DEVELOPING A FRAMEWORK FOR PLANNING ...eprints.qut.edu.au/50783/1/Ori_Gudes_Thesis.pdfDEVELOPING A FRAMEWORK FOR PLANNING HEALTHY COMMUNITIES: THE LOGAN BEAUDESERT HEALTH DECISION

DEVELOPING A FRAMEWORK FOR

PLANNING HEALTHY COMMUNITIES: THE

LOGAN BEAUDESERT HEALTH DECISION

SUPPORT SYSTEM

Ori Gudes

B.A., M.A

Associate Professor Tan Yigitcanlar, Dr Virendra Pathak, and Professor Elizabeth

Kendall

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Urban Development

Faculty of Built Environment and Engineering

Queensland University of Technology

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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System i

Keywords

Healthy cities

Healthy communities

Health planning

Framework for health information

Collaborative planning

Collaborative health planning

Framework for developing healthy cities and communities

Participatory action research

Decision support systems

Geographic information systems

Decision-making impact

Content analysis

Evaluation

Queensland, Australia

Logan Beaudesert

Logan Beaudesert Health Coalition

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ii Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System

Abstract

In the last few decades, the focus on building healthy communities has grown

significantly (Ashton, 2009). There is growing evidence that new approaches to planning are

required to address the challenges faced by contemporary communities. These approaches

need to be based on timely access to local information and collaborative planning processes

(Murray, 2006; Scotch & Parmanto, 2006; Ashton, 2009; Kazda et al., 2009). However, there

is little research to inform the methods that can support this type of responsive, local,

collaborative and consultative health planning (Northridge et al., 2003).

Some research justifies the use of decision support systems (DSS) as a tool to support

planning for healthy communities. DSS have been found to increase collaboration between

stakeholders and communities, improve the accuracy and quality of the decision-making

process, and improve the availability of data and information for health decision-makers

(Nobre et al., 1997; Cromley & McLafferty, 2002; Waring et al., 2005). Geographic

information systems (GIS) have been suggested as an innovative method by which to

implement DSS because they promote new ways of thinking about evidence and facilitate a

broader understanding of communities. Furthermore, literature has indicated that online

environments can have a positive impact on decision-making by enabling access to

information by a broader audience (Kingston et al., 2001).

However, only limited research has examined the implementation and impact of online

DSS in the health planning field. Previous studies have emphasised the lack of effective

information management systems and an absence of frameworks to guide the way in which

information is used to promote informed decisions in health planning. It has become

imperative to develop innovative approaches, frameworks and methods to support health

planning. Thus, to address these identified gaps in the knowledge, this study aims to develop

a conceptual planning framework for creating healthy communities and examine the impact

of DSS in the Logan Beaudesert area. Specifically, the study aims to identify the key

elements and domains of information that are needed to develop healthy communities, to

develop a conceptual planning framework for creating healthy communities, to

collaboratively develop and implement an online GIS-based Health DSS (i.e., HDSS), and to

examine the impact of the HDSS on local decision-making processes. The study is based on a

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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System iii

real-world case study of a community-based initiative that was established to improve public

health outcomes and promote new ways of addressing chronic disease. The study involved

the development of an online GIS-based health decision support system (HDSS), which was

applied in the Logan Beaudesert region of Queensland, Australia. A planning framework was

developed to account for the way in which information could be organised to contribute to a

healthy community. The decision support system was developed within a unique settings-

based initiative Logan Beaudesert Health Coalition (LBHC) designed to plan and improve the

health capacity of Logan Beaudesert area in Queensland, Australia. This setting provided a

suitable platform to apply a participatory research design to the development and

implementation of the HDSS. Therefore, the HDSS was a pilot study examined the impact of

this collaborative process, and the subsequent implementation of the HDSS on the way

decision-making was perceived across the LBHC.

As for the method, based on a systematic literature review, a comprehensive planning

framework for creating healthy communities has been developed. This was followed by using

a mixed method design, data were collected through both qualitative and quantitative

methods. Specifically, data were collected by adopting a participatory action research (PAR)

approach (i.e., PAR intervention) that informed the development and conceptualisation of the

HDSS. A pre- and post-design was then used to determine the impact of the HDSS on

decision-making.

The findings of this study revealed a meaningful framework for organising information

to guide planning for healthy communities. This conceptual framework provided a

comprehensive system within which to organise existing data. The PAR process was useful in

engaging stakeholders and decision-making in the development and implementation of the

online GIS-based DSS. Through three PAR cycles, this study resulted in heightened

awareness of online GIS-based DSS and openness to its implementation. It resulted in the

development of a tailored system (i.e., HDSS) that addressed the local information and

planning needs of the LBHC. In addition, the implementation of the DSS resulted in

improved decision- making and greater satisfaction with decisions within the LBHC. For

example, the study illustrated the culture in which decisions were made before and after the

PAR intervention and what improvements have been observed after the application of the

HDSS. In general, the findings indicated that decision-making processes are not merely

informed (consequent of using the HDSS tool), but they also enhance the overall sense of

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iv Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System

‗collaboration‘ in the health planning practice. For example, it was found that PAR

intervention had a positive impact on the way decisions were made. The study revealed

important features of the HDSS development and implementation process that will contribute

to future research. Thus, the overall findings suggest that the HDSS is an effective tool,

which would play an important role in the future for significantly improving the health

planning practice.

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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System v

Table of Contents

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

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

Table of contents .................................................................................................................................... v

List of figures ...................................................................................................................................... viii

List of tables ........................................................................................................................................... x

List of abbreviations ............................................................................................................................. xii

Statement of original authorship .......................................................................................................... xiv

Acknowledgments ................................................................................................................................ xv

Awards ................................................................................................................................................ xvii

List of publications ............................................................................................................................. xvii

Access to the health decision support system ...................................................................................... xix

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

1.1 Preview ........................................................................................................................................ 1

1.2 Background ................................................................................................................................. 1

1.3 Research outcomes ...................................................................................................................... 3 1.3.1 Aim, objectives, and research questions .......................................................................... 3

1.4 Overview of the study ................................................................................................................. 4

1.5 Research method ......................................................................................................................... 4

1.6 Research importance and significance ........................................................................................ 8

1.7 Summary ..................................................................................................................................... 8

CHAPTER 2: LITERATURE REVIEW ........................................................................................... 9

2.1 Preview ........................................................................................................................................ 9

2.2 Healthy cities and communities ................................................................................................... 9 2.2.1 Background ...................................................................................................................... 9 2.2.2 The use of evidence in health planning .......................................................................... 12 2.2.3 The use of collaboration in health planning ................................................................... 14 2.2.4 Challenges and opportunities ......................................................................................... 15

2.3 Collaborative health planning.................................................................................................... 15 2.3.1 Background .................................................................................................................... 15 2.3.2 Collaborative planning approaches ................................................................................ 16 2.3.3 Challenges and opportunities ......................................................................................... 19

2.4 Decision support systems .......................................................................................................... 19 2.4.1 Background .................................................................................................................... 19 2.4.2 Spatial decision support systems .................................................................................... 21 2.4.3 Online decision support systems .................................................................................... 22 2.4.4 The Australian context ................................................................................................... 23 2.4.5 Challenges and opportunities ......................................................................................... 25

2.5 Potential outcomes of decision support systems in health planning .......................................... 26

2.6 Summary ................................................................................................................................... 27

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vi Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System

CHAPTER 3: RESEARCH METHOD ........................................................................................... 29

3.1 Preview ...................................................................................................................................... 29

3.2 Overview ................................................................................................................................... 29

3.3 Case study ................................................................................................................................. 30

3.4 A framework for planning a healthy community ...................................................................... 32

3.5 Participatory action research ..................................................................................................... 34

3.6 Participatory action research intervention ................................................................................. 35 3.6.1 PAR cycle 1: introduction stage ..................................................................................... 37 3.6.2 PAR cycle 2: interaction stage ....................................................................................... 37 3.6.3 PAR cycle 3: trialling stage ............................................................................................ 39 3.6.4 Summary ........................................................................................................................ 42

3.7 Participatory action research intervention study ........................................................................ 42 3.7.1 Data collection ............................................................................................................... 43 3.7.2 Data analysis .................................................................................................................. 43

3.8 Decision-making impact study .................................................................................................. 44 3.8.1 Data collection ............................................................................................................... 44 3.8.2 Data analysis .................................................................................................................. 47

3.9 Reliability, validity and ethics ................................................................................................... 50

3.10 Summary ................................................................................................................................... 51

CHAPTER 4: PARTICIPATORY ACTION RESEARCH INTERVENTION ........................... 53

4.1 Preview ...................................................................................................................................... 53

4.2 Background ............................................................................................................................... 53

4.3 Introduction stage ...................................................................................................................... 55

4.4 Interaction Stage ........................................................................................................................ 55

4.5 Trialling stage ............................................................................................................................ 61 4.5.1 User satisfaction survey findings ................................................................................... 61

4.6 System design and architecture ................................................................................................. 66 4.6.1 System design ................................................................................................................ 66 4.6.2 System architecture ........................................................................................................ 67

4.7 Summary ................................................................................................................................... 67

CHAPTER 5: PARTICIPATORY ACTION RESEARCH INTERVENTION STUDY ............. 69

5.1 Preview ...................................................................................................................................... 69

5.2 Background ............................................................................................................................... 69

5.3 PAR cycle 1 ............................................................................................................................... 69

5.4 PAR cycle 2 ............................................................................................................................... 70

5.5 PAR cycle 3 ............................................................................................................................... 72

5.6 Content analysis-based findings ................................................................................................ 72

5.7 Summary ................................................................................................................................... 75

CHAPTER 6: DECISION-MAKING IMPACT STUDY ............................................................... 77

6.1 Preview ...................................................................................................................................... 77

6.2 Background ............................................................................................................................... 77

6.3 Decision-making survey findings .............................................................................................. 78 6.3.1 Pre-PAR intervention phase: survey findings ................................................................ 78

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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System vii

6.3.2 Post-PAR intervention phase: survey findings ............................................................... 83 6.3.3 Comparison between pre and post-PAR intervention decision-making survey findings 88 6.3.4 Decision-making surveys: overall findings .................................................................... 90

6.4 Actual decision-making findings ............................................................................................... 90 6.4.1 Pre-PAR intervention phase: actual decision-making findings ...................................... 91 6.4.2 Post-PAR intervention phase: actual decision-making findings .................................... 98 6.4.3 Actual decision-making: overall findings .................................................................... 110

6.5 Summary ................................................................................................................................. 111

CHAPTER 7: DISCUSSION AND CONCLUSION ..................................................................... 113

7.1 Preview .................................................................................................................................... 113

7.2 Background ............................................................................................................................. 113

7.3 Review of the study objectives ................................................................................................ 114

7.4 Major findings ......................................................................................................................... 117 7.4.1 Overview ...................................................................................................................... 117 7.4.2 key elements and domains of information that are needed for developing healthy

communities ................................................................................................................. 117 7.4.3 A conceptual planning framework for creating healthy communities .......................... 117 7.4.4 Participatory action research Intervention .................................................................... 118 7.4.5 Decision-making impact study ..................................................................................... 119 7.4.6 Summary of major findings ......................................................................................... 121

7.5 Conclusion ............................................................................................................................... 124

7.6 Value and significance of the study ......................................................................................... 125

7.7 Limitations of the study ........................................................................................................... 126

7.8 Recommendations for future research ..................................................................................... 128

7.9 Summary ................................................................................................................................. 128

CHAPTER 8: BIBLIOGRAPHY ................................................................................................... 131

CHAPTER 9: APPENDICES ......................................................................................................... 137

9.1 Decision-making processes questionnaire ............................................................................... 137

9.2 User satisfaction questionnaire ................................................................................................ 140

9.3 HDSS corrections and updates report ...................................................................................... 142

9.4 Logbook .................................................................................................................................. 143

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viii Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System

List of Figures

Figure 1.1 The process of the online GIS-based DSS development ....................................................... 6

Figure 1.2 PAR cycles and the methodological tools developed in the study ........................................ 7

Figure 2.1. The six areas characterising a healthy community (WHO, 1997) ...................................... 10

Figure 2.2. Public health framework for health impact assessment and health profiling (derived from

Schulz & Northridge, 2004) .............................................................................................. 14

Figure 3.1. Logan Beaudesert location map ......................................................................................... 31

Figure 3.2. LBHC structure (the board and its six advisory groups) .................................................... 32

Figure 3.3. A conceptual framework for planning a healthy community (derived from World Health

Organisation 1997; Schulz & Northridge 2004) ............................................................... 33

Figure 3.4. Framework for developing the HDSS by using three PAR cycles ..................................... 36

Figure 4.1. HDSS process of development (PAR cycles) ..................................................................... 54

Figure 4.2. Service area accessibility function ..................................................................................... 59

Figure 4.3. Proximity analysis function ................................................................................................ 61

Figure 4.4. HDSS snapshot ................................................................................................................... 66

Figure 4.5. System architecture ............................................................................................................ 67

Figure 5.1. Introduction stage themes and concepts map (based on minutes from the LBHC board

meeting, April 2010) ......................................................................................................... 73

Figure 5.2. Interaction stage themes and concepts map (derived from Logbook items associated with the

interaction stage) .................................................................................................................. 74

Figure 5.3. Trailing stage themes and concepts map (derived from Logbook items associated with the

trialling stage) ................................................................................................................... 75

Figure 6.1. Themes and concepts map (derived from the pre-PAR intervention phase decision-making

survey) .............................................................................................................................. 82

Figure 6.2. Themes and concepts map (derived from the post-PAR intervention survey) ................... 87

Figure 6.3. Decision-making construct results pre-and post-PAR intervention phases ........................ 89

Figure 6.4. Thematic map of the board meeting (derived from the minutes of meeting conducted on the

08/05/2008) ....................................................................................................................... 92


13/11/2008) ....................................................................................................................... 94


14/05/2009) ....................................................................................................................... 95


08/10/2009) ....................................................................................................................... 98


11/03/2010) ..................................................................................................................... 100


14/10/2010) ..................................................................................................................... 103


10/02/2011) ..................................................................................................................... 106

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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System ix


09/06/2011) .................................................................................................................. 109

Figure 7.1. The spread-effect impact made by the HDSS .................................................................. 122

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x Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System

List of Tables

Table 1.1 Methodological tools developed to address research questions .............................................. 7

Table 2.1 Elements of each level of collaboration (derived from Mattessich et al., 2001, p. 61) ......... 17

Table 3.1 User Satisfaction survey items (derived from Omar & Lascu, 1993, p.6) ............................ 40

Table 3.2 Questionnaire constructs connected to their associated items .............................................. 46

Table 3.3 Constructs of actual decision-making ................................................................................... 50

Table 4.1 Information items survey results........................................................................................... 56

Table 4.2 Features and functionalities selected by LBHC board members for the HDSS prototype ... 57

Table 4.3 Proposed workflow for accessibility function ...................................................................... 58

Table 4.4 Proposed workflow for proximity analysis function............................................................. 60

Table 4.5 Means, standard deviations and frequencies of responses to the five constructs of user

satisfaction survey (Importance) .......................................................................................... 62

Table 4.6 Means, standard deviations and frequencies of responses to the five constructs of user

satisfaction survey (Performance) ....................................................................................... 62

Table 4.7 Means, standard deviations and frequencies of responses to the 23 items of the user satisfaction

survey .................................................................................................................................. 63

Table 4.8 Correlation coefficients between weighted constructs and overall satisfaction item ............ 64

Table 6.1 Means, standard deviations and frequencies of responses to the five dimensions of decision-

making ................................................................................................................................. 79

Table 6.2 ANOVA results by LBHC initiatives pre-PAR intervention phase ...................................... 79

Table 6.3 Comparison of five constructs of decision-making processes with LBHC two major age groups

pre- PAR intervention phase ................................................................................................ 80

Table 6.4 Comparison of five constructs of decision-making processes with LBHC tenure groups pre-

PAR intervention phase ....................................................................................................... 80

Table 6.5 Means, standard deviations and frequencies of responses to the five dimensions of decision-

making processes post-PAR intervention phase .................................................................. 83

Table 6.6 ANOVA results by LBHC initiatives post-PAR intervention phase .................................... 83

Table 6.7 Comparison of five constructs of decision-making processes with LBHC two major age groups

post- PAR intervention phase .............................................................................................. 85

Table 6.8 Comparison of five constructs of decision-making processes with LBHC tenure groups post-

PAR intervention phase ....................................................................................................... 85

Table 6.9 Comparison between the means of five decision-making constructs (pre- and post-PAR

intervention) ......................................................................................................................... 89

Table 6.10 Summary of the actual decisions according to the three decision-making constructs (derived

from meeting conducted on the 08/05/2008) .................................................................... 92







