),

a special emphasis is laid on integrating them. During the performing stage, risk

management is considered and addressed as a major issue, and finally, the sustain

stage is designed so that lean is effective in current and future environments.

This roadmap provides guidance to the process of installing and keeping the

lean concept in an organisation as a strategic approach. A critical point is reached at

the end of the get ready stage. If management does not agree to fully participate in

the concept, it is likely that the concept will fail. In order not to cause additional

workload and disturbance among staff, it is then recommended not to start lean.

However, once started, the lean initiative needs to be maintained by providing

training on a regular basis and by constantly challenging the current stage in order to

improve to a new best practice.

A better implementation strategy is that people in the first implementation step

are involved in an efficient and effective self-assessment process, where critical

success factors and performance indicators are assessed and discussed in relation to

the overall goal of developing a lean healthcare culture.

Evaluation for continuous quality improvement 7.6.2

Lean implementation is employed as a tool for gaining competitive advantage;

however, these practices have failed owing to the absence of clear awareness and

comprehension of lean performance and its measurement. Conversely, it is not likely

to achieve lean without its performance measurement (Behrouzi and Wong 2011).

Pakdil and Leonard (2014) stressed on the fact that the complete performance

of the current or new systems and applications ought to be measured and examined

incessantly through a range of measures of performance. With a wider, constant

perspective of improvement, performance measurement is a requirement for any

organisation, and not just for lean organisations (Deming 1986). Evaluation is vital to

recognize the advancement and insufficiencies of lean theories in organizations (Imai


1986). According to (Protzman et al. 2011), one cannot handle it if one cannot

measure it.

A few studies in literature studies in the literature (Bayou and Korvin 2008;

Goodson 2002; Singh, Garg and Sharma 2010) emphasize on computing the

management systems’ compactness and highlight the call for a coalescing measure of

the results of such practices. Consequently, it is necessary to put lean assessment into

practice in lean practices’ preliminary stage (Saurin et al. 2010). With these ideas in

mind, an assessment tool is proposed in the following section.

EVALUATION MODEL FOR LSS IN ED 7.7

In manufacturing, leanness is a method that aims to eliminate wastes while

stressing the need for continuous improvement (Papadopoulou and Özbayrak 2005).

When reviewing the extant literature on lean thinking, it has become apparent that

questions such as ‘how to become leaner’, ‘how to measure the leanness of a system’

and how lean should the system be’ have received less attention (Bhasin 2011;

Soriano‐Meier and Forrester 2002). However, the developments made in service

settings, , particularly in healthcare, are even today not many as against those

accounted in the manufacturing literature, and in the literature, there is no specific

answer to the question of how could lean transformations be measured in health-care

(Machado Guimarães and Crespo de Carvalho 2014). The process of becoming

leaner is discussed in previous sections by the process of a lean implementation

roadmap and improvement plan, designed to deal with healthcare practitioners’ and

customers’ needs and their resource constraints.

This section will develop a methodology to evaluate the impact of the proposed

roadmap and improvement plan, which discussed in earlier, and answer the question

‘how to measure the leanness of healthcare system’ and ‘how lean should the

healthcare system be’. The next section describes the principles of the improved

leanness assessment model, followed by the lean healthcare measures and

performance metrics, and the proposed fuzzy leanness assessment model. The model

is proposed but not demonstrated in this study, and this is acknowledged as a

limitation and will be pursued in a future study.


An efficient leanness assessment model 7.7.1

In the field of manufacturing, for supporting the theory of one cannot improve

what one cannot measure, a significant issue is to be capable of simply assessing the

existing performance levels and for ensuring what factors should be considered as

critical for achieving further enhancement. Performance measurement is defined as

the process of measuring an action’s effectiveness and efficiency (Muthiah and

Huang 2006).

In recent years, the term lean health care has emerged (Brandao de Souza

2009), indicating a stronger focus on efficiency and patient satisfaction within the

healthcare sector. Nevertheless, we have acknowledged that the term has time and

again been misconstrued and the hospitals, like several other institutions, have begun

to apply Lean Production (LP) without identifying with the structural and cultural

prerequisites for executing TQM and LP (Dahlgaard and Dahlgaard‐Park 2006).

Numerous healthcare administrations have formerly attempted to put TQM into

practice without any substantial success and had the similar experience with lean

performance. It usually necessitates, as with Total Quality Management, a cultural

modification where the intangible or soft management factors such as partnerships,

people management and leadership are transformed so that a new culture of

organisation is build up to hold up and advance the core processes of the hospital.

A better plan of implementation is that individuals are involved in the first step

of implementation in a well-organized and effectual self-assessment process, wherein

performance indicators and decisive success factors are measured and discussed

corresponding to the overall purpose of establishing a lean healthcare culture. It

appears that existing evaluation methods formed on the basis of current models of

excellence such as the European Excellence Model (EFQM) have been employed

chiefly for helping experts while inscribing an award application. Such a method is

of use if the establishment is exceptional and could be known as a lean healthcare

organisation (Ruiz and Simon 2004). Nonetheless, to commence the expedition to

distinction, another evaluation methodology is required, a simple method and

evaluation model or framework that attracts all employees to take part in. Currently,

the development of such an evaluation and enhancement methodology is in huge

demand. Nevertheless, it is vital note that applying a system of performance

measurement necessitates a new thinking way of healthcare management.


Several researchers used varied performance metrics in relation to the lean

proposal to follow the growth and enhancement pattern. As a rule, a set of lean

metrics is employed concurrently with execution of lean method to assess the lean

initiatives’ efficiency. Nonetheless, an incorporated leanness measure being

objective and quantitative is not located in the lean theory. Lean practitioners can

follow the pattern of growth and improvement in every definite area and construct an

uneven picture of a system’s overall leanness only by reassessing the complete lean

metrics set. Consequently, lean practitioners can be acquainted with the method of

improving the leanness and the areas that have been improved, however, they do not

possess the knowledge of the lean system or how much leaner it could get. An

incorporated measure of leanness ought to be built up to assess the leanness

improvement endeavours. At present, there is no apposite technique available to

precisely compute the lean strategies’ impact on the healthcare organisations’

performance on the whole. Furthermore, there are no current studies that employ

quantitative and qualitative approaches at the same time (Pakdil and Leonard 2014).

This research proposes a healthcare leanness evaluation model by using the

triangular fuzzy concept. Fuzzy logic can deal with the uncertainty and

impreciseness of input data and it is also applicable for analysing the qualitative

variables of a system (Zadeh 1965). This study proposes a method to convert both

qualitative and quantitative values of performance metrics into quantitative ones and

then generate corresponding membership functions to evaluate the leanness value.

This leanness value answers the question of ‘how lean a healthcare system is’. To our

best knowledge, this is the first attempt in healthcare to measure leanness by

incorporating both qualitative and quantitative measures into a single quantitative

value by employing the fuzzy concept.

This study follows two main steps to accomplish an improved and efficient

leanness assessment model.

1. Identifying lean healthcare performance metrics: this represents the selection

of suitable lean healthcare performance metrics, in both qualitative and

quantitative measures, related to the selected lean services process based on

the patients’ and staff’s needs.


2. Measuring leanness value: this process evaluates the lean practices associated

with the services process and calculates the leanness values, employing

triangular membership functions.

Lean healthcare measures and performance metrics 7.7.2

Performance measurement upholds the success of the objectives of health-care

system. In keeping with the quality management theory, the improvement plans

exhibits a remarkable positive relationship with performance. A successful

performance measurement plan would assist in guiding an organisation towards

assessing the chief processes and result in an improvement to advance care. The end-

result is valuable for setting up performance targets that classify the best industry

performance and measure the healthcare service’s value, quality and effectiveness.

The lean philosophy and in its relevance in manufacturing take into account the

measures for providing a course of direction since manufacturers shift from the

present position to the future position by adapting to the long-term goals of business

(Ramesh and Kodali 2011). Moreover, in health-care, it is discussed that a tool for

measuring lean service adoption must comprise all three model types, i.e. the

dynamic, static and mixed model to completely reveal the implementation. The

supporting structure’s significance for successful implementation has ever more been

understood as significant (Hines and Lethbridge 2008; Radnor 2010). Nonetheless,

the question of whether or not any real improvements in processes have taken place

and whether or not these have essentially resulted in any enhanced results of

performance has not been answered by the enablers. The significance of gauging the

relevance of lean practices must be viewed next to the backdrop that lean

implementation is a lengthy and compound method and that despite the company

becoming leaner, a few items of performance might also direct at the incorrect track

at the outset (Karlsson and Åhlström 1996).

Lean implementation in healthcare has been increasingly reported in the

literature (Brandao de Souza 2009; Mazzocato et al. 2010; Poksinska 2010; Sobek

and Lang 2010; Winch and Henderson 2009; Young and McClean 2008);

nonetheless, the issue of ‘how much lean’ has been put into practice and is deficient

of precise answers, partly because of some misconception of what could define a

lean organization (Womack and Jones 2003). The literature focuses on the ‘hard’

aspect of a lean culture and not on its ‘soft’ aspect, i.e. only on its tools and


techniques (Badurdeen et al. 2011). On the other hand, the difficulties of lean

deployment assessment and suitable metrics (Neely 2007) in a sector still cause

struggles to achieve a universal performance evaluation system (Jean-François 2006;

Saltman et al. 2011). In healthcare, lean projects must be SMART, i.e. specific,

measurable, action-oriented, relevant, and timely (Stamatis 2011).

A critical review of all lean assessment systems in manufacturing and

healthcare settings was carried out and completed with the awareness that metrics are

one of the biggest challenges of lean deployment in healthcare (Young and McClean

2009). The extant literature has not established a true empirical or theoretical

foundation for demonstrating the effectiveness of, or the goal-specific improvements

achieved by, the Lean and Six Sigma methodologies in healthcare organisations

(DelliFraine et al. 2010; Langabeer et al. 2009). Malmbrandt and Åhlström (2013)

stated that lean practices holds vital importance as these practices could entail certain

time to bring about enhanced performance. Moreover, measures that are based on

performance are certainly imperative to take account of. Without process

improvement in major key performance, it is clear that a process is not leaner (Wan

and Frank Chen 2008).

For a meaningful integrated leanness index, one has to understand how these

metrics interrelate with each other. This research utilized the fuzzy set concept to

bring all qualitative and quantitative performance metrics to a common denominator,

thus providing an integrated leanness index. The following figure shows the model

for performance category, metrics which is related to quality as shown in Figure 7.4

and proposed evaluation using a fuzzy approach as adopted for this study.


Figure 7.4: Performance category, metrics and the proposed evaluation using

fuzzy approach

The question “how can the effectiveness of an integrated Lean-Six Sigma

model improving patient flow be evaluated for continuous process improvement?”

was raised in this research study. The following section will discuss the answer.

Developing a fuzzy logic model 7.7.3

Basic concepts of fuzzy logic

To represent non-statistical, uncertain and linguistic values, fuzzy models use

fuzzy sets. Bojadziev et al. (2007) stated the basic definitions of fuzzy set theory as

follows:

Definition one: A fuzzy set A is defined by a set of ordered pairs,

(1)

Where:

: is a function called membership function; it specifies the grade or

degree to which any element x in A belongs to the fuzzy set A.

Definition two: A membership function of a triangular fuzzy number is defined

as follows:

Fuzzy Logic

Performance Metrics

Tangibles Responsiveness Reliability Assurance Empathy Professionalism

Performance Category

Quality Dev

elop

Eva

lua

tion

Mo

del


(2)

The concept of membership function is the basis for defining evaluation

functions for the performance measures.

Evaluation method

A new and dynamic lean performance assessment model was developed by

Behrouzi and Wong (2011) by employing fuzzy methodology. Their approach allows

the investigator to define performance indicator preferences which approach

produces a single company’s all-inclusive study of the efforts of lean

implementation.

Fuzzy set theory justifies the indecision taken over in accepted language by

making use of specific words like much, most, frequently, not very many, very

many, not many, quite a few, few, small number and large number (Zadeh 1965).

Fuzzy sets are used by fuzzy models to correspond to linguistic, uncertain and non-

statistical values (Behrouzi and Wong 2011).

Behrouzi and Wong (2011) suggest the subsequent steps to construct the

approach of measurement of unclear judgment for lean performance in the field of

manufacturing. However, this proposed technique was discussed by this research to

resolve the issue of flow of patients in healthcare EDs.

1. Determine the lean performance attributes.

2. Identify the performance categories and metrics for each lean attribute.

3. Set the fuzzy area and membership function for each performance metric.

4. Calculate fuzzy membership values and their arithmetic mean scores.

Using this method, the lean score will be calculated by taking the average of all

membership-values. Then, this single score can be used for evaluation of lean

performance.

In this research, metrics are both quantitative and qualitative and based on

patient and staff perspectives. In addition, the assessment of the relative importance

of customer satisfaction depends on human judgment, which differs from one person


to another. Thus, fuzzy logic has proven to be an excellent choice for many control

system applications because it applies human control logic. In addition, it will

evaluate both qualitative and quantitative measures by a single score, usually from

0.0 to 1.

The fuzzy logic approach for performance evaluation will be developed to

evaluate the effectiveness of Lean Six Sigma Integration in patient flow

improvement. It will measure the performance of Lean Six Sigma by a single score

between 0 and 1, based on the scores of patient satisfaction and results of the time

and motion study.

Table 7.7: Performance metrics and indexes


The above table shows the performance metrics and indexes for Lean Six

Sigma Integration to improve the patient flow in the emergency department.

This research study will use the following membership functions for each

performance metric (Mi), then develop the membership values. Two points (a, b) will

be used to represent the best and worst Lean Sigma performance of each metric.

(3)

Example for more clarification

This example was obtained from the literature to explain the fuzzy logic,

membership function and performance metrics (Behrouzi and Wong 2011).

Table 7.8: Numerical example and data

Performance Metrics 7.7.4

As a main objective of this study is to determine the performance metrics for

improved patient flow, significant performance metrics that are related to quality and

time in continuous quality improvement are defined based on VOC and VOP.


In this section, performance metrics are proposed to be used in a fuzzy logic model

to help hospital decision makers in evaluation of overall performance of ED patient

flow for continuous quality improvement. Voice of the Customer (ED patients) was

gathered by using a questionnaire. Descriptive analysis by SPSS was conducted to

categorise the most significant factors that affect ED patient flow. As a result, the

seven metrics shown in Table 7.9 were identified for evaluation in fuzzy logic for

continuous quality improvement.

Table 7.9: Main critical metrics based on Voice of Customer in ED

LSS

Attribute

Performance

Category

Dimension Metrics Index

Waste

Elimination

Just-In-

Time (JIT)

Reduce

Variation

Quality

Tangibles

Responsiven

ess

M1 Waiting time during patients treatment in ED

M2 Comfort of the waiting area

M3 Waiting time before being diagnosed by

physicians

M4 Effectiveness of the system when dealing

with the patient’s complaints

M5 Layout of the ED

M6 Waiting time before examination by nurse

M7 Quietness of ED location

It is not easy to measure patient satisfaction and get a clear decision for quality

improvement with a questionnaire approach, but applying qualitative research beside

a quantitative technique in a future study would grant a better perception of the

complex problem of quality in the healthcare area (Glaveli and Karassavidou 2011).

In addition, fuzzy logic is a brilliant technique for evaluating the overall performance

and service quality of every dimension and helping hospital decision makers in

choosing and applying accurate approaches (Sinimole 2012). Ambiguous data such

as human judgments can be measured simply and applied to established results when

using fuzzy logic (Amirzadeh and Shoorvarzy 2013). Accordingly, these metrics will

be used in a fuzzy logic model to evaluate ED patient flow performance for

continuous quality improvement. In addition, fuzzy logic evaluates both qualitative

and quantitative measures by a single score, usually from 0.0 to 1.