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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System xi


from meeting conducted on the 11/03/2010) ..................................................................... 101







Table 6.18 pre- and post-PAR intervention phases summary of decisions by the three decision-making

constructs ........................................................................................................................... 111

Table 7.1 Data collection tools used to achieve the study objectives ................................................. 115

Table 7.2 Summary of literature review findings and empirical tools developed to address study

objectives ........................................................................................................................... 116

Table 7.3 Theoretical and empirical outcomes of HDSS framework for planning healthy cities and

communities based on research component ....................................................................... 123

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List of Abbreviations

ABS Australian Bureau of Statistics

ANOVA Analysis of Variance

ARC Australian Research Council

ArcGIS Server ArcGIS Server is the a server-based GIS software made by ESRI

ARHPC Adelaide Recommendations on Healthy Public Policy

BMI Body Mass Index

CNAHS Central Northern Adelaide Health Service

CRC-SI Cooperative Research Centre for Spatial Information

DOHWA Department of Health in Western Australia

DSS Decision Support System

ESRI Environmental Systems Research Institute

GIS Geographic Information System

GISCA The National Centre for Social Application of Geographical

Information

GP General Practitioner

HDSS Health Decision Support System

HREC Human Research Ethics Committee

ICT Information Communication Technology

KPIs Key Programme Indicators

LBHC Logan Beaudesert Health Coalition

LEXIMANCER Themes Analysis Tool

MAIGIS Multi-Agency Geographic Information Service

MCC Melbourne City Council

NGOs Non-Governmental Organisations

NHHRC National Health and Hospitals Reform Commission

NHMRC National Health and Medical Research Council

ODSS Online Decision Support System

OHDSS Online Health Decision Support System

OHP Optimal Health Programme

PAR Participatory Action Research

PBI Place-Based Initiative

QH Queensland Health

QUT Queensland University of Technology

SDE Spatial Database Engine

SDH Social Determinants of Health

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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System xiii

SDSS Spatial Decision Support System

SEIFA Socio-Economic Indexes for Areas

SLA Statistical Local Area

SPSS Statistical Package for Social Science

SQL Structured Query Language

WHO World Health Organisation

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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: _________________________

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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System xv

Acknowledgments

I would like to take this opportunity to thank those who have supported me throughout

my thesis journey, and provided assistance in numerous ways. Without this support, this

thesis definitely would not have been possible. Firstly, I would sincerely like to thank my

supervisory team: Associate Professor Tan Yigitcanlar, Professor Elizabeth Kendall, and Dr

Virendra Pathak. Not only have they taught me the necessary skills to be a good researcher,

they have patiently and constantly contributed time and effort to enlighten the complexity

involved in my PhD study. Thus, I am grateful for their responsiveness and guidance which

were vital components towards completion of this study.

I would also like to thank my PhD colleagues in QUT and Griffith University who

assisted me in several occasions, and their help is greatly appreciated. Also, I would like to

thank my friends back home in Israel and elsewhere, who always provided useful suggestions

and ideas when necessary. Special gratitude is also extended to the Logan Beaudesert Health

Coalition, members of which kindly participated in my study and supported me throughout

the study. Sincere thanks also go to staff at Griffith Enterprise and Scholarly Information &

Research Centre who provided exceptional support to make the HDSS a reality. Importantly,

this study would not have been possible without the financial support (i.e., tuition fee waiver

scholarship) from Queensland University of Technology, and my role as a research fellow

and GIS specialist at Griffith University.

Finally, but foremost, I would like to express my gratitude to my family: Edna Gudes,

Jacob Gudes, Yaron Gudes, Ronit Gudes, and my wife Yael Berger-Gudes, who have

unconditionally and endlessly supported me throughout this long PhD journey; I would not

have completed the PhD study without you, I love you. On a personal note, I would like to

dedicate my thesis to a friend who is not with us anymore, Uri Fridman, who taught me the

values of dedication and commitment for your own goal.

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Mapping is fundamental to the process of lending order to the world...

(Rundstrom, 1990)

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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System xvii

Awards

The HDSS project won the Queensland Spatial Excellence Awards under the Research

and Innovation category for 2011.

List of publications

Book chapters

Gudes, Ori, Kendall, Elizabeth, Yigitcanlar, Tan, Hoon Han, Jung, & Pathak, Virendra

(2011) Developing a competitive city through healthy decision-making. In Melih, Bulu

(Ed.) City Competitiveness and Improving Urban Subsystems: Technologies and

Applications. IGI Global, USA.

Yigitcanlar, Tan & Gudes, Ori (2008) Web-based public participatory GIS. In Adam,

Frédéric & Humphreys, Patrick (Eds.) Encyclopaedia of Decision Making and Decision

Support Technologies. IGI Global Publishing, Hershey, Pa, pp. 969-976.

Non Peer reviewed conference papers

Gudes, Ori, Kendall, Elizabeth, Yigitcanlar, Tan, & Pathak, Virendra (2011) Online

geographic information systems for improving health planning practice: lessons learned

from the case study of Logan Beaudesert, Australia. In URISA GIS in Public Health

2011, Atlanta, Georgia, USA.

Peer reviewed conference papers

Gudes, Ori, Yigitcanlar, Tan, Kendall, Elizabeth & Pathak, Virendra (2011) A knowledge-

based approach: the way healthy communities make decisions. In The Fourth Knowledge

Cities World Summit (KCWS) 2011, Bento Goncalves - Brazil.

Gudes, Ori, Pathak, Virendra, Kendall, Elizabeth, & Yigitcanlar, Tan (2011) Thinking

spatially, acting collaboratively: a GIS-based health decision support system for

improving the collaborative health-planning practice. In Traver, Vicente, Fred, Ana,

Filipe, Joaquim, & Gamboa, Hugo (Eds.) Proceedings of the HEALTHINF 2011:

International Conference on Health Informatics, SciTePress - Science and Technology

Publications, Rome, pp. 148-155.

Gudes, Ori, Kendall, Elizabeth, Yigitcanlar, Tan, & Pathak, Virendra (2010) Knowledge-

based approach for planning healthy cities: the case of Logan Beaudesert, Australia. In

The Third Knowledge Cities World Summit (KCWS) 2010, Melbourne - Australia.

Gudes, Ori, Yigitcanlar, Tan, Tal, Yoav, & Bar-Lavi, Yaakov (2009) Innovative cartography

standards for Web-GIS portals: case study of the 'Survey of Israel's' Web-GIS Portal. In

http://eprints.qut.edu.au/39352/














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xviii Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System

Proceeding of the International Federation of Surveyors (FIG) Working Week 2009

Surveyors Key Role in Accelerated Development, 3-8 May 2009, Dan Eilat Hotel, Eilat.

Gudes, Ori, Yigitcanlar, Tan, & Pathak, Virendra (2009) A community health support system

for the planning of healthy cities. In (Ed.) Infrastructure Research Theme Postgraduate

Student Conference 2009, 26 March 2009, Queensland University of Technology,

Brisbane.

Journal papers

Gudes, Ori, Kendall, Elizabeth, Yigitcanlar, Tan, Pathak, Virendra, & Scott, Baum (2010).

Rethinking health planning: a framework for organising information to underpin

collaborative health planning. Health Information Management Journal, 39(2), pp. 18-

29.

Han, Jung Hoon, Sunderland, Naomi, Kendall, Elizabeth, Gudes, Ori, & Henniker, Garth

(2010). Chronic disease, geographic location and socioeconomic disadvantage as

obstacles to equitable access to e-health. Health Information Management Journal,

39(2), pp. 30-36.

Baum, Scott, Kendall, Elizabeth, Muenchberger, Heidi, Gudes, Ori, & Yigitcanlar, Tan

(2010). Geographical information systems: an effective planning and decision-making

platform for community health coalitions in Australia. Health Information Management

Journal, 39(3), pp. 28-33







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Developing a Framework for Planning Healthy Communities: The Logan Beaudesert Health Decision Support System xix

Access to the health decision support system

The HDSS can be accessed by using the following link:

http://gis03.rcs.griffith.edu.au/HDSS/HDSSViewer/index.html

User name / email: examiner password: HDSS

Access to the HDSS support channel: http://www.youtube.com/user/MyHDSS


http://www.youtube.com/user/MyHDSS

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1

Chapter 1: Introduction


1.1 PREVIEW

Chapter 1 introduces this PhD study and provides its storyline. It outlines the

background and the problem statement of the study, aim and objectives, research questions,

overview of the study, method and design, importance and significance.

1.2 BACKGROUND

In the last few decades, the focus on building healthy communities has grown

significantly. This trend is the result of an international initiative (i.e., healthy cities1

movement) to create the broad conditions that contribute to health rather than continuing to

treat burgeoning levels of disease. As part of these efforts, the process of developing healthy

communities has become an important focus for health planners. There is growing evidence

that new approaches to planning are required. These approaches need to be based on timely

use of local information, collaborative health planning and the engagement of the

communities in decision-making (Murray, 2006; Scotch & Parmanto, 2006; Ashton, 2009;

Kazda et al., 2009).

The National Health Survey of 2008 (ABS, 2008) reported that 1 in 4 Australian adults

smoked regularly and 15.3% consumed alcohol at a rate that would be highly risky to their

health. For people over 18 years, 60% of were classified as overweight according to a self-

reported Body Mass Index (BMI), and growing numbers of people consume inadequate fruit

or vegetable. There was significant evidence that these risk indicators had worsened since the

2008 survey. Evidently, risk indicators in the Logan Beaudesert area were considered higher

than the Australian average (Kendall et al., 2007). The cost of chronic disease to society

remains significant and current management and planning methods do not appear to be

having sufficient impact to address these issues (Gudes et al., 2010). Consequently, new

methods of collaborative health planning are seen as being important for progressing health

planning to address chronic issues in society.

1 The ‗healthy cities movement‘ is hereafter referred to as ‗healthy communities‘ when appropriate to do so. However, the

distinction between these elements is acknowledged. The ‗healthy communities‘ term is used more commonly in the USA

than the ‗healthy cities‘ term which pertains to the specific framework designed by the World Health Organisation (WHO).

2


One response by the Logan Beaudesert community has been the establishment of

collaboration among Griffith University, Queensland University of Technology, Queensland

Health, Logan City Council, and Scenic Rim City Council, to promote new ways of

addressing chronic diseases. This collaborative secured an Australian Research Council

(ARC) funded project entitled ‗Coalitions for Community Health: A Community-based

Response to Chronic Disease‘ which was held in the Logan Beaudesert area during 2007-

2011. The aim of this ARC project was to examine the effectiveness of the community health

coalition to improve the management of chronic conditions within a particular community

(Logan Beaudesert). Within the ARC project, this PhD focuses on planning for healthy

communities. Specifically this study focuses on the type of information required to plan for

health communities. It then uses participatory processes to develop an online GIS-based DSS

to facilitate informed decision-making among health planners. Then, the study examines the

impact of this tool on decision-making.

In recent years, a few health community initiatives have emerged. For example,

community and university research collaboration has become a major strategic theme of

health funding agencies in Canada and elsewhere (Buckeridge et al., 2002). The literature

suggests that the involvement of affected local stakeholders in planning improves the ability

to address health inequalities (Kirsten & Rushton, 2009). However, there is little research in

relation to the methods that support this type of responsive, local, collaborative and

consultative approach to health planning (Northridge et al., 2003). Thus, in order to address

questions of interest and overcome health problems, the literature emphasises that more

attention should be directed to the development of adequate information tools and strategies

(Kirsten & Rushton, 2009).

Some research justifies the use of decision support systems (DSS) in planning for

healthy communities. DSS have been found to increase collaboration between stakeholders

and communities, improve the accuracy and quality of decision-making processes, and

improve the availability of data and information for health decision-makers (Nobre et al.,

1997; Cromley & McLafferty, 2002, Waring et al., 2005). Geographic Information Systems

(GIS) have been suggested as an innovative way of implementing DSS. GIS provides access

to spatial and visual information which subsequently enables a new way of thinking about

health. Research indicates that online DSS have a positive impact on decision-making by

enabling access for a broader audience (Kingston et al., 2001). The literature stresses that

improvements in data storage and information processing have increased the capacity of

3


organisations and have made the cost of specific software, licensing, and hardware

manageable. Thus, it is possible now more than ever before for decision-makers and

communities to make use of information tools through online environments in health

planning.

However, very limited research has been conducted in this area to date, especially in

terms of evaluating the impact of DSS on stakeholders and decision-makers. Previous studies

emphasise that due to the lack of effective information systems and an absence of

frameworks for making informed decisions in health planning, it has become imperative to

develop innovative frameworks and tools in health planning practice (Higgs & Gould 2001).

The following knowledge gaps have been identified in the health planning literature (National

Health and Hospitals Reform Commission, 2008; Kazda et al., 2009):

Lack of methods to develop DSS tools in a collaborative manner;

Lack of knowledge about GIS applications for decision-makers in the health

planning field;

Lack of focus on the usage of geographical information;

Lack of frameworks for organising information; and

Lack of knowledge about the potential impact of DSS on decision-making

processes.

This represents a serious gap in the knowledge required for improving health planning

to create healthy communities. The current study aims to address this gap in the theory, by

using a case study of local health coalition (i.e., Logan Beaudesert Health Coalition) involved

in the development and use of online GIS-based DSS for decision-making.

1.3 RESEARCH OUTCOMES

1.3.1 AIM, OBJECTIVES, AND RESEARCH QUESTIONS

The study aims to develop a conceptual planning framework for creating healthy

communities and examining the impact of DSS in the Logan Beaudesert. To achieve this aim,

the following objectives were identified:

To identify the key elements and domains of information that are needed to

develop healthy communities;

To develop a conceptual planning framework for creating healthy communities;

4


To collaboratively develop and implement an online GIS-based Health DSS (i.e.,

HDSS); and

To examine the impact of the HDSS on local decision-making processes.

The study, therefore, addresses the following research questions:

What information is needed to develop healthy communities?

How is an HDSS (online GIS-based health DSS) developed and implemented?

How do end-users respond to HDSS (i.e., user satisfaction)?

What impact can an HDSS have on decision-making processes (i.e., the way

evidence, consensus, and participation have been perceived)?

1.4 OVERVIEW OF THE STUDY

The structure of this thesis systematically addresses these research questions. First,

Chapter 2 examines the literature on healthy communities and collaborative planning. It

highlights the problems associated with current health planning approaches and argues the

need for new health planning frameworks and tools. The chapter explores the potential of

DSS in health planning, and discusses the challenges and opportunities created by online

GIS-based DSS. Chapter 3 develops an appropriate research design, and a conceptual

framework for the study. It describes the case study site, data collection and analysis

methods. Chapter 4 involves the application of a collaborative planning approach (i.e.,

Participatory Action Research [PAR]) to develop an online GIS-based DSS. It describes the

design, development and implementation process which was undertaken. Chapter 5 examines

three PAR cycles (i.e., PAR intervention) executed to develop and implement the system. It

discusses issues of impotence to stakeholders and decision-makers during the process of

HDSS development and implementation. Chapter 6 reports on how decisions were made

before and after the PAR intervention. In addition, it explores stakeholders and decision-

makers perceptions of decision-making. Finally, the findings are evaluated and discussed in

Chapter 7, and conclusions are drawn.

1.5 RESEARCH METHOD

This study is based on a conceptualisation of the current literature which produced a

planning framework including a model of health information to guide the development of the

online GIS-based DSS. A case study design is used to examine the partial application of this

5


conceptual framework (see section 3.4) to the development and implementation of an online

GIS-based DSS within a specific region (i.e., Logan Beaudesert). Specifically, the study

focuses on the decision-making of a local coalition of health planners (i.e., LBHC) who were

engaged in a healthy communities‘ initiative.

Participatory Action Research (PAR) is used to facilitate the collaborative development

and implementation of the online GIS-based DSS. PAR was seen as an appropriate design for

this study given that it engages end-users in the generation of initiatives that affect their lives.

This feature is considered critical to the process of developing and implementing a

meaningful method for health planning. The PAR intervention involved three iterative cycles

designed to: 1) raise awareness, 2) design and develop an online GIS-based DSS, and 3)

implement and trial the system. PAR cycle 1 (introduction stage) aimed to increase GIS

awareness within the LBHC; PAR cycle 2 (interaction stage) aimed to design and develop the

online GIS-based DSS system; and PAR Cycle 3 (trialling stage) aimed to implement the

system and understand its usage. Continuous measurement occurred during the PAR process

or PAR intervention (i.e., a Logbook of interactions with stakeholders; survey of information

priorities, workshops to collect opinions about features, functionality and health scenarios,

survey of user satisfaction and usage statistics). The purpose of this data collection was to

examine the process of developing the online GIS-based DSS and to understand the

perceptions of key stakeholders and decision-makers as they implemented and used the

system.