Fuzzy membership values will be calculated for each indicator after

performance indicators are measured using VOC and VOP in an organisation. Then,

according to Pakdil and Leonard (2014), “the final lean score is calculated as the

mean of all membership values taken into consideration in lean assessment (Behrouzi

and Wong 2011)”.

However, developing and validating findings have not been done in this

research study. It is thus clear that the study has a limitation and will require further

work to demonstrate and evaluate these metrics within fuzzy logic. It will then be

possible to evaluate accurately the effectiveness of the Integrated Lean Six Sigma

approach in improving ED patient flow, by measuring a Lean-Sigma score between

zero and one, based on the proposed fuzzy evaluation method.

Summary

In this research fuzzy logic has been proposed to evaluate the effectiveness of

lean six sigma integration in patient flow improvement. The performance metrics

have been identified and categorized based on patient and staff requirements. The

length of stay and patient satisfaction scores have been gathered from survey and

observation methods after mapping the processes and gathering the voice of the

customers. Then, integration of VSM and statistical analysis help to prioritise

customer needs and lean technical descriptors (Johnson and Capasso 2012; Mohanraj

et al. 2011). The fuzzy logic aims to evaluate the effectiveness of the current process

based on the performance metrics derived from the perspectives of the customers.

Chapter 8: Conclusions and Future Works 190

Conclusions and Future Works Chapter 8:

This chapter presents the conclusions and highlights the contributions of the

study to theory and practice. Finally, its limitations are outlined and areas for future

research are suggested.

SIGNIFICANCE OF THE RESEARCH STUDY 8.1

As has been observed, control of patient flow is a major factor for improving

hospital emergency department operations. Indeed, patient flow is a central driver of

a hospital’s operational performance, which is tightly coupled with the overall

quality and cost of health care. The main role of the ED can be defined as the

treatment of patients who are critically ill or injured (Singh Gill 2012). As the ED is

considered a central place in the healthcare organisation, issues in other components

of the system may be linked with those in the ED (Yen and Gorelick 2007). Hence,

problems in the ED may strongly affect the public opinion of the entire healthcare

organisation (Mazzocato et al. 2012). Emergency departments (EDs) suffer more

from patient delays than perhaps any other component of the healthcare system.

Delays in EDs remain a critical challenge, warning remarks such as, “Access Block

and ED overcrowding have created a dynamic tension and the future of emergency

medicine will be determined by the resolution of this conflict” (Forero et al. 2011;

Sklar et al. 2010). To understand and deal with the problems of long waiting lists and

access blocks in healthcare facilities, specific studies and actions are required (Bain

et al. 2010). EDs worldwide are challenged by a variety of problems that eventually

affect patient flow. Overcrowding, treatment delays, reduction in quality and safety

of care and inefficient use of available resources are considered patient flow

obstacles in the ED (Campbell et al. 2004; Cooke et al. 2004; Lecky et al. 2014;

Pines et al. 2011; Richardson and Mountain 2009). The key to improving the flow of

patients in EDs is the reduction or elimination of non-value-added activities and the

consequent streamlining of the process (Holden 2011).

In 2012, there were 35.57 million visits to primary health care centres (PHCs)

in Saudi Arabia and over 20 million ED visits (Health 2012). A total of 70% of EDs

have reported more than 100,000 annual visits and more than half of the patients


waited more than six hours in the ED, and 15% waited more than 24 hours (Pines et

al. 2011). In addition, the most important causes of overcrowding identified were

delays in discharging inpatients (90%), lack of admitting inpatient beds (70%), LOS

of admitted patients in the ED (70%), increase in the volume of ED patients (60%),

and delay in the disposition plan while the patient is in the ED (60%).

Therefore, the ED is continually under increased pressure to meet community

expectations in terms of service quality. Healthcare providers are facing a significant

challenge in identifying waste in their processes and selecting a proper strategy to

address the waste. It is clear that EDs are under great pressure to improve their

operations; however there are only a few indications of responses to the tremendous

need for a focus on improving ED operations globally, and in Saudi Arabia, there are

no specific national initiatives to reduce crowding (Alyasin and Douglas 2014;

Saghafian et al. 2014). To do this, it is necessary to understand the operations in the

emergency departments. Integration of Lean principles and Six Sigma methodology

(DMAIC) provide the proposed solution for the research problem of investigating

patient flow issues in the emergency department.

Due to the limitations in the literature which are presented in sections 2.5 and

2.6, the integration of Lean Six Sigma methodology was proposed in this research as

a continuous quality improvement approach that aims to solve the problem of patient

flow in the emergency department. This integration helps to evaluate the current

situation and determine clear measurement variables for improvements. This

research is significant as there is little research available on how to integrate Lean

concepts and Six Sigma methodology to investigate and improve patient flow in

emergency departments. This research has conducted a real single case study in one

hospital emergency department in Saudi Arabia. Therefore, hospitals, particularly in

Saudi Arabia and the Middle East, will now have a very effective approach to

improving patient flow in the emergency department. By means of the proposed

integrated approach to investigating the patient flow problem, providers, managers

and decision makers will be able to identify the major causes of waste affecting the

emergency department, using their available resources. This integration with the

described procedure can be used in other similar cases.

192 Chapter 8: Conclusions and Future Works

DEVELOPMENT OF INNOVATIVE LEAN STRATEGY 8.2

We have summarised evidence that supports the need for Lean Six Sigma

integration, particularly for patient flow improvement in the emergency department.

The lack of specific national initiatives to reduce crowding and improve patient flow

in emergency departments in Saudi Arabia inspired us to find an innovative approach

with an appropriate methodology for investigating the waste that affects patient flow

in ED. There is a need to evaluate whether this integrated approach can indeed

effectively and continuously improve patient flow and healthcare services quality

generally. The main goal of this study was to investigate the most significant

problems that impact patient flow in an ED, taking the voice of the customer and the

voice of the process into consideration together. We found that ED overcrowding

was a significant issue from internal and external customer perspectives.

An integrated framework for investigating patient flow problems in emergency

departments is a relatively new topic, and there is limited available data on applying

integrated methods in the healthcare setting. As a consequence, an inductive

approach has been selected as an appropriate strategy. Action research applying

mixed methods to a single case study is then used as a research strategy to achieve

the research objectives. This research study combined qualitative and quantitative

methods in the data collection processes, in a single case study for real problem

solving in one emergency department in Saudi Arabia. Investigation of the problem

has been done through in-depth observation of the patient flow in the emergency

department. This observation method builds a clear picture of the current system for

patient flow in the emergency department. Patient flow has been categorised from

admission until discharge. Then, voice of the process (VOP) with process mapping

has been employed to build Value Stream Mapping (VSM) which will help to

identify the value- and non-value-added activities. At the same time, questionnaires

have been distributed to understand the main customer (patient) requirements (VOC).

Also, A3 problem solving sheets have been used to gather the voice of the staff who

work in the emergency department.

To enhance the lean concepts, tools and techniques such as Value Stream

Mapping (VSM) and A3 reports (Hines et al. 2004; Singh and Sharma 2009; Singh,

Garg, Sharma, et al. 2010; Vinodh et al. 2010), have been used, as they are clear and

objective communication tools. These can capture workers’ knowledge of work


processes in the value stream, and when value stream maps are drawn and A3

documents are written, all specialists and staff can review them. This takes into

account cross-departmental involvement of procedure changes and creates a

significant ideas to solve the current problem (Yusof et al. 2012).

The case study also uses a questionnaire to collect the customers’ perspectives

in the investigation of lean in healthcare settings (de Carvalho et al. 2014). The voice

of the customer was collected in this study by using a questionnaire survey to

understand their experience and requirements for improving the current system of the

emergency department; see Chapter 3. For further study, the phenomenon under

investigation can generate data that can be represented in a quantitative fashion.