A pre- and post design was used to examine the impact of the online GIS-based DSS on

decision-making within the LBHC. Two waves of data collection (i.e., pre-and post-PAR

intervention) were used to explore and understand decision-making before and after the PAR

intervention. Quantitative methods (i.e., questionnaires) were used to examine the perceptions

of LBHC members about the decisions made by the coalition. In line with existing research in

this area, the study focuses on the extent to which decisions were based on evidence,

participation and consensus. Questionnaires also measured the satisfaction of members with

information used to underpin decisions and the importance of decisions. Qualitatively,

observational data were used to examine the way in which actual decisions were made by

decision-makers in the LBHC (i.e., LBHC board members). Qualitative data were analysed

using content analysis to reveal the most frequently occurring concepts and issues. Where

appropriate, Leximancer was used to assist in the analysis. Quantitative data were analysed

using SPSS 19. T-tests were used to examine the changes in decision-making over time.

6


Independent samples were used rather than a longitudinal design to control for the significant

attrition in the LBHC population and membership over time. Figure 1.1 provides broad

information about the process of the online GIS-based DSS development. It illustrates the

broad process that was adopted in this study to develop the HDSS. The first stage focused on

identifying health information items based on comprehensive literature review and feedback

attained by users. The second stage consisted of collaboratively designing the system by

adopting a PAR approach (i.e., PAR intervention). The third stage involved evaluation of the

system and its observed impact on decision-making. The last stage involved refinement and

improvement of the system based on users‘ feedback.

Identify health information items

Collaboratively design and implement a solution (i.e., HDSS) using PAR approach

Evaluate the solution and its impact

(does it meet the users’ needs?)

Refinement of the HDSS(Refine and improve the tool)

Figure 1.1 The process of the online GIS-based DSS development

Figure 1.2 provides specific information about the PAR intervention. It illustrates the

three PAR cycles (i.e., PAR intervention) and the methodological tools that have been

developed to systematically address the research questions. In summary, the system was a

pilot study that enabled examination of the initial impact of collaboratively developing this

tool, and its subsequent implementation of the way decision-making was perceived and made

across the LBHC.

7


Figure 1.2 PAR cycles and the methodological tools developed in the study

Table 1.1 illustrates how these methods relate to the research questions and how

specific method tools were developed to address research questions.

Table 1.1 Methodological tools developed to address research questions

Data collection tools /

Methods

Research question

What

information is

needed to

develop healthy

communities?

How is an

HDSS (online

GIS-based

health DSS)

developed and

implemented?

How do end-users respond

to HDSS (i.e., user

satisfaction)?

What impact can an

HDSS have on

decision-making

processes (i.e., the

way evidence,

consensus, and

participation have

been perceived)?

Literature review

Case study

PAR intervention in

three cycles

Logbook

Decision-making

surveys

Observational data of

actual decision-making

8


1.6 RESEARCH IMPORTANCE AND SIGNIFICANCE

The study aims to make a significant contribution to the health planning literature and

body of knowledge in the following areas:

Theoretical – Broadly, this study improves understanding of the complex planning

processes required to develop healthy communities in Australia. In particular, this study

suggests theoretical and technical directions to develop a framework for improving the way

decisions are made in the health planning field.

Practical – Only a few literature studies indicate that DSS has been successfully

employed in health planning initiatives. Currently, there is no DSS that is effectively able to

improve decision-making processes for the planning of healthy communities in Australia.

Therefore, the results of this research are unique. The conceptual framework, approaches and

methodological tools used in this study can contribute to Australian health planning outcomes

by improving decision-making processes, increasing collaboration between stakeholders and

thereby improving health care services for the communities. The method used to collect and

analyse the data in the Logan Beaudesert can be transferred to other regions within Australia,

and thus contribute to the development of healthy communities. In summary, this study has

established a new line of knowledge to guide the future use and development of DSS within

the health planning field.

1.7 SUMMARY

The chapter introduced the required background and problem statement of the study.

Subsequently, the aim, study objectives and research questions were drawn. Finally, the

research overview and methods scope were provided, and the importance and significance of

this study were highlighted.

9

Chapter 2: Literature Review


2.1 PREVIEW

Chapter 2 provides a comprehensive literature review of the study topic. The literature

review covers the extant literature on all the required aspects of the study objectives.

Amongst the discussed topics are the healthy cities approach, collaborative planning, decision

supports systems, and its potential outcomes in decision-making processes. This, in turn,

formed the knowledge base required for the study.

2.2 HEALTHY CITIES AND COMMUNITIES

2.2.1 BACKGROUND

In many cities around the world, the cost of health to society will significantly increase

within the next few decades (Anderson et al., 2006). Varying responses have emerged

regarding the best way to address rising health costs, one of which is the healthy cities

initiative. This initiative was officially introduced in 1986 by Ilona Kickbusch at a conference

of the World Health Organisation (WHO) in Copenhagen, Denmark (WHO, 1999). The most

commonly used definition of a healthy city is as follows: “One that is continually creating

and improving those physical and social environments and strengthening those community

resources which enable people to mutually support each other in performing all the functions

of life and achieving their maximum potential” (Flynn, 1996, p. 300).

Defining a healthy city is vital for planning purposes so that health planners are clear

about the preferred outcomes. In this regard, Duhl and Sanchez (1999) defined a list of

fundamental characteristics that would need to occur to create a healthy city and community:

a commitment to health, strategic planning that promotes health, intersectoral action, public

participation in health, innovation and healthy public policy. Figure 2.1 illustrates these six

characteristics of a healthy city and community (WHO, 1997). Adoption of these

characteristics may lead to the emergence of a healthy city or healthy community. In this

regard, the WHO (1997) has described a range of attributes or qualities that should be evident

in a healthy city and community, amongst them: high health status, appropriate health,

supplying basic needs, high quality of physical environment, innovative city economy, access

to resources, high degree of participation, community support, and encouragement of

connectedness.

10


Health public

policy Innovation Community

participation

Intersectoral

action

Political

decision-making

Commitment to

health

High health status

Appropriate health

Basic needs

Quality of

environment

Innovative city

economy

Access to variety of

resources

High degree of

participation

Supportive

community

Encouragement of

connectedness

High degree of

participation


resources

Encouragement of

connectedness

Encouragement of

connectedness

High health status

Sustainable

ecosystem

Basic needs

Quality of

environment

Area o

f resu

lts

Healt

h o

utc

om

es

Qu

alitie

s

Figure 2.1. The six areas characterising a healthy community (WHO, 1997)

In order to plan effectively for healthy communities, it is necessary to revive the

historic collaboration between urban planning and public health professionals, and together

conduct informed decision-making (Northridge et al., 2003). In other words, health-planning

efforts must focus on the creation of structures and processes that actively work to dismantle

existing health inequalities and create economic, political, and social equality (Schulz &

Northridge, 2004).

Although more than 20 years have passed since the initiation of the healthy cities

movement, there is some evidence that it has not yet achieved its full potential (Ashton,

2009). However, the founder of the healthy cities movement (i.e., Kickbusch) recently called

for a renewal of the commitment (Ashton, 2009), on the basis that the urban agenda has

become more relevant. Trends such as rapid urbanisation, unsustainable development, and

global warming have highlighted the focus of urban health. Towns, cities and communities

committed to promoting health and sustainability now face two key challenges: how to move

health promotion from the margins to the mainstream; and how to integrate multiple forms of

information and sectors in such a way that planning can contribute to the development of

healthy communities (Dooris, 1999).

The promotion of ‗healthy‘ public policy has been noted as being central to the healthy

cities approach (Flynn, 1996). However, the healthy cities concept necessitates planning that

moves beyond current approaches. It requires planning that focuses on the whole community

11


and the promotion of health, rather than being confined to the development of responses to

one or more specific health problems based on a narrow body of knowledge. Healthy cities

are based on models of city governance in which public authorities recognise the need to

work with and support a range of actors who are either fully committed to health or play a

significant role in contributing to the conditions that promote health (WHO, 1999). Thus, the

healthy cities concept suggests the need to restructure health decision-making processes, by

shifting power to the local level and basing decisions on a localised but comprehensive body

of knowledge. Planning for healthy communities requires collaboration between different

groups in the community that can contribute to health-promoting conditions, such as local

government, community organisations, universities, private organisations, and health

services. Hence, decision-makers and stakeholders must be able to formulate health-planning

if they are to promote healthy cities policies that are more comprehensive.

To develop a health plan in a certain city, it is imperative to establish a vision of the

city and to understand its strengths and weaknesses. For instance, the WHO (1997) stated that

health plans should be based on a good understanding and knowledge of community needs.

For example, the city of Kuching in Malaysia conducted a survey that asked people to list

their five likes and dislikes of the city (WHO, 1999). This method of consultation often leads

to a long-term health plan with priorities that are already integrated in accordance with

community needs. Indeed, Northridge et al. (2003) argued that collaboration between urban

planners and public health practitioners may be essential to achieve the type of planning that

would lead to a healthy city.

A growing number of case studies show how city authorities have developed innovative

and successful solutions to address health and environmental problems. For instance, the city

of Belo Horizonte, Brazil, developed a participative budgeting approach, which allows for a

higher level of involvement from citizens in setting priorities for municipal investments

(WHO, 1999). Another example is that of the city of Cali, Columbia, where a set of

municipal programmes were launched to improve housing conditions, reduce poverty, and

improve environmental conditions. These programmes occurred with the full cooperation of

the city‘s authorities, non-governmental organisations (NGOs), and the Catholic Church

(WHO, 1999). Consequently, with good health management and awareness of a city‘s health

capacity that is integrated with a public participatory approach, cities can become healthy

places (WHO, 1999). The healthy cities approach represented a new vision for cities as part

of a global community, and was seen as a potential driver for change that was so desperately

12


needed (Ashton, 2009). As urban planners work at the interface between the built

environment and social context (applying the knowledge of social science and urban design

to generate the physical configurations of cities), it is believed that stronger collaborations

between urban planners and public health practitioners may prove effective in designing and

planning for healthy communities (Northridge et al., 2003).

The healthy city approach is based on the process of building health, not just the

eventual outcome. A Healthy City and community is committed to health and has the

structures in place to work towards further improvements (WHO, 1997). Current research has

shown that the greatest likelihood of successful health outcomes has been achieved through a

process that facilitates the engagement and ownership of multiple parties (stakeholders,

community members, local authorities) (Scotch & Parmanto, 2006). Furthermore, the process

of health planning decision-making should be based on a structured model that draws

together multiple forms of knowledge, and increases the possibility of coherent localised and

responsive solutions (Scotch & Parmanto, 2006).

In terms of the process involved in creating a healthy city and community, Flynn (1996)

suggested the following steps: establishing a broad structure for the community, encouraging

community participation, assessing community needs, establishing priorities and strategic

plans, soliciting political support, taking local action, and evaluating progress. Despite the

presence of these guidelines for creating healthy cities and communities, there is little

consensus about how health planning can best contribute to the process (Duhl & Sanchez,

1999). Thus, the importance of the healthy cities approach is measured by its capacity to

encourage collaborative decision-making which is based on use of evidence, participation and

consensus, and then transferred into informed actions.

2.2.2 THE USE OF EVIDENCE IN HEALTH PLANNING

One of the reasons that health planning has not been able to contribute to the healthy

cities movement is that there are no models to define the type of information that must be

collected for the use of health planners, and there is no method for sharing this information in

a meaningful form. The literature emphasises that health planners were focused on narrow

data (e.g., diseases ratio, number of hospitalisations etc.); however, this data led to planning

for diseases not for health. As Flynn (1996) stated every community is unique with different

physical, social, political, and cultural contexts that must be understood in the planning

process. Given this, it is necessary for planners to develop a thorough understanding of each

individual community health profile and those features that influence health. Schulz and

13


Northridge (2004) developed a public health framework for health impact assessments

(Figure 2.2). This framework summarises the different levels of factors that influence health

and, therefore, should be considered in health planning. According to Northridge et al.

(2003), factors that contribute to health can be divided into four levels: Macro, Meso, Micro

and Individual. According to the model, these factors interact to contribute to health in

communities.

For instance, the natural environment, macro social factors, and inequalities

(fundamental factors) influence health outcomes and well-being (individual level factors) via

multiple pathways through differential access to power, information, and resources. These

fundamental factors, in turn, influence intermediate factors (the built environment and the

social context). Intermediate factors include the development of land use policies.

Consequently, it is at this level that the impact of the built environment is especially subject

to policy management by planners. The proximate factors (the usual focus of public health

practitioners) include three domains: stressors, social integration or social support, and health

behaviours. The proximate factors have been given the greatest scientific attention over the

years (Northridge et al., 2003), but are influenced by many other factors that have escaped

research attention.

The last column in Figure 2.2 contains two domains: health outcomes and well-being,

and these in turn influence the individual habitués. Figure 2.2 illustrates the interactive and

dynamic relationships among the various domains, between the fundamental and intermediate

factors as well as between the intermediate and proximate factors, and their impacts on health

outcomes during an individual‘s course of life. The relationships within the model in terms of

health outcomes are clearly influenced by broader factors such as where and how people live.

However, for health planners the primary interest in the model is the influence of health

outcomes.

14


I. FUNDAMENTAL

(Macro level)

Natural environment

(topography, climate,

water supply)

Macro social factors

Historical conditions Political orders Economic order Legal codes

Human rights doctrines Social and cultural

institutions

Ideologies (racism, social justice, democracy)

Inequalities Distribution of material

wealth

Distribution of employment opportunities

Distribution of educational

opportunities Distribution of political

influence

II. INTERMEDIATE

(Meso/community level)

Built environment

Land use (industrial,

residential, mixed use or single use)

Transportation systems

Services (shopping, banking, health care facilities, waste transfer

stations)

Public resources (parks, museums, libraries)

Zoning regulations Buildings (housing,

schools, workplaces)

Social context Community investment

(economic development, maintenance, police services)

Policies (public, fiscal, environmental, workplace)

Enforcement of ordinances (public, environmental,

workplace) Community capacity Civic participation and

political influence

Quality of education

III.PROXIMATE

(Micro/interpersonal level)

Stressors

Environmental, neighbourhood,

workplace and housing

conditions Violent crime and safety Police response

Financial insecurity Environmental toxins

(lead, particulates)

Unfair treatment

Health behaviours Dietary practices Physical activity

Health screening

Social integration and social support

Social participation and integration

Shape of social

networks and resources available within networks

Social support

IV. HEALTH & WELL-BEING

(Individual or population levels)

Health outcomes

Infant and child health

(low birth weight, lead poisoning)

Obesity

Cardiovascular diseases Diabetes Cancers

Injuries and violence

Infectious diseases Respiratory health

(asthma) Mental health All-cause mortality

Well-being Hope/despair

Life satisfaction Psychosocial distress Happiness

Disability

Body size and body image

Figure 2.2. Public health framework for health impact assessment and health profiling (derived from Schulz &

Northridge, 2004)

2.2.3 THE USE OF COLLABORATION IN HEALTH PLANNING

In accordance with the WHO‘s Ottawa Charter, the aim of municipal public health

planning is to assist communities to build healthy public policy, create supportive

environments, strengthen community actions, develop personal and collective skills by

providing learning opportunities, and reorientate health services (Logan City Council, 2003).

The underlying fundamental causes of health problems are rarely given adequate attention.

Without good management, cities can become dangerous and unhealthy places (WHO, 1999).

For instance, in 1999 it was reported that more than a third of the urban population in Africa,

Asia, and Latin America live in inadequate conditions where their health is constantly under

threat (Satterthwaite, 1999). Clearly, decisions made by governments today become the

determinants of future health status, with city authorities having a particular role to play in

health - not only in investment, planning, and management but also in encouraging and

supporting the initiatives and innovations of other groups within the city (WHO, 1999). Thus,

the responsibility of a healthy city and community is to identify and respond to all key actors

within the city using collaborative and participatory methods.

15


2.2.4 CHALLENGES AND OPPORTUNITIES

Health public policy plays an essential role in the creation of healthy cities and

communities, and therefore it is central to the movement (WHO, 1997). The Adelaide

Recommendations on Healthy Public Policy (ARHPC), adopted in 1988, noted that one of

the main challenges for the future was to reorient the policies of all key actors in the

community towards equity, health promotion, and disease prevention (ARHPC, 1988). In

general, these polices can lead to political support, an essential component of local action.

Thus, the system for making political decisions in the city is arguably the most important

environmental factor for health (WHO, 1997). The city must provide a vehicle for two-way

communication between the political system and other partners (WHO, 1997).

Communication empowers individuals and groups to take action for health initiatives.