A mixed method research approach with a concurrent triangulation strategy is

employed for this study. This is also known as convergent parallel design, when the

quantitative and qualitative data collection and analysis strands during the same

phase of the research process prioritise the methods equally, keeping the strands

independent during analysis and then mixing the results during the overall

interpretation (Creswell and Plano Clark 2011). This allows both sets of results to be

interpreted to provide a richer and more comprehensive response to the research

questions, in contrast to the use of a mono-method design; see Chapter 3.

A cause and effect diagram was developed based on the integration of

observations, the voice of the customer, process mapping and A3 Problem Solving

Sheets. This integration combines the researcher’s participation with ED staff in an

intensive observation of the current system, data collection to determine the patient

flow problem, the voice of the process, and staff perspectives. This study, using an

innovative method, has uncovered that emergency department overcrowding is the

result of many factors, as shown in Figure 7.1 and summarised below.

Voice of Patient 8.2.1

According to the patients, major factors that directly cause overcrowding in

emergency department are long waiting time before diagnosing by a physician,

insufficient bed capacity and unavailability of administrative staff during office

hours.


Voice of Staff 8.2.2

Based on staff voice using A3 problem solving sheets, note taking and

conversation and semi-structured interviews, the following figures show the major

results gathered to understand the current situation from staff perspectives.

Figure 8.1: Key problems from staff’s perspectives

As shown in Figures 8.1 and 8.2, there are a number of key problems which are

directly affecting the patient flow and causing overcrowding and long waiting time in

emergency department. These problems can be classified under four main areas:

1. The layout of emergency department.

2. Lack of staff training and lower standard of training particularly for triage nurses,

physician and quality department staff.

3. Non-availability of some important resources including beds, patient centred

primary health centre and sufficient number of staff.

4. Finally, the lack of rules and clear guidelines for treatment plan. These include

lack of involvement of the specialist senior physician in daily decision making in

ED.


Figure 8.2: Key problems from staff’s perspectives

Voice of Process 8.2.3

Based on observation and process mapping, the following figure shows the

main results of the process of observation.

Figure 8.3: ED layout problems

As shown in Figure 8.3, a number of factors that contribute to the inefficiency

of ED layout, which led to patient flow obstacles. Redesigning the ED layout with

consideration of ED entrance, reception desk; triage room, waiting area, observation

room locations, location of signs and patient path ways will improve the current

situation for patient flow and increase the efficiency of emergency department.


PLAN FOR PATIENT FLOW IMPROVEMENT 8.3

Patient flow, health care service and growth in the ED could be continuously

improved by the appropriate integration of fundamental factors, engaged frontline

workers, long-term leadership obligations, an understanding of patients’

requirements, and the implementation of lean strategies:

Develop a method for time recording,; see Appendix C

Roadmap for change using Lean Six Sigma approach; see Chapter 7.

Proposed evaluation method for continuous quality improvement; see

Chapter 7.

CONTRIBUTION 8.4

The major contributions of this research are listed below:

For scientific community

o Development of a new systematic approach based on integrated Lean

and Six Sigma methodology to identify root causes of overcrowding

and improve the patient flow in an emergency department.

o Conduct the research in Saudi Arabia, a developing country where

Lean Six Sigma concepts are still new. Thus, this research will be a

new contribution for the research community in the Middle East.

For practitioners

o A new layout for patient flow in the emergency department with

minimum non-value-added activities.

o The root causes for delay and overcrowding in emergency department

will be known, which will help management and staff to deal with

these problems.

o Understandings of the patient’s needs and priorities and their

requirements.

For hospitals:

o Elimination of waste and non-value-added activities, which will result

in better patient flow and shorter turnaround time.

o Improvement of hospital staff satisfaction.


o Increased reliability and credibility in healthcare.

o Reduced costs and improved quality of health care.

For Patients

o Patient voice heard and their needs understood

o Increased patient safety.

o Increased patients’ satisfaction.

o Development of an education program for patients to increase their

awareness of urgent and non-urgent cases before they decide to visit

the emergency department.

o Development of a good environment with quiet location for patients in

emergency departments by eliminating overcrowding causes.

RESEARCH LIMITATION AND FUTURE WORKS 8.5

This thesis has taken an important step towards a deeper understanding of the

relevance of Lean Six Sigma strategies in healthcare sittings, specifically in

investigating patient flow problems in emergency departments, and has proposed a

plan for continuous quality improvement, and suggested an evaluation model to

assess the current proposed Integrated Lean Six Sigma framework. However, some

research limitations are acknowledged, as shown in the following:

This research is single case study within one hospital in Saudi Arabia; this

may decrease the possibility of generalisation.

Presence of the researcher as an observer may lead the ED team members

to alter their behaviour, knowing that they are being observed; hence all

the possible inefficiencies may not be observed

Value stream mapping should include time intervals for the whole process.

However, this study proposed a map based on ED layout and activities

without time intervals in VSM, due to limitations in the available data in

hospital.

Only a few lean tools were used in this study to identify the waste, thus

implementation of more lean tools to reduce waste can better generalise

the findings.


DMAIC used as Six Sigma methodology in this study; the process

capability and statistical process control should be considered in future

research.

Based on the findings and outcomes of this research, there are opportunities for

future research:

Developing and validation of the proposed fuzzy evaluation method with

quality and time as a performance metrics could be an important extension

of this work.

More real single and multi-case studies, to validate the proposed Integrated

Lean Six Sigma model in healthcare systems.

To apply Six Sigma methodologies, integrating process capability and

statistical process control with lean should be considered in future

research.

Integration of simulation and Lean Six Sigma approach in one model

should be considered in future work.

It could have been interesting to use a simulation study with the result of

A3

Future research can pursue a simulation study with standard method like

Event-driven Process Chain (EPC) methodology for mapping the process

by identifying key functions of events, resources involved and process

paths and logical operators. If required, events can be identified with

associated time measures such as waiting times, for further analysis of the

process.

Healthcare systems might be different in different countries and

improvements or modifications of the current approach might be

necessary.

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Appendices 240

Appendices

Appendix A

Patient observation in Aseer Central Hospital emergency department: reception

through triage

Patient

No.

Arrival Time at

Reception

Time of

Triage

Waiting

Time

1 00:13 00:15 2 Min.

2 00:30 00:35 5 Min.

3 00:31 00:38 7 Min.

4 00:35 00:45 10 Min.

5 00:37 00:50 13 Min.

6 00:39 01:00 21 Min.

7 00:50 01:15 25 Min.

8 00:54 01:20 26 Min.

9 01:06 01:25 19 Min.

10 01:07 01:30 23 Min.

11 01:08 01:40 32 Min.

12 01:21 01:47 26 Min.

13 01:22 02:00 38 Min.

14 01:30 02:10 40 Min.

15 01:32 02:15 43 Min.

16 01:59 02:45 46 Min.

17 02:00 03:00 60 Min.

18 02:09 03:30 81 Min.

19 03:44 03:47 3 Min.

Appendices 241

20 04:29 04:30 1 Min.

21 05:05 05:10 5 Min.

22 05:43 05:45 2 Min.

23 06:16 06:20 4 Min.

24 07:02 07:03 1 Min.

25 08:04 08:05 1 Min.

26 09:09 09:15 6 Min.

27 09:12 09:20 8 Min.

28 09:42 09:45 3 Min.

29 11:24 11:30 6 Min.

30 13:22 13:42 20 Min.

31 15:08 15:50 42 Min.

32 18:14 18:25 11 Min

ED Shifts

Morning (No.1) 07:00–15:00

Afternoon (No.2) 15:00–23:00

Night (No.3) 23:00–07:00

Appendices 242

Appendix B

Patient observation in Aseer Central Hospital emergency department: arrival through

discharge

Patient

No.