Successful projects must work for greater awareness of the principle of health for all, and

must understand how this principle is applied in practice. Efforts to increase awareness and

understanding of these issues must be comprehensive, consistent, and continuous.

The success of healthy cities initiatives in laying the groundwork for healthy public

policy depends upon their ability to generate innovation in several areas (WHO, 1997).

Innovation depends on the creation of a climate that supports change, and spreading

knowledge through innovative programmes and practices is essential. For instance, the city of

Kuching, Malaysia, provided an interesting example by initiating the ‗Healthy Cities Week‘,

which promotes and creates an appropriate climate for innovation (WHO, 1999; Kuching

Healthy City Annual Report 2002). Thus, collaboration is the climate catalyst of the healthy

cities approach.

2.3 COLLABORATIVE HEALTH PLANNING

2.3.1 BACKGROUND

The literature supports the application of collaborative health planning to the healthy

cities approach (WHO, 1997, Ashton, 2009). First, collaborative planning promotes

democratic decision-making that facilitates shared ownership and engagement in solutions

(Mattessich et al., 2001). Second, it encourages planners to communicate, interact, and

negotiate with other sectors in order to resolve disputes between groups that may have some

investment in the planning process (Campbell & Fainstein, 1996). Third, it facilitates a more

collaborative form of governance, which in turn implies a more collaborative and efficient

delivery of health promotion practices (Bishop & Davis, 2001).

16


2.3.2 COLLABORATIVE PLANNING APPROACHES

Collaborative planning approaches are increasingly being advocated and implemented

in healthy communities initiatives due to the benefits of these approaches (Murray, 2006),

including the ability to:

Combine information, knowledge, and skills from multiple stakeholders

(Margerum, 1999);

Generate agreement over solutions (Innes & Booher, 1999);

Create a sense of ownership over the outcomes (Mitchell, 1997);

Increase support for implementation (Mitchell & Hollick, 1993);

Open communication channels between participants (Buchy & Race, 2001);

Achieve mutual learning and personal growth for participants (Healey, 1997;

Sager, 1994; Buchy & Race, 2001);

Increase democratisation of the decision-making process (Forester, 1989: Sager,

1994: Healey, 1997).

Mattessich et al. (2001, p. 59) defined collaboration as: “A mutually beneficial and well-

defined relationship entered into by two or more organizations to achieve common goals. The

relationship includes a commitment to mutual relationships and goals; a jointly developed structure

and shared responsibility, mutual authority and accountability for success; and sharing of resources

and reward”. These authors suggested a model to evaluate the level of collaboration (i.e.,

cooperation, coordination, and collaboration) in planning. Table 2.1 describes the different

planning elements for each level of this planning. Given the current levels of partnership to

promote healthy cities, it is likely that collaborative planning practice will form a

fundamental part of health planning in the future. Amongst the relevant theories,

communicative planning theory represents the most appropriate paradigm to underpin, inform

and shape collaborative planning practice (Healey, 1993). This theory relates to any

collaborative planning initiative where all partners and stakeholders are committed to the

shared vision of a healthy community and are seeking the development of appropriate health

polices in a democratic and adequate manner.

Ridley and Jones (2001) argued that the most significant forms of collaboration are

those that become part of the day-to-day practice of health care and health planning. In

collaborative health planning, a clear understanding and recognition of the stakeholder

17


relationships, behaviour patterns, weaknesses, strengths and potential outcomes is necessary.

To obtain such an understanding, it is imperative to have feedback from external parties (e.g.,

observer, external research group etc.) on the performance of particular group of decision-

makers. This, in turn, may empower decision-makers and improve their dialogue.

Investing in novel collaborative approaches to health planning requires several

investments, for example, increasing access to essential information, engaging stakeholders

or the community, and planning in a participatory manner. However, there are no recipes or

fixed formulae to implement such approach. Indeed, it is possible to use one planning method

to develop another (Ridley & Jones, 2001).

Table 2.1 Elements of each level of collaboration (derived from Mattessich et al., 2001, p. 61)

Essential

Elements

Cooperation Coordination Collaboration

Vision and

Relationships Basic for cooperation is

usually between

individuals but may be

mandated by third party

Organisational missions

and goals are not taken

into account

Interaction is on an as

needed basis; may last

indefinitely

Individual relationships are

supported by the

organisations they represent

Missions and goals of the

individual organisations are

reviewed for compatibility

Interaction is usually

around one specific project

or task of definable length

Commitment of the

organisation and their

leaders is fully behind

their representatives

Common, new mission

and goals are created

One or more projects

are undertaken for

longer-term results

Structure

Responsibilities,

and

Communication

Relationships are

informal; each

organisation functions

separately

No joint planning is

required

Information is conveyed

as needed

Organisations involved take

on needed roles, but

function relatively

independent of each other

Some project-specific

planning is required

Communication roles are

established and definite

channels are created for

interaction

New organisational

structure and/or clearly

defined and

interrelated roles that

constitute formal

division of labour are

created

More comprehensive

planning is required

that includes

developing joint

strategies and

measuring success in

terms of impact on the

needs of those served

Beyond

communication roles

and channels for

interaction, many

―levels‖ of

communication are

created as clear

information is a

keystone of success

18


Essential

Elements

Cooperation Coordination Collaboration

Authority and

Accountability Authority rests solely

with individual

organisation

Leadership is unilateral

and control is central

All authority and

accountability rests with

the individual

organisation which acts

independently

Authority rests with

individual organisations,

but there is coordination

among participants

Some sharing of leadership

and control

There is some shared risk,

but most of the authority

and a accountability falls to

individual organisations

Authority is

determined by the

collaboration to

balance ownership by

the individual

organisations with

expediency to

accomplish purpose

Leadership is

dispersed, and control

is shared and mutual

Equal risk is shared by

all organisations in the

collaboration

Resources and

Rewards Resources (staff time,

dollars, and capabilities)

are separate, serving the

individual organisation‘s

needs

Resources are

acknowledged and can be

made available to others for

a specific project

Rewards are mutually

acknowledged

Resources are pooled

jointly secured for a

longer-term effort that

is managed by the

collaborative structure

Organisations share in

the products; more is

accomplished jointly

than could have been

individually

The literature described collaborative planning under a number of terms (Murray,

2006). The terms include communicative planning (Healey, 1997), building consensus (Innes

& Booher, 1999), cooperation (Yaffee, 1998), coordination (Margerum, 1999) and

partnerships (Mitchell, 1997). Bentrup (2001, p. 740) presented the key elements which are

associated with collaborative planning approaches:

Integration;

Stakeholders educate each other;

Informal face to face dialogue among stakeholders;

Continuous stakeholder participation throughout the planning process;

Stakeholder participation encouraged to create a holistic plan;

Use of information to determine facts; and

Generally, consensus is used to make decisions.

Despite the lack of unified definition of collaborative-based planning approaches, it

was observed that the use of evidence and information, consensus, and participation in

decision-making processes were repeatedly discussed in the literature.

19



Although practicing in a collaborative manner is likely to positively influence the

planning of healthy communities, there are several challenges. Creating and sharing new

knowledge is considered to be one of the main challenges. For example, utilising and

channelling the particular skill and knowledge of a group of decision-makers, in addition to

coordinating efforts between practitioners in public health agencies, researchers, planners,

and community groups to address the conceptual, informational, and technological needs in

health planning (Cromley & McLafferty, 2002). Specifically, Higgs and Gould (2001)

identified a particular lack of collaboration between academics and health care practitioners

or managers and/or system developers and users (Buckeridge et al., 2002). Thus, it has been

recognised that an intensive time commitment is required to develop mutual understanding

and effective working environment, collegiality and collaborative relationships (Buckeridge

et al., 2002).

The literature has revealed a few challenges for collaborative health planning. For

instance, Kazda et al. (2009) identified challenges such as the lack of community readiness,

inadequate priority for prevention, balancing of taking action at present as opposed to

capacity building in the long term, and insufficient attention to the process of technology and

transfer of knowledge. Being responsive to these challenges may lead to improved

intervention planning and public participation procedures within the community. Developing

an awareness of these existing challenges may provide opportunities to facilitate decision-

making processes that can invoke environmental changes through collaborative health

planning. As Croner (2003) pointed out, it is clear that robust health planning practice

depends on the use of consensus approaches. The key to participation, collaboration and

consensus in health planning, is information that can be easily shown and discussed (i.e.,

online spatial medium). However, there are few examples of sharing information in this way

in health planning. As Internet technology improves, it will become an integral platform for

health planning DSS.

2.4 DECISION SUPPORT SYSTEMS

2.4.1 BACKGROUND

DSS are types of information communication technology (ICT) that can be applied in

online environments, and provide the mechanisms to help decision-makers and related

stakeholders to assess complex problems and solve those problems in a meaningful way

20


(Shim et al., 2002). The overall aim of DSS (without substituting decision-makers) is to

improve the efficiency of the decisions made by stakeholders, optimising their overall

performance and minimising judgemental biases (Turban, 1993).

By definition, DSS incorporates two main domains: 1) policy, which entails making

decisions to solve problems; and 2) technology, which uses computational problem solving

tools. In recent years, decision-making processes have become more challenging than ever

because of the number of unstructured or semi-structured problems that communities face

(Simon, 1960; Gorry & Morton, 1971; Shim et al., 2002). Monitoring these problems and

making the correct decisions are challenging and requires attention to multi-faceted

considerations such as costs, benefits, time span, contingent effects of actions, and

stakeholder involvement (Dur et al., 2009). Individual experience and judgement are still the

most widely used methods in policy decision-making processes. However, as Dur et al.

(2009) pointed out, although it is necessary within a democratic procedure to respect the

choices of decision-makers, these inconsistent methods can no longer meet the challenges of

today‘s communities.

The impact of ICT on decision-making is significant. In recent years, improvements in

both solution methods and algorithm structures have increased the problem solving ability of

DSS (Dur et al., 2009). In particular, new linear programming solutions and other inference

techniques such as neural networks, genetic algorithms and fuzzy logic have played crucial

roles in these developments (Shim et al., 2002). As Nobre et al. (1997) observed, there is a

general recognition that the creation of health geographical-based information may increase

the perception of current health problems and their relation to other variables, as well as

provide better insight into historical trends and differences between regions. The use of DSS

increases efficiency by providing the timely and rapid assessment of epidemiological patterns

relevant to decision-making (Nobre et al., 1997). However, as Bharati and Chaudhury (2004)

pointed out, decision confidence and decision effectiveness are the main outputs from a DSS,

which, in turn, may support collaborative health planning by creating a ‗knowledge

community‘. Salmon (2004) suggested that constructing knowledge is one of the main

principles for building online communities, highlighting the importance of shared

information. Thus, sharing information in a visual or spatial manner is able to provide

important insights.

21


2.4.2 SPATIAL DECISION SUPPORT SYSTEMS

Visualisation of input and output information is likely to improve stakeholders‘

involvement in decision-making and knowledge sharing, as well as simplifying the decision-

making process (Dur et al., 2009). Specifically, GIS can provide the computational,

analytical, problem solving, and visualisation capabilities of DSS. In effect, GIS is one of the

novel technologies in health planning, providing the universal link that allows integration of

the data needed for effective decisions (Rushton, 2000). Research indicated that GIS has the

potential to be used in a range of decision-making tasks, with the use of its analysis and

visualisation capacities (e.g. spatial aspect) providing an opportunity to use this tool as part of

a decision-making system. For example, through GIS, users can visualise the effects of

healthcare delivery strategies (Higgs & Gould, 2001). However, if GIS is to be integrated into

a decision-making mechanism, several improvements are required, particularly in the context

of the local government public health sector.

The use of GIS technology within the local government public health sector has

significantly increased during the past decade (Rushton, 2000). There is increasing demand

for the application of GIS technologies to enhance public health programmes (Cromley &

McLafferty, 2002), and the role of GIS in public health management and practice continues

to evolve. GIS is perceived to have a beneficial role within four settings: visualisation,

exploratory, spatial analysis, and model building (Higgs & Gould, 2001). Another use for

GIS is priority mapping, undertaken by stakeholders to provide a context for priorities (i.e.,

location, distribution, and relationship to other spatial factors). Furthermore, GIS can

contribute to target prevention efforts by enabling planners to predict changes in disease

distribution (Rich et al., 2005). For example, observation of geographical shifts in disease

distribution over time has been a powerful tool in comprehensive cancer control efforts, and

could be applied to other diseases as well (McElroy et al., 2006). The use of GIS increases

efficiency by providing a timely and rapid assessment of epidemiological patterns that could

be relevant to decision-making (Fonseca & Malheiros, 2005). Thus, the application of GIS

technology is an important step towards a better understanding of public health issues and

their inherent complexities (Waring et al., 2005), and to gain insights into the spatial

distribution of disease, social determinants of health and health outcomes (Higgs & Gould,

2001).

Some public health professionals continue to believe that better cluster detection

methods and geographically referenced data will lead to knowledge that will benefit the

22


public (Phillips et al., 2000). Further, spatial analysis can assist in the identification of

specific areas for planners to target and evaluate interventions that promote increased

awareness of risk factors (Trooskin et al., 2005). Similarly, Rushton (2000) argued that

spatial analysis methods are also becoming embedded in DSS.

As highlighted above, such tools also have the potential to help decision-makers in a

variety of ways. For example, Higgs and Gould (2001) suggested a range of DSS features and

points of information such as ‗what if‘ scenarios: identify any ‗under-serviced‘ areas;

evaluate the quality of services offered; accessibility to services and health care information;

locate the nearest health care facility; health surveillance; target resources; increase proximity

to recreational areas or community facilities; and produce maps. Thus, the availability of

public health information in a robust DSS environment is in a nascent state (Croner, 2003).

DSS can be seen to provide powerful insights into contemporary community issues in a

spatial, temporal, and visual form (Caldeweyher et al., 2006).

A case study discussed in the following paragraph will exemplify its potential. The case

study of the Multi-Agency Internet Geographic Information Service (MAIGIS) West

Midlands (Theseira, 2002) is an interesting example to demonstrate the influence of DSS on

decision-makers. The MAIGIS DSS was used as a population data source for the

measurement of health and the influences on health for use in quantitative aspects of health

impact assessments in the West Midlands, UK. MAIGIS also encouraged a more consistent

approach to decision-making by enabling all regional agencies to use the same datasets

within the decision-making process. Consequently, the application of mapping and spatial

analytic techniques in the DSS had implications on public policy and in the reallocation of

healthcare resources in the West Midlands community. Therefore, with knowledge of their

local areas, decision-makers were given suggestions on how they could alleviate existing and

practical health problems.

2.4.3 ONLINE DECISION SUPPORT SYSTEMS

There is growing evidence that online DSS environments have a positive impact on

decision-making (Kingston et al., 2001). The ultimate technical goal of online DSS is to

ensure that information is made available for end-users to perform analyses, and store and

represent their own results within the system (Yigitcanlar & Gudes, 2008). Contrary to static

presentations, information becomes dynamic when users are allowed to access or interact

with a database from their own computer (Croner, 2003). As Richards et al. (1999) stressed,

the application of GIS techniques in an online DSS allows decision-makers to ask questions

23


of maps and to quickly, clearly, and convincingly show the results of complex analyses.

Thus, as the relevant technology becomes more readily available and more industries realise

its potential, the numbers of online DSS are increasing rapidly (Su et al., 2000).

Online DSS incorporate features which can improve decision-making processes. As

more industries realise the potential of these systems, these technologies are being widely

used by various organisations worldwide. As Yigitcanlar and Gudes (2008) pointed out,

online DSS need to be interactive and to promote knowledge sharing and exchange. Thus, the

growing interest in online systems is encouraging a rapid expansion of research, especially in

the planning context. Furthermore, as online DSS create the potential for enhanced decision

support environments (Scotch & Parmanto, 2006), the implementation of such systems in

health planning provides new insights, and may improve decision-making processes within

the dimensions of use of evidence, participation, and consensus.

According to the literature, online DSS should be based on three main components:

usability, accurate data, and interactivity. As Theseira (2002) noted, flexibility and ease of

use of the interface are critical elements in the successful implementation of these systems. It

is essential that the data included is clear to both professional and non-professional users and,

in addition, users need to be able to have user interface to query the data and to print various

outputs for their own usage. The Australian Health Information Council (AHIC), highlighted

that public health applications of online DSS have already been adopted by several countries,

such as the United States and the United Kingdom (AHIC, 2008). However, while the use of

online DSS is an increasingly important activity in Australian organisations, it is not yet

sufficiently accessible to decision-makers, health planners, and communities within the health

planning practice in this country.