Arrival Time at

Reception

Time of

Triage

Time Seen by ED

Physician

Discharge

Time

1 00:03 00:04 00:09 NA

2 00:13 00:15 00:17 01:00

3 00:14 01:00 NA NA

4 00:29 01:00 NA NA

5 00:30 00:35 00:36 00:36

6 00:31 00:32 00:33 00:33

7 00:35 00:45 00:47 01:15

8 00:37 00:50 00:52 00:52

9 00:39 01:00 01:02 01:02

10 00:50 01:15 01:17 01:17

11 00:51 01:05 01:07 01:07

12 00:54 01:20 01:22 03:00

13 01:06 01:25 01:27 02:30

14 01:07 01:30 01:32 01:32

15 01:08 01:40 01:42 03:00

16 01:21 01:45 01:47 01:47

17 01:22 02:00 02:02 02:02

18 01:23 02:02 02:04 04:45

19 01:30 02:10 02:12 02:12

20 01:40 02:15 02:17 02:17

21 02:00 03:00 03:02 03:04

22 02:59 03:02 03:05 04:12

Appendices 243

23 03:44 03:47 03:49 03:49

24 04:29 04:30 04:34 04:34

25 04:37 04:35 04:37 04:39

26 05:05 05:10 05:12 05:12

27 05:43 05:45 05:47 06:15

28 06:16 06:20 06:22 06:22

29 06:41 06:41 06:43 06:45

30 10:29 NA 10:30 10:35

31 12:38 12:39 12:40 12:45

32 13:52 13:53 13:58 14:00

33 14:07 14:17 14:15 14:20

34 14:21 NA 14:25 14:30

35 14:38 NA 14:40 15:55

36 15:40 NA 15:40 15:45

Appendices 244

Appendix C

Time Study Sheet Developed for Aseer Central Hospital Emergency Department

Date:

Patient ID:

Registration/Reception Desk:

Arrival Time:

Triage Area Assessment:

Arrival Time:

Time Assessment Finished:

Triage Level:

Waiting Area:

Waiting time:

Physician Assessment:

Arrival Time:


Triage Level:

X-Ray Assessment:

Arrival Time:

Waiting Area Time:


Triage Level:

Lab Assessment:

Arrival Time:

Waiting Area Time:


Triage Level:

Discharge Time:

Length of Stay (LOS):

Appendices 245

Appendix D

Letter to Aseer Central Hospital Emergency Department

Appendices 246

Appendix E

Ethical Clearance

Appendices 247

Appendix E

Ethical Clearance

Appendices 248

Appendix F

Info-consent-questionnaire

Appendices 249

Appendix G

Patient Questionnaire, page 1

Appendices 250

Appendix G


Appendices 251

Appendix G


Appendices 252

Appendix H

Agreement to use questions

Appendices 253

Appendix I

Agreement to use questionnaire

Appendices 254

Appendix J

Supervisor letter to emergency department director

Appendices 255

Appendix J

Supervisor letter to my sponsor

Appendices 256

Appendix J

Letter from Saudi Cultural Mission for data collection

Appendices 257

Appendix J

Agreement from ED director to conduct our research study

From: musa alfaifi ([email protected])

Sent: Wednesday, 20 June 2012 7:45:40 PM

To: [email protected]

Appendices 258

Appendix J

Approval from Hospital

Appendices 259

Appendix K

Statistical analysis and tests

Nonparametric correlations for all subscales Correlations

Reason is

ease of

access (av)

Reason is

financial

ease (av)

Reason is

better

treatment

(av)

Reason is

preferred

venue (av)

External

environment

(av)

Efficient

procedures

(av)

Medical

services

related to

doctor's

capability

(av)

Medical

services

related to

doctor's

use of

time (av)

Medical

support

radiology

services

(av)

Medical

support

laboratory

services

(av)

Waiting

times for

primary

care (av)

Medical

service

treatment

problems

(av)

Medical

service

resource

problems

(av)

ED staff

health

care

problems

single-

factor

solution

(av)

Spearman's

rho

Reason is

ease of

access (av)

Correlation

Coefficient 1.000 .336

** .204

* .248

** -.183 -.053 -.020 -.219

* -.138 -.075 .388

** .012 .195

* .087

Sig. (2-

tailed) . .000 .031 .009 .055 .582 .839 .022 .150 .437 .000 .902 .039 .365

N 112 111 112 109 111 110 110 110 110 109 111 112 112 111

Reason is

financial

ease (av)

Correlation

Coefficient .336

** 1.000 .029 .190

* -.125 -.044 -.025 -.276

** -.042 -.175 .290

** .052 .133 .100

Sig. (2-

tailed) .000 . .763 .047 .188 .643 .796 .003 .659 .066 .002 .581 .159 .296

N 111 113 112 110 113 112 112 112 112 111 112 113 113 112

Reason is

better

treatment

(av)

Correlation

Coefficient .204

* .029 1.000 .093 -.130 -.077 -.320

** -.097 .142 -.109 -.045 .041 .234

* .136

Sig. (2-

tailed) .031 .763 . .333 .171 .420 .001 .309 .138 .258 .635 .663 .012 .152

N 112 112 113 110 112 111 111 111 111 110 112 113 113 112

Reason is

preferred

Correlation

Coefficient .248

** .190

* .093 1.000 -.074 -.125 -.305

** .075 -.010 -.179 -.076 .360

** .320

** .143

Appendices 260

venue (av) Sig. (2-

tailed) .009 .047 .333 . .444 .195 .001 .440 .915 .063 .433 .000 .001 .135

N 109 110 110 110 110 109 109 109 109 108 109 110 110 110

External

environment

(av)

Correlation

Coefficient -.183 -.125 -.130 -.074 1.000 .434

** .250

** .572

** .186

* .214

* -.464

** -.149 -.288

** -.516

**

Sig. (2-

tailed) .055 .188 .171 .444 . .000 .007 .000 .044 .020 .000 .108 .002 .000

N 111 113 112 110 119 118 117 118 118 117 115 118 118 117

Efficient

procedures

(av)

Correlation

Coefficient -.053 -.044 -.077 -.125 .434

** 1.000 .529

** .558

** .388

** .435

** -.273

** -.518

** -.309

** -.671

**

Sig. (2-

tailed) .582 .643 .420 .195 .000 . .000 .000 .000 .000 .003 .000 .001 .000

N 110 112 111 109 118 118 116 117 117 116 114 117 117 116

Medical

services

related to

doctor's

capability

(av)

Correlation

Coefficient -.020 -.025 -.320

** -.305

** .250

** .529

** 1.000 .198

* .209

* .511

** -.004 -.543

** -.367

** -.490

**

Sig. (2-

tailed) .839 .796 .001 .001 .007 .000 . .033 .024 .000 .966 .000 .000 .000

N 110 112 111 109 117 116 117 117 117 116 113 116 116 115

Medical

services

related to

doctor's use

of time (av)

Correlation

Coefficient -.219

* -.276

** -.097 .075 .572

** .558

** .198

* 1.000 .320

** .250

** -.463

** -.202

* -.152 -.520

**

Sig. (2-

tailed) .022 .003 .309 .440 .000 .000 .033 . .000 .007 .000 .029 .103 .000

N 110 112 111 109 118 117 117 118 118 117 114 117 117 116

Medical

support

radiology

services (av)

Correlation

Coefficient -.138 -.042 .142 -.010 .186

* .388

** .209

* .320

** 1.000 .185

* -.223

* -.355

** -.035 -.364

**

Sig. (2-

tailed) .150 .659 .138 .915 .044 .000 .024 .000 . .046 .017 .000 .709 .000

N 110 112 111 109 118 117 117 118 118 117 114 117 117 116

Appendices 261

Medical

support

laboratory

services (av)

Correlation

Coefficient -.075 -.175 -.109 -.179 .214

* .435

** .511

** .250

** .185

* 1.000 -.173 -.320

** -.149 -.348

**

Sig. (2-

tailed) .437 .066 .258 .063 .020 .000 .000 .007 .046 . .067 .000 .110 .000

N 109 111 110 108 117 116 116 117 117 117 113 116 116 115

Waiting

times for

primary care

(av)

Correlation

Coefficient .388

** .290

** -.045 -.076 -.464

** -.273

** -.004 -.463

** -.223

* -.173 1.000 .068 .320

** .432

**

Sig. (2-

tailed) .000 .002 .635 .433 .000 .003 .966 .000 .017 .067 . .470 .000 .000

N 111 112 112 109 115 114 113 114 114 113 116 116 116 115

Medical

service

treatment

problems

(av)

Correlation

Coefficient .012 .052 .041 .360

** -.149 -.518

** -.543

** -.202

* -.355

** -.320

** .068 1.000 .274

** .495

**

Sig. (2-

tailed) .902 .581 .663 .000 .108 .000 .000 .029 .000 .000 .470 . .003 .000

N 112 113 113 110 118 117 116 117 117 116 116 119 119 118

Medical

service

resource

problems

(av)

Correlation

Coefficient .195

* .133 .234

* .320

** -.288

** -.309

** -.367

** -.152 -.035 -.149 .320

** .274

** 1.000 .319

**

Sig. (2-

tailed) .039 .159 .012 .001 .002 .001 .000 .103 .709 .110 .000 .003 . .000

N 112 113 113 110 118 117 116 117 117 116 116 119 119 118

ED staff

health care

problems

single-factor

solution (av)

Correlation

Coefficient .087 .100 .136 .143 -.516

** -.671

** -.490

** -.520

** -.364

** -.348

** .432

** .495

** .319

** 1.000

Sig. (2-

tailed) .365 .296 .152 .135 .000 .000 .000 .000 .000 .000 .000 .000 .000 .

N 111 112 112 110 117 116 115 116 116 115 115 118 118 118

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Appendices 262

Nonparametric correlations for all nine scales Correlations

Reasons single-

factor solution

(av)

Concepts of

quality single-

factor solution

(av)

Environment

single-factor

solution (av)

Systems and

procedures

single-factor

solution (av)

Medical

services single-

factor solution

(av)

Medical

support

services single-

factor solution

(av)

Waiting times

single-factor

solution (av)

Medical service

problems

single-factor

solution (av)

ED staff health

care problems

single-factor

solution (av)

Spearman's

rho

Reasons single-

factor solution

(av)

Correlation

Coefficient 1.000 .084 -.037 -.053 -.145 -.247

* .254

** .213

* .097

Sig. (2-tailed) . .389 .703 .585 .131 .011 .008 .026 .312

N 110 108 110 109 109 105 109 110 110

Concepts of

quality single-

factor solution

(av)

Correlation

Coefficient .084 1.000 -.359

** .093 .160 .042 .395

** -.298

** -.033

Sig. (2-tailed) .389 . .000 .330 .093 .670 .000 .001 .729

N 108 113 113 112 112 108 111 112 111

Environment

single-factor

solution (av)

Correlation

Coefficient -.037 -.359

** 1.000 .567

** .518

** .412

** -.419

** -.311

** -.573

**

Sig. (2-tailed) .703 .000 . .000 .000 .000 .000 .001 .000

N 110 113 119 116 117 114 115 118 117

Systems and

procedures

single-factor

solution (av)

Correlation

Coefficient -.053 .093 .567

** 1.000 .714

** .676

** -.253

** -.542

** -.672

**

Sig. (2-tailed) .585 .330 .000 . .000 .000 .007 .000 .000

N 109 112 116 116 115 111 113 115 114

Medical services

single-factor

solution (av)

Correlation

Coefficient -.145 .160 .518

** .714

** 1.000 .636

** -.213

* -.484

** -.619

**

Sig. (2-tailed) .131 .093 .000 .000 . .000 .024 .000 .000

Appendices 263

N 109 112 117 115 117 113 113 116 115

Medical support

services single-

factor solution

(av)

Correlation

Coefficient -.247

* .042 .412

** .676

** .636

** 1.000 -.371

** -.516

** -.598

**

Sig. (2-tailed) .011 .670 .000 .000 .000 . .000 .000 .000

N 105 108 114 111 113 114 110 113 112

Waiting times

single-factor

solution (av)

Correlation

Coefficient .254

** .395

** -.419

** -.253

** -.213

* -.371

** 1.000 .327

** .435

**

Sig. (2-tailed) .008 .000 .000 .007 .024 .000 . .000 .000

N 109 111 115 113 113 110 115 115 114

Medical service

problems single-

factor solution

(av)

Correlation

Coefficient .213

* -.298

** -.311

** -.542

** -.484

** -.516

** .327

** 1.000 .498

**

Sig. (2-tailed) .026 .001 .001 .000 .000 .000 .000 . .000

N 110 112 118 115 116 113 115 119 118

ED staff health

care problems

single-factor

solution (av)

Correlation

Coefficient .097 -.033 -.573

** -.672

** -.619

** -.598

** .435

** .498

** 1.000

Sig. (2-tailed) .312 .729 .000 .000 .000 .000 .000 .000 .

N 110 111 117 114 115 112 114 118 118

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

Appendices 264

SEM Measurement model

Groups

Notes for Group (Group number 1)

The model is recursive.

Sample size = 120

Variable Summary (Group number 1)

Your model contains the following variables (Group number 1)

Observed, endogenous variables

Environment Av

Systems Procedures Av

Medical Support Services Av

Medical ServicesPblmSingleFactorSolnAv

Waiting Times Single Factor Av

ED Staff HlthCare Av

Medical Services Av

Observed, exogenous variables

Concepts Of Quality Av

Reasons9Av

Unobserved, exogenous variables

e4

e5

e7

e2

e1

e3

e6

Appendices 265

Variable counts (Group number 1)

Number of variables in your model: 1

6

Number of observed variables: 9

Number of unobserved variables: 7

Number of exogenous variables: 9

Number of endogenous variables: 7

Parameter Summary (Group number 1)

Weights Covariances Variances Means Intercepts Total

Fixed 7 1 0 0 0 8

Labeled 0 0 0 0 0 0

Unlabeled 13 9 9 0 0 31

Total 20 10 9 0 0 39

Assessment of normality (Group number 1)

Variable min max skew c.r. kurtosi

s

c.r.

Reasons9Av 1.11

1

4.77

8

-

.106

-

.475

.195 .436

ConceptsOfQualityAv 2.36

4

5.36

3

-

.931

-

4.16

5

-.506 -

1.13

1

EDStaffHlthCareAv 1.00

0

5.00

0

-

.271

-

1.21

3

-.329 -

.737

WaitingTimesSingleFactorAv 1.00

0

5.00

0

-

1.09

9

-

4.91

4

1.391 3.11

0

MedicalServicesPblmSingleFactorSol

nAv

1.25

0

5.00

0

.077 .344 .102 .227

MedicalServicesAv 1.16

7

5.08

9

-

.305

-

1.36

3

.510 1.14

1

MedicalSupportServicesAv 1.30

8

5.00

0

.238 1.06

3

1.554 3.47

4

SystemsProceduresAv 1.00

0

4.99

8

-

.268

-

1.19

6

-.158 -

.353

EnvironmentAv 1.00

0

4.60

0

.361 1.61

4

-.610 -

1.36

5

Multivariate 8.418 3.27

7

Appendices 266

Observations farthest from the centroid (Mahalanobis distance) (Group number 1)