2.4.4 THE AUSTRALIAN CONTEXT

According to Queensland Health‘s (QH) 2005 report on public health and GIS, despite

the fact that modern technologies including software and fast personal computers had been

available for at least a decade, the incorporation of DSS technologies into health planning

practice in Australia was only at its inception. Since this study began in 2008, similar

practices in Australia have emerged. For example, Melbourne City Council (MCC) initiated a

programme titled Melbourne 2030 Planning for Sustainable Growth (MCC, 2008). Other

examples of Australian health services using online mapping applications or DSS could be

found at the following:

24


Victorian Department of Human Services;

South Australian Department of Human Services;

Western Australia Department of Health;

Commonwealth Department of Health and Aged Care; and

Central Northern Adelaide Health Service.

The state of South Australia has embraced DSS and spatial information to assist with

the preparation of a South Australia regional health service plan 2008 (National Health and

Hospitals Reform Commission, 2008). The National Centre for Social Application of

Geographical Information (GISCA), a university-based consultation team, assisted the South

Australia Department of Health in the development of this health services plan. Thus, there

are a few good examples of DSS being used for informed decision-making processes in

Australia.

Although there are several good examples of health DSS applications (facilitated by

Victoria, West Australia, and South Australia health government initiatives), the majority of

the systems are focused on specific health issues (e.g., National Diabetes Service Scheme and

the social health atlas of Central Northern Adelaide Health Service [CNAHS, 2008]) rather

than the promotion of health at the community level. These applications all lack the

communication channels and the collaborative planning mechanisms (as outlined earlier in

section 2.3.2) which are imperative for developing healthy cities and communities. Thus, an

online health DSS needs to embrace the collaborative health planning and the facilitation of

evidence-based decision-making elements. The combination of these qualities may achieve

vigorous impacts on Australia‘s health.

Generally, Australia has an excellent tradition and track record in health and medical

research (NHHRC, 2008). However, research on health DSS is often under-resourced; for

example, the research scheme commissioned in 2009 by the National Health and Medical

Research Council (NHMRC) constituted less than three precent of the NHMRC‘s total

research funding. Moreover, the National Health and Hospitals Reform Commission‘s health

reform report outlines that the biggest challenge is to transfer the DSS research findings into

health practice (NHHRC, 2008). Meeting these challenges requires that research and

evidence-based decision-making be recognised as essential prerequisites to improving health

outcomes. The extensive literature suggests that more research is needed to shed light on

what interventions work best from the health outcome perspective. Thus, a national approach

25


is required to drive action throughout the Australian health sector (NHHRC, 2008). One of

the ways to do so is to adopt evidence-based and collaborative-based approaches, using an

online system which can be accessed locally by health planners and decision-makers. This, in

turn, can expedite and improve the use of evidence, knowledge, and guidelines in the field of

health planning.


The advantages of utilising DSS have influenced the use of technology in the

development of healthy communities. The technology gap that limits access to ICT tools has

serious implications for DSS applications in public health. For instance, the number of DSS

applications in public health has been quite limited, possibly because they required trained

and skilled users. These factors produce differential access that exists inherently in many

geographic regions. A further challenge is the collaborative efforts between practitioners in

public health agencies, researchers, and community groups to address both the conceptual

and the technological issues relating to DSS implementation (Cromley & McLafferty, 2002).

Higgs and Gould (2001) support this by identifying a particular lack of collaboration between

GIS academics and health care practitioners, as well as between DSS developers and end-

users (Buckeridge et al., 2002). Thus, it has been recognised that an intensive time

commitment is required to develop mutual understanding, effective working relationships,

and a sense of collaboration (Buckeridge et al., 2002).

Furthermore, research indicated that accessibility to information and its quality are

considered to be amongst the most tangible challenges. Accessibility to information is clearly

important to enable individuals and communities to address health issues (WHO, 1999), but

once accessed, individuals must be able to make use of the data effectively (Buckeridge et al.,

2002). Therefore, a major challenge is to design an efficient and useful interface for the

system. Furthermore, heterogeneity in user skill and knowledge both demand consideration

when designing systems (Buckeridge et al., 2002). Another major challenge is associated

with maintaining privacy and confidentiality of health data (Kelly & Tuxen, 2003). Health

GIS researchers need to be aware of the types of data disclosure practices that threaten

medical record confidentiality (Higgs & Gould, 2001). In addition, DSS developers should be

aware of publishing within accepted standards of data security and privacy (Croner, 2003).

Difficulties encountered in accessing data indicate that privacy concerns present and create

serious obstacles to DSS development (Buckeridge et al., 2002). Besides that, the quality of

26


the information affects data integrity, and measures must be in place to ensure the most

accurate, complete, and standardised data is being used.

As Croner (2003) pointed out, it is clear that robust DSS interoperability will depend on

collaborative approaches. As Internet technology improves, some of these challenges will be

resolved. Thus, health planners must consider these challenges when they approach the

design phase. This, in turn, will allow further use of the DSS environments as an integral part

of health planning for improving the collaborative health planning practice. However,

increasing accessibility to effective health information through DSS may not be sufficient,

unless health planning is also being practiced in a collaborative manner.

2.5 POTENTIAL OUTCOMES OF DECISION SUPPORT SYSTEMS IN HEALTH

PLANNING

The role of DSS in health planning practice continues to evolve. Application of this

technology is an important step towards better understanding public health issues and their

inherent complexities (Waring et al., 2005). Analysing and mapping public health data is

becoming increasingly important in the attempt to improve the performance of major public

health actions and to promote community health (Cromley & McLafferty, 2002). The

literature identifies a number of prospective DSS outcomes. Amongst these outcomes, but

not limited are: increasing collaboration or participation, developing trust, increasing

satisfaction in decision-making, growing user satisfaction, constructing knowledge, and

encouraging use of evidence in decisions-making processes (Igbaria & Guimaraes, 1994).

DSS is perceived to have a role in a number of settings for health planning. For

example, identifying service health barriers and multicultural health needs, supporting

strategies to address gaps, facilitating multi-directional communication channels, and re-

affirming transparent communication and decision-making processes (Phillips et al., 2000).

To encourage collaboration and reduce health inequalities, DSS may be used as an outreach

vehicle for community-based public health empowerment. This, in turn, “may help our

understanding of the complex relationship between socioeconomic factors and health status”

(Phillips et al., 2000, p. 976).

Like any technology, DSS is a tool to achieve further goals (disease prevention,

supporting decision-making etc.). Thus, DSS may empower decision-making at all levels and

help address health planning tasks. For example, the ability to conduct spatial analyses

promotes the provision of effective health services. This, in turn, may lead decision-makers

27


and health planners to re-examine the nature of access of health services to the community

and, if is found to be lacking, provide equal access. Therefore, it is an important direction for

those in charge of making decisions regarding social services and healthcare allocation

(Kaukinen & Fulcher, 2005). In this regard, decision-making does not happen in isolation,

and is not formulated and implemented only by decision-makers in government offices. A

range of institutions such as NGOs, community organisations and city councils mediate and

intertwine between decision-makers and people‘s livelihoods. Thus, the DSS may be an

important interface where decision-makers can meet and essential information can reside.

2.6 SUMMARY

Although more than two decades have passed since the initiation of the healthy cities

movement, it has not yet achieved its full potential (Ashton, 2009). The reasons for this

include but are not limited to the lack of collaboration between planners and health

practitioners, and the absence of planning tools such as DSS. This study examines the role of

an online GIS-based DSS in supporting the health planning for healthy communities in Logan

Beaudesert, Queensland, Australia, through its impact on decision-making processes.

Specifically, the study focuses on developing a conceptual framework, to underpin

planning for healthy communities. There is also a need for new ways of planning, based on

collaborative practice and broad knowledge (e.g. evidence-based approaches). Collaborative

planning is supported by a sound theory (e.g., communicative planning theory), but lacks

practical tools. One tool that has been gaining prominence is the DSS.

This review of DSS has revealed the potential usage of DSS in the collaborative health-

planning context. This section focused on the real decision-makers (e.g., health planners,

decision-makers, stakeholders etc.) and their potential strengths, effects, and outcomes of

adopting DSS tools within the process of decision-making. It also covered discussions on the

practical values of the DSS, particularly as a tool for improving collaborative practice and

decision-making. Further, the literature emphasised that while DSS is an essential tool, it can

be more effective if it is sited in online environments. An online platform encourages the

participation of many stakeholders, and as a result the DSS becomes more apparent.

However, there is still a need to know how to develop and implement such a tool. Thus, the

tool needs to be tested to establish whether it influences decision-making processes and, if so,

to what extent. Based on the literature review, identifying this potential impact has been

defined as one of the study objectives, and is elaborated in the method chapter.

28


In summary, current approaches (e.g., healthy cities & communities) and methods have

not yet achieved their full potential and there is a need for a more robust framework. As

Ridley & Jones (2001, p.4) observed, “There has never been such a promising time as now to

promote better user and public involvement”. It has been acknowledged that by applying a

more evidence-based approach in a collaborative manner, health systems will improve. The

literature review for this study has covered the extensive literature on all the key aspects of

the study objectives, which forms the knowledge base necessary for this research. The

methodology adopted in this study and the range of tasks undertaken to achieve the primary

study aim and objectives are underpinned primarily by the findings from the literature review.

29

Chapter 3: Research Method


3.1 PREVIEW

This chapter describes the study approach and design, research site and process, data

collection methods, and data analysis techniques. It also provides an in-depth description of

the conceptual framework that was designed for this study. Using a mixed method design,

data were collected through both qualitative and quantitative methods. Specifically, data were

collected by adopting a participatory action research (PAR) approach that informed the

development and conceptualisation of the HDSS. A pre- and post-design (see Section 3.6)

was used to determine the impact of the HDSS on decision-making. The following sections

provide a detailed overview of the research process used to address the study objectives that

were outlined in Chapter 1.

3.2 OVERVIEW

The literature on the current practice of collaborative health planning, both locally and

internationally, was extensively reviewed via academic journals, books, government reports,

conference proceedings, newsletters, workshops, seminars, the Internet and other sources.

The review also encompassed the development of the conceptual framework that underpins

this study, as well as the instruments and procedures for collecting quantitative and

qualitative data. As a result, an appropriate case study was identified and a general study

approach designed. The study approach that was adopted is a mixed method of PAR

Intervention with the pre-and post decision-making impact study. The PAR Intervention is

defined as the combination of all three PAR cycles:

Raising awareness about the potential use of GIS and collaborative planning (PAR

Cycle 1 or introduction stage);

Participating in the design and development of the HDSS tool to support decision-

making (PAR Cycle 2 or interaction stage); and

Trialling and implementing for improving the tool (PAR Cycle 3 or trialling stage).

30


Specifically, the PAR Intervention was embedded within a pre- and post (i.e.,

snapshots) research design aimed at determining the impact of the PAR intervention on

decision-making processes. Two waves of data collection were used, one prior to the

beginning of the PAR intervention and one following its completion. To address the study

objectives and research questions, a case study was selected.

3.3 CASE STUDY The Logan Beaudesert Health Coalition, Queensland, Australia, is a partnership

established in 2006 to address the growing level of chronic disease in the Logan- Beaudesert

region of Queensland (See Figure 3.1). The initiative was intended to enhance existing

services and infrastructure, establish formal partnerships, improve existing resources, and

implement additional services and strategies. It also aimed to focus on the broad determinants

of health to reduce risk factors and the incidence of chronic disease in a specific locality

(Kendall et al., 2007). The LBHC was a response to the acknowledgment that the cost of

chronic disease to society remained significant and current management and planning

methods did not appear to be having sufficient impact. Consequently, collaborative health

planning was seen as an important method for progressing health decision-making and

addressing chronic issues in the region.

Therefore, the LBHC was implemented with the view of improving the region‘s health

capacity at multiple levels through enhanced and responsive localised planning. The LBHC

consists of representatives from the following organisations: Queensland Health, Logan City

Council, Scenic Rim Regional Council, Youth and Family Service Logan, Griffith

University, South East Primary HealthCare Network, and Regional Health. The LBHC has a

central Board (i.e., LBHC Board), which oversees six health programmes and advisory

groups, each addressing a specific area identified as needing attention. These working groups

focus on early childhood (0 to 8 years of age), multicultural health, the prevention and

management of existing chronic diseases, the integration between general practices and acute

settings, efficient health information management, and health promotion. Each programme

has a manager and a selected group of key stakeholders from multiple sectors and relevant

organisations. The six health programmes or advisory groups are responsible for facilitating

decisions relating to polices or strategies by providing recommendations and information to

the LBHC board. In addition, the decisions of the LBHC board are reflected back to the six

health programmes, Figure 3.2 illustrates the LBHC structure.

http://www.google.com.au/url?sa=t&source=web&cd=1&sqi=2&ved=0CBsQFjAA&url=http%3A%2F%2Fwww.sphn.org.au%2F&rct=j&q=SPHN&ei=cvqUToCtH-ueiAeqwMCHBg&usg=AFQjCNHxSYz56yypi3bz7faNavLQOQJB0Q&sig2=NpvTHCdcLzsVSX9Xp2YWNQ&cad=rja

31


By providing recommendations and information, the programmes assist the LBHC

Board to make decisions and develop policies and strategies. The role of the LBHC Board is

to coordinate and direct the coalition as a whole. LBHC members serve in accordance to their

specific role requirements or contract across the LBHC organisations, and there is no time

limit for their membership in the coalition. However, throughout the study, some LBHC

members left and new members came, because of a sense of utilization or other work

commitments etc. The Queensland State Government funds the LBHC and has given the

Board a mandate to modify, alter or adapt any of the current programs in response to

evidence and performance data with the scope to design and implement new health initiatives

as required.

Figure 3.1. Logan Beaudesert location map

32


Figure 3.2. LBHC structure (the board and its six advisory groups)

3.4 A FRAMEWORK FOR PLANNING A HEALTHY COMMUNITY

The overall aim of DSS is to improve the efficiency of stakeholders‘ decision-making,

optimise overall performance and minimise judgemental biases (Turban 1993). The

framework that has been developed for this study illustrates the overall place of DSS within a

healthy communities‘ planning initiative (See Figure 3.3). However, the literature emphasises

the importance of grounding a DSS in a broad information framework. Specifically, it is

suggested that the information management framework as described by Schulz and

Northridge (2004, see Section 2.2.2) should guide the development of a community health

profile, with information derived from multiple sources. The ability to present this

information in a meaningful, accessible and usable way is a critical challenge in establishing

healthy communities. In this regard, Duhl and Sanchez (1999) and the WHO (1997) define a

list of six fundamental characteristics (health public policy, innovation, community

participation, intersectoral action, policy decision-making and commitment to health) that are

needed to create a healthy community. If these characteristics are adopted, it is likely that a

healthy city and communities will emerge. Thus, this framework suggests that a DSS that

exists as part of a broader city health planning process should facilitate these qualities. As the

study was restricted by a three year time frame, it was decided to test this framework

partially. Thus, the study tested the impact made by the DSS on a group of decision-makers

and health planners in a selected case study (i.e., LBHC) within the local community level.

Multicultural

Health

Promotion

Information Management

GP

Integration

Optimal Health

Early Years

LBHC Board

33


Fundamental Factors City level

(Macro)

Intermediate Factors Community level

(Meso)

Proximate Factors Interpersonal level

(Micro)

Health and well-being Individual or

population levels

Natural environment

Macro social factors

Inequalities

IND

ICA

TO

R S

ETS

Social context

Built environment

Stressors

Health behaviours

Social integration

and support

Health outcomes

Well-being

Fac

tors

Hea

lth

pro

file

Health Decision Support System (Design and implementation of system for supporting decision-making processes)

Heal

th D

SS

Pro

cess

es

Po

licie

s Confident, effective policy and decision-making

City level policies (Macro)

Community level policies (Meso)

Interpersonal level policies (Micro)

Individual or population level policies

Health public

policy Innovation Community

participation

Intersectoral

action

Political

decision-making

Commitment to

health

High health status

Appropriate health

Basic needs

Quality of

environment

Innovative city

economy


resources

High degree of

participation

Supportive

community

Encouragement of

connectedness

High degree of

participation


resources

Encouragement of

connectedness

Encouragement of

connectedness High health status

Sustainable

ecosystem

Basic needs

Quality of

environment

Healthy community

Are

a o

f

resu

lts

Hea

lth

ou

tco

mes

Qu

alit

ies

Figure 3.3. A conceptual framework for planning a healthy community (derived from World Health Organisation 1997; Schulz & Northridge 2004)

34


3.5 PARTICIPATORY ACTION RESEARCH

PAR is increasingly being applied as the overarching name for an orientation toward

research practice that places the researcher in the position of co-learner, and puts a strong

emphasis on input from participants or end-users and the on-going translation of research

findings into action (Minkler, 2000). Recently, this approach has gained attention in health

research, particularly in the public health context (Minkler & Wallerstein, 2003). One of the

most important characteristics of PAR is the fact that participants whose lives are affected by

the research initiative take an active role in its design. In this regard, Israel et al. (2001)

define PAR as adhering to the following principles:

Participatory;

Engaging community members and researchers in a joint process in which both

contribute equally;

A co-learning process for researchers and community members;

A method for systems development and local community capacity building;

An empowering process through which participants can increase control over their

lives, nurturing community strengths and problem-solving abilities; and

A way to balance research and action.

Amongst its advantages in the context of healthy cities or healthy communities is its

ground-up approach driven by the end-users rather than a top-down approach led by experts.

This approach strengthens the degree and quality of input from participants by using

democratic participatory processes driven by community priorities and based on community

contributions. Krasny and Doyle (2002) suggest that while PAR is oriented toward social

change, it is also based on a broader approach to knowledge that recognises the existence of

multiple forms of knowledge and multiple perspectives. Thus, researchers involved in a PAR

initiative enter the community as co-learners rather than teachers (Minkler, 2000).

The literature reveals that through consultation meetings, many healthy communities

have effectively incorporated a high level of community participation (Minkler, 2000; Stern,

Gudes & Svoray, 2009). As Minkler (2000) emphasises, PAR offers a promising approach

for realising community participation and conceptualising the vision of the Healthy Cities

movement through a collaborative health planning process. The PAR process also offers an

important method to support the dissemination and analysis of information by decision-

35


makers, predominantly as part of a broader conceptual framework (i.e., a conceptual

framework for planning a healthy community). The PAR method can support both

quantitative and qualitative data regarding needs (e.g., of decision-makers) and the

prospective impacts on planning responses (Maeng & Budic, 2010). In addition, the literature

review emphasizes that one of the key requirements of a collaboration-based system is the

flexibility it provides to adapt to users‘ needs, thereby increasing the efficiency of planning

processes. Thus, the PAR approach was found to be suitable for developing the HDSS.

3.6 PARTICIPATORY ACTION RESEARCH INTERVENTION

The adopted PAR approach incorporates quantitative and qualitative techniques of data

collection. In order to collect all the user feedback and evaluate its impact on decision-makers

and health planners‘ perspectives, a specific PAR method was designed. This method was

developed and tested as part of the broader conceptual framework constructed for this study.

Specifically, the PAR Intervention consisted of three PAR Cycles: PAR 1 (introduction

stage) which aimed to achieve awareness about the use of GIS in decision-making, PAR 2

(interaction stage) which aimed to collect feedback from users to develop and design the

HDSS, and PAR 3 (trialling stage) which aimed to trial and implement the HDSS to refine

the tool. Figure 3.4 shows the PAR Intervention and the three PAR cycles executed.

36


PAR Cycle 2(i.e., Interaction

stage)

PAR Intervention

Post–PAR intervention

user satisfaction survey

Logbook

* Information items survey* Functionality and features * System workflows

PAR Cycle 1(i.e., Introduction

stage)Post-PAR

intervention decision-making

survey

Pre-PAR intervention

decision-making survey

Pre–PAR intervention

observational data of actual

decision-making

PAR Cycle 3(i.e., Trialling

stage)HDSS Phase 1

Pre–PAR Intervention Phase

August

2008 (ARC and PhD

projects

commenced)

March

2010(Intervention

commenced)

March

2011(HDSS prototype

deployed [HDSS

phase 1])

HD

SS

DE

VE

LO

PM

EN

T P

RO

CE

SS

HDSS Phase 2

March

2012

(HDSS Phase 2)

System design and development

Post–PAR Intervention Phase

Post–PAR intervention

observational data of actual decision-

making

Figure 3.4. Framework for developing the HDSS by using three PAR cycles

37


3.6.1 PAR CYCLE 1: INTRODUCTION STAGE

The Introduction stage is associated with the early days of the study when the concept

of GIS was first introduced to the LBHC board members, with several introductory

presentations included to raise their awareness. The PAR intervention phase commenced with

a series of GIS introductory presentations to the LBHC board and other advisory groups that

took place in March and April 2010. The primary purpose of this cycle was to raise

awareness of the GIS concept for decision-making. To raise LBHC board awareness, this

cycle included a number of general information sessions about the concept of GIS and its

positive impact and potential application for decision-makers in the LBHC.

3.6.2 PAR CYCLE 2: INTERACTION STAGE

The interaction stage is associated with the period of time between the introduction

stage and trialling stage, where LBHC board members were engaged (e.g., through

consultation meetings and workshops) in order to design and develop the HDSS in a

collaborative manner. The literature emphasises that health planners do not have at hand all

the frameworks needed to determine the type of information that must be considered (Gudes

et al., 2010). However, it also highlights that the development of these types of frameworks is

not a simple matter. As Flynn (1996) states, “Every community is unique, with different

physical, social, political and cultural contexts that must be understood in the planning

process”. For this reason, health planners must develop a thorough understanding of the

individual community‘s health profile and the structural features that influence its health.

Thus, the framework that is to be used to structure information should organise information

in a way that directs the attention of decision-makers to the entire range of conditions

influencing health (Gudes et al., 2010). In this regard, a potential framework that could

underpin DSS has been suggested by Schulz and Northridge (2004). The application of

Schulz and Northridge‘s framework, which initially directed the GIS data collection efforts,

provided a solid foundation and a more comprehensive understanding of the community

health profile for the HDSS. Thus, the framework was used to ensure a meaningful basis on

which to make decisions that contribute to the development of a healthy community.

In line with the recommendation of Maeng and Budic (2010), PAR 2 consisted of a

series of consultative meetings to obtain input from end-users about prospective GIS

information items, features and functionality, and health scenarios (i.e., workflows) to be

included in the HDSS. To determine and identify the inputs of LBHC board members for

38


development and design of the HDSS prototype, the following information was collected and

then analysed.

To assist in identifying the relevance and urgency of including particular types of

information in the HDSS prototype, an information-items survey was designed and conducted

among the HDSS end-users (i.e., LBHC board members). The information obtained in this

survey originated from Schulz and Northridge‘s (2004) framework. Accordingly, the

information items survey composed of a list of available data based on their framework (see

also Table 4.1). Subsequently, a descriptive analysis was conducted to identify the

essentialness, relevance, priority and urgency of including particular types of GIS

information items in the HDSS prototype. The data collected from the information-items

survey was categorised by three groups according to the level of essentialness using a three-

point Likert scale: 1 = essential now, 2 = could be included in phase two of the HDSS, and 3

= not necessary at all. Subsequently, the cumulated selection score was calculated for each

group of items (see Table 4.1), which was then used to determine the level of essentialness.

This ranking system made it possible to ascertain which GIS information groups of items to

include in the HDSS prototype, and to address the study objective. Consequently, information

items which were indicated as the most essential were ultimately included in the system.

In addition to the selected information items, a separate discussion regarding the

inclusion of features and functionality items in the HDSS was held. The LBHC members

were provided with features and functionality list that had been adopted by a similar,

previous project undertaken by the Western Australia Department of Health in 2010. Next, a

functionality demonstration was conducted, and LBHC board members were asked whether

to include or prioritise each individual feature. A discussion was held about each feature,

until a final list was constructed. Table 4.2 presents the final HDSS features list as

determined by the LBHC board members.

Further, based on findings from the information items survey, a list of workflows and

potential health scenarios were suggested. In general, the scenarios were intended to guide a

HDSS users through a structured workflow that could provide spatial output based on a group

of predefined information items. It was designed to identify functional capability within the

proposed HDSS prototype, based on real health scenarios. These system workflows were

used to demonstrate the core functionality that could be provided by the HDSS prototype.

Subsequently, the LBHC board members commented on the suggested workflows,

particularly on which GIS layers to include in each workflow. A thorough discussion was

39


then held by the LBHC board members to determine its level of essentialness for their day-to-

day planning and decision-making practice. After a fruitful discussion, two health scenarios

were carefully chosen to be part of the HDSS scope (see tables 4.3 and 4.4). Next, the revised

document was disseminated among the LBHC board members and received final

endorsement. In summary, throughout PAR Cycle 2 (Interaction Stage), the feedback and

information collected followed by the analysis undertaken provided an invaluable opportunity

to design and develop the HDSS in a collaborative manner.

3.6.3 PAR CYCLE 3: TRIALLING STAGE

The trialling stage was associated with the period of three to four months from when

the HDSS prototype was officially deployed (01/03/2011) and when LBHC board members

were using the system. The primary purpose of this cycle was to trial the system and collect

evidence about the extent of usage and degree of satisfaction it attained. To collect this

information, two instruments were used:

Google Analytics script to monitor the number of unique visits, views and the

average time on site; and

A User Satisfaction survey to explore and understand the experiences of the LBHC

board members in using the HDSS. This survey was an important tool for continual

refinement and improvement of the system.

User satisfaction survey

To understand the extent to which users were satisfied with the HDSS, particularly in

terms of usability, a survey was designed (User Satisfaction Survey). The survey was

circulated during the trialling stage. Therefore, users were given three to four months from

the HDSS deployment date to become familiar with the system.

The literature emphasises that the best predictor of effective decision-making is

satisfaction with one‘s decision-making (Bharati & Chaudhury, 2004). There is evidence in

the literature that decision-making satisfaction in the context of a decision support system is

likely to be associated with the perceived quality of the system, information and presentation.

Omar and Lascu (1993) identify a five-construct (23 items) scale for measuring satisfaction.

This scale provides a meaningful framework for testing usage and satisfaction and is linked to

a validated survey. For instance, Omar and Lascu (1993, p. 6) examined these in a reliability

test and all constructs were found to have a coefficient alpha score higher than 0.5. A high

coefficient indicates that the item performs well in capturing the constructs. Thus, the 23

40


items-based satisfaction scale was included within the User Satisfaction survey and analysis

procedure. The survey consists of the following constructs: information quality (9 items),

planning (6 items), staff and services (3 items), system support for decision-making (2 items),

and user involvement (3 items). Table 3.1 presents the survey items with their association to

the respective construct.

Table 3.1 User Satisfaction survey items (derived from Omar & Lascu, 1993, p.6; see also Appendix 8.2)

Construct Item

Information quality

(Items 1-9)

Availability and timeliness of information provided by the HDSS

Ability to access the system without support from the system administrator

Accuracy and completeness of the information provided by the system

Flexibility of the data and its applicability to a range of scenarios

User confidence in the system

Ease of access for users to the HDSS

Current and up-to-date information provided by the system

Efficiency of the system in setting up, update and maintenance

Relevance of the system outputs to LBHC

Planning

(Items 10-15)

System priorities that reflect the overall LBHC objectives

Defining and monitoring information systems policies for the HDSS

Level of LBHC involvement in defining and monitoring the system

Existence of a planning agenda to develop the system

Improvements to the system

System responsiveness to changing user needs

Staff and services

(Items 16-18)

Quality and competence of the system

Technical competence level of the system administrator

Communication between users and the system administrator

System supports for

decision-making

(Items 19-20)

Data analysis capabilities of the system to support the decision-making process

Availability of tools in the system to analyse issues related to the Logan Beaudesert

area

User involvement

(Items 21-23)

User‘s feeling of participation in the HDSS

User influence on the development of the system

Helpfulness of the system administrator

DATA COLLECTION

The survey of User Satisfaction has been developed for each of the constructs listed in

Table 3.1. The items associated with the first construct, information quality, explain the

characteristics of information in terms of currency, accuracy, relevance, flexibility, ease of

use and access. Items associated with the second construct, planning, explain the

characteristics of management and planning aspects. Items associated with the third

construct, Staff and services, provide information related to staff competence and services

supporting the system, whereas items associated with the fourth construct, systems support

for decision-making, explain the characteristics of information quality and its ability to

41


support the decision-making processes. Lastly, items associated with the fifth construct, user

involvement, pertain to attributes which generate an environment that encourages user

involvement and participation.

To measure response, Omar and Lascu (1993) suggest a seven point Likert scale. Thus,

the response scale ranged from very poor to excellent, with higher scores indicating better

performance in terms of a particular item. The questionnaire consisted of 23 validated items

and was further refined to be suitable for the current study. Subsequently, the questionnaire

(see Appendix 8.2) was circulated to the LBHC board members by email after three months

from HDSS deployment day. In this regard, Evans and Riha (1989, p. 199) noted that:

[HDSS] ―evaluation had to occur over a span of time; that requires methods capable of

addressing both formative (in process) and summative (concluding) concerns”. Accordingly,

and given the earlier activities of PAR cycle 1-2, it was reasonable to allocate three to four

months from the HDSS deployment day of 1 March 2011 to collect data. This timeframe also

provided sufficient time for LBHC board members to become familiar with the system and to

incorporate it into their daily planning routine.

DATA ANALYSIS

The user satisfaction survey was utilised to identify the perceived levels of HDSS

satisfaction experienced by LBHC board members. Given that only 17 LBHC board members

participated in this survey, the data was used descriptively to improve the HDSS in

accordance with the PAR method (i.e., as part of PAR Cycle 3). Derived from Omar and

Lascu‘s (1993) recommendations, 23 items were identified. These items were associated with

five constructs: information quality, planning, staff and services, systems supports for

decision-making, and user involvement. The items were then divided into two main groups:

importance and performance. As suggested by Omar and Lascu (1993), the 23 performance

items were multiplied by the importance items, yielding „weighted performance items‘. To

measure the statistical dependence between each of Omar and Lascu‘s five constructs and a

broad question that asked respondents to rate their overall level of satisfaction with the HDSS

(see item 24 in the user satisfaction survey, Appendix 8.2), Spearman's correlation test was

utilised. Therefore, the 23 items were cumulated to the five constructs, and then correlated

with the overall satisfaction construct. This revealed which construct attained highest level of

correlation with the overall satisfaction construct.

To attain a deeper understanding of the experiences of LBHC board members with the

HDSS, respondents were also asked to describe their overall satisfaction in open-ended

http://en.wikipedia.org/wiki/Correlation_and_dependence

42


questions. Their text was converted into a delimited format (CSV file) to make it transferable

to Leximancer software. Through this analysis, key themes and concepts associated with the

overall HDSS satisfaction were identified. This type of analysis revealed the themes or

concepts that were associated with high to low levels of satisfaction. In addition, when

appropriate, participants‘ statements were used to highlight some findings.

3.6.4 SUMMARY

The literature emphasises the importance of incorporating focus groups, discussions,

consultation meetings, and qualitative and quantitative data collection as part of a

participatory-based research study (Rowe & Frewer, 2000). The PAR intervention

implemented these instruments as part of developing and designing the HDSS. However,

implementing a PAR approach within a health planning initiative is a time-consuming task

and requires attention to issues of power, trust, research rigour and conflicting interests of

scientists and citizens. Therefore, a separate study PAR intervention study) was also designed

to provide important information and evidence about PAR Intervention, as well as key

elements identified in the design and development process of the HDSS throughout the PAR

intervention.

3.7 PARTICIPATORY ACTION RESEARCH INTERVENTION STUDY

To examine the PAR Intervention process throughout the three PAR cycles, a diary

(i.e., Logbook) was created. The Logbook recorded the numerous actions, including

meetings, consultations, workshops, emails, webinars, and other interactions during the PAR

Intervention period. Participants at these events included LBHC board members and other

stakeholders from the LBHC programmes who were involved in the design and development

process of the HDSS during the PAR intervention. Technically, the Logbook entries included

the date of the interaction, the parties involved, the exact statements made, and the

interpretation of each interaction.

The Logbook provided important evidence about the process and key elements

involved in the design and development of the HDSS. However, it was identified that the

data, which was presented in the Logbook as a series of events (see Appendix 9.4), should be

articulated as a structured or narrative story to effectively describe the development process

undertaken throughout the PAR Intervention. One of the methods employed for this purpose

was a narrative technique.

43


The literature provides some definitions for a narrative inquiry:

“The study of human experience and lives” (Clandinin, 2007, p.37);

Looked at this way narrative is the phenomenon studied in inquiry. Narrative

inquiry, the study of experience as story, then, is first and foremost a way of

thinking about experience. Narrative inquiry as a methodology entails a view of the

phenomenon. To use narrative inquiry methodology is to adopt a particular

narrative view of experience as phenomenon under study (Connelly & Clandinin,

2006, p. 477).

When describing a narrative, one of the most important aspects is the point of view

from which the story is told. The literature emphasises that there are two basic forms of

narrative - diegesis and mimesis. The former term denotes telling a story while the latter

means showing a series of events. Another important aspect of a narrative is the mode of time

(Bell, 2002).