Observation number Mahalanobis d-squared p1 p2

99 23.477 .005 .466

120 23.477 .005 .130

5 21.157 .012 .175

50 20.933 .013 .071

87 20.604 .015 .031

116 20.604 .015 .008

78 19.384 .022 .018

113 19.384 .022 .005

25 17.814 .037 .037

40 17.814 .037 .015

67 16.761 .053 .052

77 16.684 .054 .029

24 15.902 .069 .071

69 15.321 .082 .119

42 14.852 .095 .167

60 14.852 .095 .105

4 14.528 .105 .123

55 14.035 .121 .200

27 13.939 .125 .162

79 13.611 .137 .204

114 13.611 .137 .140

29 13.541 .140 .108

76 13.466 .143 .084

11 13.225 .153 .097

20 13.225 .153 .063

71 13.004 .162 .072

112 13.004 .162 .046

108 12.272 .198 .197

47 11.597 .237 .487

59 11.597 .237 .403

48 11.265 .258 .531

81 11.183 .263 .502

46 11.066 .271 .498

90 11.044 .273 .431

10 10.975 .277 .398

68 10.674 .299 .522

54 10.183 .336 .767

62 10.183 .336 .703

97 10.183 .336 .633

98 10.183 .336 .557

Appendices 267

119 10.183 .336 .481

14 10.132 .340 .442

2 10.084 .344 .401

19 10.084 .344 .330

88 9.878 .360 .403

117 9.878 .360 .332

83 9.598 .384 .466

6 9.444 .397 .511

26 9.359 .405 .503

7 9.199 .419 .557

80 9.125 .426 .542

32 8.894 .447 .653

28 8.815 .455 .645

13 8.372 .497 .870

64 8.371 .497 .827

1 8.158 .518 .889

51 7.877 .547 .952

43 7.841 .550 .941

61 7.841 .550 .916

23 7.723 .562 .928

16 7.696 .565 .911

57 7.680 .567 .884

39 7.625 .572 .873

18 7.602 .575 .843

12 7.484 .587 .864

89 7.450 .590 .840

74 7.406 .595 .819

22 7.207 .616 .884

58 7.146 .622 .875

104 6.871 .651 .948

75 6.866 .651 .927

31 6.856 .652 .901

101 6.666 .672 .941

3 6.665 .672 .916

86 6.638 .675 .895

115 6.638 .675 .857

49 6.297 .710 .958

106 6.295 .710 .937

105 6.184 .721 .948

92 6.042 .736 .963

15 5.900 .750 .975

36 5.774 .762 .981

102 5.743 .765 .975

Appendices 268

45 5.351 .803 .997

85 5.310 .807 .997

17 5.283 .809 .995

21 5.283 .809 .991

84 5.268 .810 .986

53 5.245 .812 .979

8 5.219 .815 .970

30 5.106 .825 .976

70 5.024 .832 .976

111 5.024 .832 .960

109 4.988 .835 .947

96 4.972 .837 .924

118 4.972 .837 .886

107 4.884 .844 .885

100 4.820 .850 .871

103 4.567 .870 .942

Appendices 269

Models

Default model (Default model)

Notes for Model (Default model)

Computation of degrees of freedom (Default model)

Number of distinct sample moments: 4

5

Number of distinct parameters to be estimated: 3

1

Degrees of freedom (45 - 31): 1

4

Result (Default model)

Minimum was achieved

Chi-square = 18.162

Degrees of freedom = 14

Probability level = .200

Group number 1 (Group number 1 - Default model)

Estimates (Group number 1 - Default model)

Maximum Likelihood Estimates

Regression Weights: (Group number 1 - Default model)

Estima

te

S.

E.

C.

R.

P

WaitingTimesSingleFac

torAv

<-

--

ConceptsOfQualityA

v

.308 .0

89

3.4

71

***

MedicalServicesPblmSi

ngleFactorSolnAv

<-

--

Reasons9Av .147 .0

58

2.5

34

.01

1

MedicalServicesPblmSi

ngleFactorSolnAv

<-

--

ConceptsOfQualityA

v

-.237 .0

62

-

3.8

08

***

EnvironmentAv <-

--

ConceptsOfQualityA

v

-.357 .0

65

-

5.5

01

***

SystemsProceduresAv <-

--

WaitingTimesSingle

FactorAv

.160 .0

51

3.1

65

.00

2


--

MedicalServicesPbl

mSingleFactorSolnA

v

-.375 .0

78

-

4.7

98

***

MedicalServicesAv <-

--

MedicalServicesPbl

mSingleFactorSolnA

v

-.327 .0

82

-

4.0

06

***

MedicalSupportServices

Av

<-

--

MedicalServicesPbl

mSingleFactorSolnA

v

-.166 .0

68

-

2.4

43

.01

5

MedicalSupportServices

Av

<-

--

EDStaffHlthCareAv -.351 .0

52

-

6.6

***

Appendices 270

83


--


67

-

7.1

15

***

EnvironmentAv <-

--


64

-

8.6

98

***


--


66

-

8.1

28

***


--

WaitingTimesSingle

FactorAv

.136 .0

49

2.7

88

.00

5

Standardized Regression Weights: (Group number 1 - Default model)

Estima

te

WaitingTimesSingleFactorAv <--

-

ConceptsOfQualityAv .264

MedicalServicesPblmSingleFacto

rSolnAv

<--

-

Reasons9Av .174


rSolnAv

<--

-

ConceptsOfQualityAv -.274

EnvironmentAv <--

-

ConceptsOfQualityAv -.332

SystemsProceduresAv <--

-

WaitingTimesSingleFactorAv .184


-


rSolnAv

-.318

MedicalServicesAv <--

-


rSolnAv

-.294

MedicalSupportServicesAv <--

-


rSolnAv

-.194

MedicalSupportServicesAv <--

-

EDStaffHlthCareAv -.540


-


EnvironmentAv <--

-



-



-


Covariances: (Group number 1 - Default model)

Appendices 271

Estimate S.E. C.R. P Label

ConceptsOfQualityAv <--

>

Reasons9Av .070

e1 <--

>

e3 .417 .085 4.939 ***

e2 <--

>

e3 .343 .063 5.405 ***

e2 <--

>

e1 .300 .061 4.925 ***

e5 <--

>

e6 .176 .032 5.495 ***

e7 <--

>

e6 .142 .026 5.483 ***

e4 <--

>

e5 .163 .035 4.628 ***

e4 <--

>

e7 .063 .026 2.382 .017

e4 <--

>

e6 .137 .035 3.967 ***

e5 <--

>

e7 .113 .024 4.625 ***

Correlations: (Group number 1 - Default model)

Estimate

ConceptsOfQualityAv <--> Reasons9Av .107

e1 <--> e3 .508

e2 <--> e3 .570

e2 <--> e1 .506

e5 <--> e6 .583

e7 <--> e6 .581

e4 <--> e5 .469

e4 <--> e7 .224

e4 <--> e6 .390

e5 <--> e7 .468

Appendices 272

Variances: (Group number 1 - Default model)

Estimate S.E. C.R. P Label

ConceptsOfQualityAv .644 .083 7.802 ***

Reasons9Av .671 .086 7.802 ***

e2 .434 .056 7.714 ***

e1 .812 .105 7.714 ***

e3 .832 .108 7.714 ***

e4 .405 .052 7.714 ***

e5 .298 .039 7.714 ***

e7 .195 .025 7.714 ***

e6 .304 .039 7.714 ***

Squared Multiple Correlations: (Group number 1 - Default model)

Estimate

EDStaffHlthCareAv .000


MedicalServicesPblmSingleFactorSolnAv .095

MedicalServicesAv .487

MedicalSupportServicesAv .443

SystemsProceduresAv .552

EnvironmentAv .456

Modification Indices (Group number 1 - Default model)

Covariances: (Group number 1 - Default model)

M.I. Par Change

Variances: (Group number 1 - Default model)

M.I. Par Change

Regression Weights: (Group number 1 - Default model)