3.7.1 DATA COLLECTION

The PAR Intervention study adopted a hybrid approach, embracing the personified

narrator‘s point of view through a simple series of events. Accordingly, referrals to the

Logbook were incorporated in the text to contextualise some of the statements, improve the

overall sequence and enhance its validity. Subsequently, the Logbook was converted into a

delimited format (CSV file). The purpose was to make it accessible from other content

analysis tools (in this case, Leximancer). The data collected addressed the following research

question: How is an HDSS (online GIS-based DSS) developed and implemented? The

Logbook provided important information about the process undertaken to develop and design

the HDSS (i.e., PAR intervention), including the different stages, elements, and

characteristics of each cycle. The next section provides further detail of the method

incorporated to analyse this valuable data.

3.7.2 DATA ANALYSIS

To analyse the data, it was initially classified by different stages of HDSS development

(i.e., introduction, interaction, and trialling). The data was analysed and some of the main

findings (key themes and concepts) associated with the respective stage of development or

PAR intervention identified. Data analysed helped to identify, for example, what concepts

were mostly discussed during each stage. These findings were articulated and incorporated

into a narrative detailed story (see Chapter 4), followed by notes and statements to

44


substantiate some of the identified key findings. Greenhalgh et al. (2005, p. 445) defined this

method as organisational case study. They described this as the, “Detailed description of „the

case‟ as a context for events, plus chronological account of particular events as they

unfolded during the study”. Thus, based on the recommendations of Greenhalgh et al., the

PAR intervention study was formatted (see Chapter 5). In brief, the adopted narrative

technique was not only an important tool for illustrating the thorough process undertaken to

develop the HDSS through the PAR Intervention cycles, but also an invaluable instrument to

understand its process and characteristics. To some extent, it could be seen as a ‗study-

within-a-study‘.

3.8 DECISION-MAKING IMPACT STUDY

The PAR intervention was embedded within a pre- and post-research design (i.e.,

snapshots) aimed at determining the impact of the intervention on decision-making processes.

Two waves of data collection were used, one prior to the beginning of the PAR intervention

and one following completion of the PAR intervention. To understand the potential role of

HDSS in improving decision-making, both quantitative and qualitative data collection

methods were designed. These methods were employed before and after the PAR

intervention (see Figure 3.1). This, in turn, over time helped in the exploration and

understanding of the decision-making strategies and experiences of the LBHC board

members. Quantitative data (i.e., Survey of decision-making, see Appendix 9.1) was

collected to assess the culture in which decisions were made across LBHC, and measure the

perceived use of evidence, consensus and participation in decision-making. In addition,

satisfaction with information for decision-making and perceived importance of decision-

making were measured. To triangulate data collection, observational data was also collected.

The LBHC board meetings were recorded and transcribed each month for the duration of the

study. The board meetings, minutes of meetings, and summary observational notes were used

to analyse the actual decision-making of the LBHC board.

3.8.1 DATA COLLECTION

Survey of decision-making

To measure the overall climate or culture in which decisions were made across the

LBHC, two independent samples provided ratings at two points in time (prior to the PAR

intervention and following the PAR intervention). A questionnaire is an effective method to

obtain a large sample size for quantitative data analysis. A 25-item survey was constructed

45


based on several decision-making scales (Dean & Sharfman, 1993; Parnell & Bell, 1994;

Flood et al., 2000; Bennett et al., 2010). The questionnaire measured three decision-making

constructs highlighted by Mattessich et al. (2001): use of evidence in decision-making (five

items), consensus (four items), and participation in decision-making (three items). In

addition, two outcome constructs were measured: perceived importance of decision-making

(three items), and satisfaction with information for decision-making (ten items). The items

associated with the use of evidence in decision-making (also called ‗rationality‘ by Dean &

Sharfman, 1993) focused on the use of analytic techniques to identify problems and make

decisions. Items associated with consensus explored whether decisions were received with

the agreement of all parties (Flood et al., 2000). Items pertaining to participation examined

whether decisions were made in a participatory manner. Notably, only three items from this

construct were extracted (Parnell and Bell, 1994, p. 524). Parnell and Bell (1994) suggest that

these items measure decision quality and productivity as a result of participation, whereas the

other four items focus on the impact of individual members. Therefore, it was justified that

these items be excluded from the construct. Next, items associated with the fourth construct

(i.e., importance) explain the characteristics of importance of decision-making. Lastly, items

associated with the fifth construct (i.e., satisfaction with information for decision-making)

complemented other constructs, as it examined whether the provided information was

satisfactory, and underpins the entire process of decision-making. Importantly, some items

were slightly reworded to ensure better fit with the current context (e.g., measures related to

the LBHC). Responses were given on a 7-point Likert scale which ranged from a level of

agreement of 1 = not at all to 7 = completely, with higher scores indicating high agreement

with the respective statement. Although the original scales were different, most response

scales used the 7-point scale, and were thus considered to be the most useful. Table 3.2

presents the survey‘s constructs and their associated items (see also Appendix 9.1).

46


Table 3.2 Questionnaire constructs connected to their associated items

Construct name

Questionnaire items

Evidence-based

decision-making

(derived from Dean &

Sharfman, 1993 )

The LBHC looks for information when making a decision

The LBHC analyses relevant information before making a decision

Analytic techniques are important for making decisions in the LBHC

The process of decision-making within the LBHC tends to be intuitive

The LBHC focuses on crucial information and ignores irrelevant material

Importance of

decisions (derived from Dean &

Sharfman, 1993 )

The LBHC decisions are important

The LBHC decisions have the desired impact

The consequences of delaying LBHC decisions are serious

Consensus in

decision-making

(derived from Flood et

al., 2000 )

LBHC decisions are not final until all relevant members agree

Everyone‘s input is incorporated into important LBHC decisions

It is worth more time to reach consensus on important decisions

When making decisions, the LBHC works hard to reach an agreement

Participation in

decision-making

(derived from Parnell

& Bell, 1994 )

Everyone has a chance to participate in decision-making in the LBHC

The LBHC uses a participatory approach to reach effective decisions

Group decisions in the LBHC are worth any extra time required

Satisfaction with

information for

decision-making (derived from Bennett

et al., 2010)

The information helps me to recognise that a decision needs to be made

The information prepares me to make better decisions

The information helps me to think about the pros and cons of each option

The information helps me to think about which pros and cons are most

important

The information helps me to know what matters most to the decision

The information helps me to organise my own thoughts about the decision

The information helps me to think about how involved I want to be in each

decision

The information helps me to identify questions I want to ask about the

decision

The information prepares me to talk to about the decision

The information prepares me for follow-up discussions about the topic

47


Observational data of actual decision-making

To evaluate the actual decision-making processes, audio-recordings of LBHC board

meetings, minutes and observer‘s notes were analysed. Two sets of meetings (i.e., four

meetings in each phase) were selected before (pre-PAR intervention phase) and after (post-

PAR intervention phase). Specifically, two meetings were selected in each year from the

onset of the study (2008 and 2009) to the post-PAR intervention phase (2010 and 2011). A

preliminary analysis of the data collected showed that early and late year meetings tended to

be less productive in terms of decision-making thoroughness in discussions. Therefore, to

achieve maximum exposure to decisions, meetings were selected at the end of quarter 1

(April-June) and at the end of quarter 3 (October-November) in each year. The number and

nature of the decisions made were extracted from each meeting. An observation tool was

designed to evaluate the way decisions were made in the LBHC board meetings. The scale

compared the following constructs for consistency with the decision-making survey discussed

earlier:

Use of evidence (What was the degree to which information / evidence and

knowledge was used to underpin / influence decisions?)

Participation in decision-making (Who participated in the decision-making

process, and what did each party bring to the process?)

Degree of consensus (What was the outcome of the decision-making process? Was

there consensus or dissent? How was disagreement handled?)

The following sections provide a detailed description of the way in which the data were

analysed.

3.8.2 DATA ANALYSIS

Survey of decision-making

The decision-making survey comprising the 25 items was disseminated to the members

of LBHC (approximately 50 participants) in both written and online formats, so that

participants could select their preferred method of completion. Also, to substantiate the

survey findings and to obtain additional information, participants were given the opportunity

to comment in their own words on the way decisions were made (see Appendix 9.1, Section

C). The data collected in these surveys was analysed and used to understand the role of the

HDSS in improving decision-making.

48


The quantitative data was analysed using the Statistical Package for Social Science

(SPSS). The quantitative analysis techniques employed in this study included descriptive

statistics and t-test. Notably, the literature indicates that a t-test is designated to compare

averages in a research array of pre and post examination (Sarid & Sarid, 2006). Also, analysis

of variance (ANOVA) and post hoc tests were undertaken to obtain additional information on

the findings.

Descriptive statistics were used to summarise the responses, and make an assessment of

the LBHC members‘ overall perceivedness with decision-making processes prior to PAR

Intervention (pre-PAR intervention phase) and after (post-PAR intervention phase). To

analyse the comments given in the survey (see Appendix 9.1, Section C), the content analysis

tool Leximancer was employed. Consequently, the data was analysed and some of the main

findings (key themes and concepts) were identified. Leximancer was then used to obtain a

greater level of information and evidence about decision-making processes in the LBHC.

This, in turn, underpinned some of the quantitative findings identified in the decision-making

survey.

To identify the impact on decision-making processes after the PAR intervention (or the

change over time), an independent groups design was used. Given that LBHC changed

considerably during the course of the PAR Intervention and the staffing of the LBHC

changed as new initiatives were begun or completed, a repeated measures design was not

plausible. Practically, any use of repeated measures analysis based on individually-identified

data tracked over time would have resulted in missing data samples and sizes that were too

small to generate sufficient power. In addition, the use of independent groups‘ analysis would

violate the assumptions of independence of observations because a core sub-sample would

not be independent. Most importantly, the PAR intervention was not designed to promote

intra-individual changes in decision-making satisfaction as would be measured by repeated

measures analysis. Instead, the PAR intervention was designed to develop a culture of

decision-making based on evidence, consensus and participation. Indeed, it is highly

likely that satisfaction with the decision-making processes and perceptions about decision-

making in the LBHC would be correlated with the duration of engagement in the LBHC.

Thus, any repeated measurements of decision-making satisfaction may simply represent

maturation within the individual or change that would have been expected over time without

any PAR Intervention.

49


For these reasons, and to ensure that the assumptions of an independent groups design

could be upheld, participants selected for Time 1 (pre-PAR intervention phase) did not

participate at Time 2 (post-PAR intervention phase). Those who participated at post-PAR

intervention Phase were only those who had joined the LBHC since the PAR Intervention

began. By targeting survey participants in this way, the independence of the two observations

was assured and any spurious effect of time on decision-making satisfaction was eliminated.

To analyse the results, the t-test for independent means was used. Thus, the analysis

technique provided important evidence to evaluate the climate or culture in which decisions

were made throughout the study.

Observational data of actual decision-making

To evaluate the way actual decision were made in the LBHC board, a response rate was

determined by the researcher‘s observation and included the following scores: limited use

(e.g., limited use of evidence in the actual decision), moderate use, and high use. Table 3.3

presents how decisions were scored in the decision-making scale. To obtain an in-depth

understanding of the actual decision-making processes, data collected through the LBHC

board meetings, content analysis and observation summaries were used for the analysis

purpose. Thematic (i.e., content) analysis is defined by Gibson (2006, p.1) as, ―An approach

to dealing with data that involves the creation and application of „codes‟ to data. The „data‟

being analysed might take any number of forms – an interview transcript, field notes, policy

documents, photographs, video footage”. Thematic analysis demonstrated how evaluation of

the raw data of the LBHC board meetings (e.g., audio records, transcripts, minutes of

meetings and observer notes) progressed and led to the identification of overarching themes

that captured the phenomenon of performance feedback as observed in this study (Fereday &

Muir-Cochrane, 2008). Practically, the raw data were divided into data obtained before and

after the DSS intervention (i.e., pre-PAR intervention and post-PAR intervention). To process

the content analysis, the software tool Leximancer was utilised. Accordingly, content analysis

was carried out on the data collected from each selected meeting. Themes of the actual

identified decision-making processes were then formulated. In addition, important notes were

used to substantiate some of the findings presented. Each board meeting was then analysed

separately, and the findings summarised in Table 3.3 which presents the decision-making

constructs and descriptors. Thus, the data analysis procedures provided an important tool to

test whether actual decisions had changed over time throughout the study.

50


Table 3.3 Constructs of actual decision-making

3.9 RELIABILITY, VALIDITY AND ETHICS

The literature emphasises the importance of the reliability, validity and quality of

research. In this regard, Cavana et al. (2001) suggested that eight hallmarks be considered:

(1) purposiveness – scientific research has to have an aim or purpose; (2) rigour – ensures a

good theoretical base and a sound methodological design; (3) testability – logically developed

research objectives need to be tested; (4) replicability – the research, if repeated in other

circumstances, should provide similar findings, thereby increasing the credibility of the

findings; (5) accuracy – refers to how close the findings are to reality, based on a sample; (6)

objectivity – conclusions drawn from the results of data analysis should be based on the

factual rather than the subjective or emotional; (7) generalisability – the applicability of the

research findings in one organisational setting to another; (8) parsimony – a simplicity in

explaining the phenomena or problems that occur. Yin (2003) also noted the importance of

Use of evidence Level of participation Level of consensus

Limited level Evidence limited to

personal assumptions or

opinions and hearsay

Only a few members (i.e.,

less than 50%) played an

active role in the

discussion by speaking or

presenting a viewpoint

Only a few members

(i.e., less than 50%)

verbally agreed with

the decision, or no

opportunities were

available to disagree

(i.e., no requests to

indicate agreement, or

active disagreement

was suppressed)

Moderate level Discussion of knowledge

drawn from sources other

than personal opinion or

reference to internally

prepared evidence (i.e.,

local reports)

At least 50% of members

played an active role in

the discussion or

provided input that

influenced the decision-

making process

Most members agreed

with the decision or all

members agreed, but

limited opportunities

were available to

disagree

High level External evidence from

multiple sources was

reviewed and

incorporated in the

discussion

Input of some kind was

evident from all

members, or a

comprehensive and

enthusiastic discussion

was held that engaged

more than 50% of the

members

All members agreed

unreservedly with the

decision

51


validity in that it should not only ensure research is of high quality, but should also be

officially accepted and approved. Cavana et al. (2001) argued, however, that it is not possible

to meet all of these hallmarks completely.

Generally, the goal of research case study is to minimise errors and bias (Yin, 2003).

Driven by Yin (2003) and Cavana et al. (2001) and to ensure research quality and credibility,

the following actions were undertaken in the study. First, the decision-making draft survey

was tested through a pilot sample to ascertain its full validity before it was administered to a

larger sample group large sample group as suggested by (Gorard, 2003). Accordingly, a

group of respondents for the pilot survey were selected from colleagues at the Griffith

University‘s School of Health and postgraduate students at the Queensland University of

Technology‗s School of Urban Development. Participants were asked to provide comments

and suggestions with regard to the questionnaire structure. Feedback included the flow of

questions, appropriateness of wording, and time taken to answer the questions (as described

by Gorard, 2003). The survey was modified accordingly, and the research moved into the

next phase where the questionnaires were distributed to the LBHC members. These

procedures were also applied in the User Satisfaction Survey.

As for the qualitative data, Morse et al. (2002) suggested a number of strategies to

ensure trustworthiness in qualitative research: (1) promote investigator responsiveness; (2)

ensure methodological coherence; (3) ensure an appropriate sample; (4) pursue data

saturation or representativeness; (5) seek negative cases; (6) collect and analyse data

concurrently; (7) think theoretically and confirm ideas in new data; (8) engage in theory

development (i.e., move from the particular (micro) to the general (macro) and test on

original data. The study incorporated some of these strategies to ascertain the qualitative

analysis outcomes. For instance, the qualitative data was obtained and analysed after each

board meeting. This, in turn enabled authentication and validation of the findings.

Ethical clearance was required, and the application was reviewed by the Human

Research Ethics Committee (HREC), Queensland University of Technology. The study met

the requirements of the National Statement on Ethical Conduct in Human Research (i.e.,

approval number: 0900001060) on 16/11/2009.

3.10 SUMMARY

This chapter provided a detailed account of the research design and methods applied to

this study. The methods used to address the research questions and achieve the study

52


objectives were described. The overall research design, process and timeline were introduced,

followed by an explanation of the PAR cycles. The chapter described the quantitative and

qualitative data collection and analysis methods in detail, with descriptions of these

techniques highlighting the theoretical and methodological links.

53

Chapter 4: Participatory Action Research Intervention

Chapter 4: Participatory Action Research

Intervention

4.1 PREVIEW

Chapter 4 describes the system design and implementation process which was

undertaken to develop the HDSS. It presents the data collection methods, processes of

development, data analysis techniques, and the collaborative approach implemented (i.e.,

PAR utilising three PAR cycles) to develop such a system.