M.I. Par Change

Model Fit Summary

CMIN

Model NPAR CMIN DF P CMIN/DF

Default model 31 18.162 14 .200 1.297

Saturated model 45 .000 0

Independence model 9 597.520 36 .000 16.598

RMR, GFI

Model RMR GFI AGFI PGFI

Default model .050 .968 .897 .301

Saturated model .000 1.000

Independence model .255 .382 .228 .306

Appendices 273

Baseline Comparisons

Model NFI

Delta1

RFI

rho1

IFI

Delta2

TLI

rho2

CFI

Default model .970 .922 .993 .981 .993

Saturated model 1.000 1.000 1.000

Independence model .000 .000 .000 .000 .000

Parsimony-Adjusted Measures

Model PRATIO PNFI PCFI

Default model .389 .377 .386

Saturated model .000 .000 .000

Independence model 1.000 .000 .000

NCP

Model NCP LO 90 HI 90

Default model 4.162 .000 19.351

Saturated model .000 .000 .000

Independence model 561.520 486.000 644.469

FMIN

Model FMIN F0 LO 90 HI 90

Default model .153 .035 .000 .163

Saturated model .000 .000 .000 .000

Independence model 5.021 4.719 4.084 5.416

RMSEA

Model RMSEA LO 90 HI 90 PCLOSE

Default model .050 .000 .108 .453

Independence model .362 .337 .388 .000

AIC

Model AIC BCC BIC CAIC

Default model 80.162 85.850 166.574 197.574

Saturated model 90.000 98.257 215.437 260.437


ECVI

Model ECVI LO 90 HI 90 MECVI

Default model .674 .639 .801 .721

Saturated model .756 .756 .756 .826


Appendices 274

HOELTER

Model HOELTER

.05

HOELTER

.01

Default model 156 191

Independence model 11 12

Appendices 275

Tests of Between-Subjects

Effects

Dependent Variable: Reasons single-factor

solution (av)

Source Type III

SS

df_trtmt df_error MS F Sig.

Gender 3.443 1 101 3.443 4.894 0.029

EducationLevel4gps 1.646 3 101 0.549 0.780 0.508

Gender *

EducationLevel

(4gps)

1.537 3 101 0.512 0.728 0.538

Error 71.061 101 0.704

Total 1235.321 109

Corrected Total 77.680 108

a R Squared = .085 (Adjusted R

Squared = .022)

Tests of Between-Subjects

Effects

Dependent Variable: Concepts of quality

single-factor solution (av)

Source Type III

SS


Corrected Model 5.920 7 104 0.846 1.280 0.268

Intercept 1656.821 1 104 1656.821 2506.928 0.000

Gender_r 0.298 1 104 0.298 0.451 0.503


Gender_r *

EducationLevel4gps

4.308 3 104 1.436 2.173 0.096

Error 68.733 104 0.661

Total 2208.074 112



Squared = .017)

Tests of Between-Subjects Effects

Dependent Variable: Environment single-

factor solution (av)

Source Type III

SS


Corrected Model 1.641 7 110 0.234 0.309 0.948

Intercept 655.751 1 110 655.751 864.656 0.000

Gender_r 0.070 1 110 0.070 0.093 0.761


Appendices 276

Gender_r *

EducationLevel4gps

0.726 3 110 0.242 0.319 0.812

Error 83.424 110 0.758

Total 919.560 118



Squared = -.043)


Dependent Variable: Systems and procedures single-


Source Type III

SS


Corrected Model 12.985 7 107 1.855 3.146 0.005

Intercept 718.800 1 107 718.800 1218.954 0.000

Gender_r 0.376 1 107 0.376 0.637 0.426


Gender_r *

EducationLevel4gps

5.550 3 107 1.850 3.137 0.028

Error 63.096 107 0.590

Total 965.125 115



Squared = .116)

Estimates

Dependent Variable: Systems and procedures single-factor solution (av)

Education level (4gps) Mean Std.

Error

95% Confidence

Interval

Lower Bound Upper

Bound

Primary/Intermediate

level

3.126 0.190 2.75 3.501

Secondary level 3.124 0.176 2.775 3.474

Diploma 2.438 0.172 2.097 2.778

BA/higher ed 2.794 0.108 2.581 3.008

Appendices 277

4. Gender * Education level (4gps)

Dependent Variable: Systems and procedures single-


Gender Education level (4gps) Mean Std.

Error

95% Confidence

Interval

Lower Bound Upper

Bound

Female Primary/Intermediate level 2.73 0.213 2.309 3.153

Secondary level 3.26 0.243 2.781 3.744

Diploma 2.73 0.243 2.244 3.206

BA/higher ed 3.03 0.176 2.677 3.376

Male Primary/Intermediate level 3.52 0.313 2.899 4.142

Secondary level 2.99 0.256 2.479 3.494

Diploma 2.15 0.243 1.669 2.631

BA/higher ed 2.56 0.125 2.316 2.809


Dependent Variable: Medical services



Corrected Model 6.520 7 108 0.931 1.628 0.135

Intercept 873.014 1 108 873.014 1525.971 0.000

Gender_r 0.561 1 108 0.561 0.981 0.324


Gender_r *

EducationLevel4gps

2.392 3 108 0.797 1.394 0.249

Error 61.787 108 0.572

Total 1186.583 116



Squared = .037)


Dependent Variable: Medical support services single-



Corrected Model 9.668 7 106 1.381 4.659 0.000

Intercept 907.753 1 106 907.753 3062.137 0.000

Gender_r 2.808 1 106 2.808 9.474 0.003


Gender_r *

EducationLevel4gps

3.892 3 106 1.297 4.376 0.006

Error 31.423 106 0.296

Total 1204.811 114



Appendices 278

Squared = .185)

4. Gender * Education level (4gps)

Dependent Variable: Medical support services single-factor

solution (av)

Gender Education level (4gps) Mean Std.

Error

95% Confidence

Interval

Lower Bound Upper

Bound

Female Primary/Intermediate level 3.21 0.151 2.914 3.512

Secondary level 3.38 0.172 3.036 3.718

Diploma 3.50 0.164 3.171 3.822

BA/higher ed 3.53 0.125 3.283 3.778

Male Primary/Intermediate level 3.44 0.206 3.032 3.848

Secondary level 3.21 0.206 2.801 3.617

Diploma 2.52 0.172 2.182 2.864

BA/higher ed 3.01 0.090 2.833 3.188


Dependent Variable: Waiting times single-


Source Type III

SS


Corrected Model 14.863 7 106 2.123 2.603 0.016

Intercept 1343.775 1 106 1343.775 1647.523 0.000

Gender_r 2.662 1 106 2.662 3.264 0.074


Gender_r *

EducationLevel4gps

4.768 3 106 1.589 1.949 0.126

Error 86.457 106 0.816

Total 1804.2 114



Squared = .090)

Estimates

Dependent Variable: Waiting times single-factor

solution (av)

Education level (4gps) Mean Std. Error 95% Confidence

Interval

Lower Bound Upper

Bound

Primary/Intermediate

level

4.16 0.212 3.737 4.577

Secondary level 3.92 0.219 3.482 4.352

Diploma 4.11 0.202 3.710 4.510

BA/higher ed 3.55 0.129 3.297 3.807

Appendices 279


Dependent Variable: Medical service problems single-


Source Type III

SS


Corrected Model 7.840 7 110 1.120 2.280 0.033

Intercept 1147.353 1 110 1147.353 2335.371 0.000

Gender_r 1.064 1 110 1.064 2.165 0.144


Gender_r *

EducationLevel4gps

2.095 3 110 0.698 1.421 0.240

Error 54.042 110 0.491

Total 1576.438 118



Squared = .071)


Dependent Variable: ED staff health care problems



Corrected Model 11.273 7 109 1.610 1.988 0.063

Intercept 1111.719 1 109 1111.719 1372.047 0.000

Gender_r 1.580 1 109 1.580 1.950 0.165


Gender_r *

EducationLevel4gps

2.791 3 109 0.930 1.148 0.333

Error 88.319 109 109 0.810

Total 1576.333 117



Squared = .056)

Appendices 280

Appendix L

A3 Problem Solving Sheets

Investigating sheet, page 2

Appendices 281

Investigating sheet, page 3

Appendices 282

Improvement sheet, page 2

Appendices 283

Improvement sheet, page 3