4.2 BACKGROUND

The HDSS2 was implemented in three PAR cycles, namely: PAR Cycle 1 (introduction

stage), PAR Cycle 2 (interaction stage), and PAR Cycle 3 (trialling stage). The period of time

prior to the introduction stage was termed the pre-PAR intervention phase, whereas the

period subsequent to this was termed the post-PAR intervention phase. The introduction stage

covered the period when the concept of GIS was first introduced to the LBHC board

members and during this stage several introductory presentations were conducted. The

interaction stage was associated with the period of time when the LBHC board members were

engaged (i.e., through consultation meetings, workshops etc.) in designing the system in a

collaborative manner. The trialling stage was aligned with the period when the HDSS tool

was officially deployed and LBHC board members were using the system. Figure 4.1 depicts

the HDSS stages of development (PAR Cycles) and the respective timelines.

2 HDSS denotes the name of the system prototype, whereas DSS is a term which represents the decision support systems

concept.

54


PAR Cycle 2(i.e., Interaction

stage)

PAR Intervention

PAR Cycle 1(i.e., Introduction

stage)

PAR Cycle 3(i.e., Trialling

stage)HDSS Phase 1

Pre–PAR Intervention Phase

August

2008 (ARC and PhD

projects

commenced)

March

2010(Intervention

commenced)

March

2011(HDSS prototype

deployed [HDSS

phase 1])

HD

SS

DE

VE

LO

PM

EN

T P

RO

CE

SS

HDSS Phase 2

March

2012

(HDSS Phase 2)

System design and development

Post–PAR Intervention Phase

Figure 4.1. HDSS process of development (PAR cycles)

55


4.3 INTRODUCTION STAGE

The PAR intervention commenced with a series of GIS introductory presentations to

the LBHC board members and other advisory groups that took place in March and April

2010. The primary purpose of this cycle was to raise awareness of the GIS and DSS as tools

to support decision-making. To raise the awareness of the LBHC board members, this cycle

included a number of demonstrations of GIS, as well as discussion about its impact and

potential application to local decision-making in health planning.

4.4 INTERACTION STAGE

During the interaction stage, the LBHC board members collaboratively defined the key

components for designing the HDSS: Information items, features and functionality, and

system workflows. The following provides more information about the instruments used to

design and develop the system. Table 4.1 presents the main findings from the Information

Items survey. The findings indicate that the most essential information items included

socioeconomic, demographic, public transportation, shops, roads, recreation, community

facilities, education facilities, health facilities and disease data. Two data items (health

behaviours and hospital admissions) were indicated as being essential, but due to difficulties

accessing these datasets, this data was not used in the HDSS prototype.

56


Table 4.1 Information items survey results

Please rate your level of requirement for each of the

following information items. For example, tick the cell

that best represents how important you think each

type of information is for inclusion in the HDSS

prototype. Please add any comments you think may be

relevant to our decisions about information

This group of

information

items is

essential now

N (%)

This group

of

information

items could

be included

in phase 2 of

the HDSS

N (%)

This group of

information

items is not

necessary at all

N (%)

Demographic (Population, Projected population (2007-

2027), Mortality rate, Indigenous, Multicultural (Clustered

Nationalities), Nationalities and Population density)

10 (100%)

___ ___

Socio Economic (SEIFA Index, Employment and

Unemployment rate, Income average and financial

resources, Internet access, Education, Businesses by

Industry Division, and Public Housing

9 (90%) 1 (10%)

___

Sustainable Built and Natural Environments

(Environmental hazards, Biodiversity and Contaminated

land)

2 (20%) 8 (80%)

___

Terrain (Aerial images, Topography and Contour) 1 (10%) 7 (70%) 2 (20%)

Public transportation (Bus stations, Bus routes, Railway

Stations and Railway routes)

10 (100%) ___ ___

Recreation (Parks, City swimming pools, Sporting

facilities and Cycling paths)

10 (100%) ___ ___

Emergency (Police, Fire station and Ambulance station) 4 (40%) 6 (60%) ___

Shops (Shopping centres, Fast food outlets) 8 (80%) 2 (20%) ___

Roads (Major roads and Streets) 9 (90%) 1 (10%) ___

Health facilities (Pharmacies, Aged care, Breast Screen,

Child Health, Medical Services, Mental health, Oral

health, Public hospitals, Private hospitals, GP‘s and

Medicare)

10 (100%)

___

Education Facilities (Child community Services, Higher

education, Libraries, Schools, Special education, State Pre

School, Youth clubs, Play groups and Universities /

TAFE)

9 (90%) 1 (10%)

___

Community facilities (Non-profit organisations,

Community centres, Community facilities, Community

Welfare, Employment services, Religious institutions,

Services clubs, Social clubs Sporting clubs, Youth clubs,

Schools, State, Non-state schools and Centre link offices)

9 (90%) 1 (10%)

___

*** Health Behaviours (Obesity [BMI]) 10 (100%) ___ ___

*** Hospital admissions (summary by year of the total

number of separations by SLA for the following admitted

diseases: Depression, Cardiovascular, Diabetes

and Asthma)

10 (100%)

___ ___

Health data (Avoidable mortality, Chronic disease,

Composite indicators chronic diseases, Health Risk

Factors, Premature mortality by selected cause, Private

health insurance and Self assessed health)

8 (80%)

___

2 (20%)

*** Indicated as being essential, but due to difficulties accessing these datasets, this data was not used in the

HDSS prototype.

57


Table 4.2 presents the final list of selected features and functions which were included in the

HDSS prototype, along with a description of the purpose of each.

Table 4.2 Features and functionalities selected by LBHC board members for the HDSS prototype

Feature / Function Purpose

User Login Screen for user to log into system

Map Navigation Basic Map Navigation, including zooming and panning

Base Map/ Imagery View Ability to select aerial imagery or street maps as a base

view

Layers Ability to view health and demographic layers of the

LBHC

Layer list Ability to turn layers on or off

Identify attributes Ability to view details of attributes found at a certain

location

Online Help Accessibility to text on help notes for using the system

Print Map Ability to print a map

Map Legend Ability to view an image indicating symbology used in

the map

Layer Metadata Ability to view metadata (i.e., data on data) for each of

the layers used in the system

Spatial Bookmarks Ability to store the extent of a view for quick zoom in

Simple Search Ability to undertake a simple geographical search of a

name field on two spatial layers: SLAs (Statistical

Local Areas) and community health centres

Redlining and Measurements Ability to draw points, lines, polygons and text on the

map

User Feedback Ability for users to submit feedback regarding data set

issues, updates or any other requirements of the system.

Accessibility analysis Ability to compute the service area of two layers

(public hospitals and GPs) based on driving or

pedestrian travel time

Proximity function Ability to find features in specified layers (public

hospitals and GPs) within a specified buffer distance of

a point entered by the user

Based on the information items selected and the defined features and functionality, the

LBHC board members were consulted to articulate the details of the two workflows of the

HDSS prototype (i.e., proximity and accessibility to health facilities). Two of the designated

workflows are illustrated in Tables 4.3 and 4.4.

58


Table 4.3 Proposed workflow for accessibility function

Workflow

Name

Accessibility Function

Description The literature emphasises that accessibility to health facilities has been

identified as a key determinant of health.

Objective To test the effect of travel time to health facilities

End Users LBHC members, Logan and Scenic Rim planners

Outcome To Identify gaps in the provision of health facilities in the community

Workflow 1. User logs into HDSS Prototype.

2. A map view is presented showing SLA boundary suburb

names.

3. The user zooms in to a specific area.

4. The user selects a button on the interface to calculate

service area catchments for a facility layer.

5. A form appears in which the user has the option to:

6. Pick a facility layer which may be one of three types:

Public Hospitals (default)

GP Clinics

Chronic Disease Centres

7. Pick a transport mode:

Pedestrian

Private Car (default)

8. Enter in travel time, (5,10, or 20 minutes)

9. Click on a button to show the service area. The system

processes the request and updates the map to show travel

time from the selected facility in the map view as

polygons.

10. The user can visualise gaps between polygons which

highlight areas not serviced.

11. The user sends the map to the printer.

Optional

Workflow

The user turns on a layer of population statistics to compare demographic

data to the accessibility to facilities.

GIS layers Street map/aerial imagery

SLA

Suburbs

Public hospitals

GP Clinics

Chronic diseases centres

Population statistics (optional)

It is well established in the literature that accessibility to health facilities is a key

determinant of health outcomes (Ensor & Cooper, 2004). Therefore, an ability to analyse the

effect of travel time to health facilities on health is vital for health planners. This scenario

enables end-users to identify gaps in the provision of health facilities within the community

and offers an evidence-based approach for sensible planning of health facilities. The analysis

could be extended by cross referencing additional data layers, such as population, projected

growth, disadvantage indicators or chronic disease prevalence. For example, in order to

perform this analysis, the user chooses to calculate service area catchments for a facility

59


layer. The user enters relevant parameters. A facility layer may be one of three types: Public

Hospitals, GP Clinics, Community Health Centres, a mode of transportation (either

pedestrian or private car) and a desired travel time (the default setting is 5, 10 or 20 minutes).

Next, the user clicks to submit the request to the server for processing. The result is sent to

the browser‘s map, showing travel time from the selected facilities as polygons. The user can

visualise gaps between polygons which highlight areas not serviced. The user can also select

additional data layers to examine accessibility to facilities in the context of other factors.

Figure 4.2 illustrates the Service Area accessibility function. Section 4.6 discusses the HDSS

extensively and provides more detail.

Figure 4.2. Service area accessibility function

60


Table 4.4 Proposed workflow for proximity analysis function

Workflow Name Proximity Function

Description A workflow to determine whether there are particular types of health

facilities within a specified distance of a user entered location

Objective To identify facilities within a buffer distance

End Users LBHC members

Outcome Highlight facilities in the map view within the specified buffer distance

Workflow 1. User logs into HDSS Prototype.

2. A map view is presented showing SLA boundaries and suburb

names.

3. The user zooms to a specific area.

4. The user selects a button on the interface to undertake

proximity analysis.

5. A form appears in which the user can pick one of three layers

to search:

Hospitals

GP Clinics


6. Enter a buffer distance (radial distance).

7. Pick a point on the map to buffer from.

8. Click on a button to find facilities.

9. The system processes the request and returns a transparent

shaded circular buffer around the input location and highlights

any features found in the buffer.

10. The system pops up a message listing the total number of

facilities found and listing them by name.

GIS layers Street base map

SLA

Suburbs

Public Hospitals

GP Clinics


The ability to identify the proximity of particular types of health facilities to a user

specified location may be useful to examine the spatial correlation between entities that

influence health. In this workflow, the HDSS helps to determine whether there are particular

types of facilities within a specified distance of a user entered location. This application could

be used, for example, to determine the number of relevant medical centres within 20 km of a

specific residential address. The outcome of this scenario is that health facilities are

highlighted in the map within the user specified buffer distance around a chosen location.

Figure 4.3 demonstrates the proximity function.

61


Figure 4.3. Proximity analysis function

4.5 TRIALLING STAGE

During the trialling stage two instruments were used to understand the extent of usage

and degree of satisfaction the HDSS attained. The first instrument was Google Analytics

script which monitored the systems logs. Findings indicate that throughout the three months

of trialling the system, it was visited more than 100 times by 33 unique users (excluding the

admin. group). On average, users spent four minutes using the system. Also, evidence

indicates that some users were using the systems from different computers (e.g., office, home

etc.). Given that only 17 LBHC board members had access to the system and the time of

implementation was short (three months), the extent of usage was considered to be good.

4.5.1 USER SATISFACTION SURVEY FINDINGS

To establish the degree of satisfaction, a user satisfaction survey was utilised to

understand users‘ experiences with the system. Twelve LBHC board members completed the

survey. Given that there were 17 HDSS users at the time, this response rate was considered to

be good (i.e., 70%). As suggested by Omar and Lascu (1993), 23 items were rated according

to importance and performance. These items compiled with the five constructs presented in

Table 3.1, namely, information quality, contribution to planning, services for users, support

for decision-making, and user involvement. In terms of the importance of these attributes, the

findings indicate (see Table 4.5) that all constructs were considered important. Support for

decision-making and services for users rated the highest score (i.e., 6.4), whereas user

62


involvement yielded the lowest score (i.e., 5.6). In terms of performance (see Table 4.6),

services for users rated the highest with a score of 6.1, while supports for decision-making

and planning rated the lowest (i.e., 5.0 and 4.9 respectively).

Table 4.5 Means, standard deviations and frequencies of responses to the five constructs of user satisfaction

survey (Importance)

Construct Mean SD N

Information quality (Items 1-9)

6.2 0.4 12

Planning

(Items 10-15)

5.8 0.7 12

Staff and services (Items 16-18)

6.4 0.6 12

System supports for decision-

making

(Items 19-20)

6.4 0.6 12

User involvement

(Items 21-23)

5.6 0.6 12

Table 4.6 Means, standard deviations and frequencies of responses to the five constructs of user satisfaction

survey (Performance)

Construct Mean SD N

Information quality (Items 1-9)

5.1 0.7 12

Planning

(Items 10-15)

4.9 0.9 12

Staff and services (Items 16-18)

6.1 0.4 12

System supports for decision-making

(Items 19-20)

5.0 0.9 12

User involvement

(Items 21-23)

5.7 0.6 12

The performance items were examined individually to gain a better understanding of

what factors impacted on the total scores (see Table 4.7). Findings indicate that technical

competence and helpfulness of the system administrator item rated the highest score (i.e.,

6.67), whereas the accuracy/completeness and currency of the information, and flexibility of

the data applicability to a range of scenarios yielded the lowest score (i.e., 4.67 and 4.64

respectively).

63


Table 4.7 Means, standard deviations and frequencies of responses to the 23 items of the user satisfaction survey

Construct Item N Mean Std.

Deviation

Information quality

(Items 1-9)

Availability and timeliness of information

provided by the HDSS 11 5.64 1.12

Ability to access the system without

support from the system administrator 12 6.00 .85

Accuracy and completeness of the

information provided by the system 12 4.67 1.23

Flexibility of the data and its applicability

to a range of scenarios 11 4.64 1.02

User confidence in the system 12 5.17 1.19

Ease of access for users to the HDSS 12 5.33 .88

Current and up-to-date information

provided by the system 12 4.67 1.07

Efficiency of the system in setting up,

update and maintenance 11 5.00 1.09

Relevance of the system outputs to LBHC 12 5.50 1.16

Planning

(Items 10-15)

System priorities that reflect the overall

LBHC objectives 11 5.36 1.28

Defining and monitoring information

systems policies for the HDSS 11 4.00 .89

Level of LBHC involvement in defining

and monitoring the system 11 5.09 1.37

Existence of a planning agenda to develop

the system 11 4.73 1.55

Improvements to the system 11 5.36 .92

System responsiveness to changing user

needs 12 4.75 .96

Staff and services

(Items 16-18)

Quality and competence of the system 12 5.75 .96

Technical competence level of the system

administrator 12 6.67 .49

Communication between users and the

system administrator 12 6.17 .71

System supports for

decision-making

(Items 19-20)

Data analysis capabilities of the system to

support the decision-making process 12 5.17 .83

Availability of tools in the system to

analyse issues related to the Logan

Beaudesert area

12 4.83 1.11

User involvement

(Items 21-23) User‘s feeling of participation in the HDSS

12 5.33 1.07

User influence on the development of the

system 12 5.17 1.11

Helpfulness of the system administrator 12 6.67 .49

Based on Omar and Lascu‘s (1993, p.8) recommendations, the five performance

constructs were multiplied by the importance constructs to yield ‗weighted performance

constructs‘ (Table 3.2). When performance scores were weighted according to importance

ratings, they represented participant views more comprehensively, and therefore attained a

higher level of correlation with overall satisfaction with the system. For example, a high

64


score suggests that a valued area (e.g., importance construct that was rated high) was also

perceived to be highly performed by HDSS users. Conversely, a low score means that

performance and importance did not equate well, for instance, a construct that was rated

‗highly important‘ but was perceived to be only moderately performed (e.g., planning).

The weighted performance constructs were then correlated to the overall satisfaction

with the HDSS in its current form (see item 24 in the user satisfaction survey, Appendix 9.2).

The Spearman's correlation test shows that information quality and support for decision-

making constructs were significantly correlated with overall satisfaction (R2=0.62 and 0.59;

p<.05 respectively). The planning construct tended towards a significant correlation with

satisfaction (R2=.37), but none of the other constructs were significantly correlated with

satisfaction (see Table 4.8).

Table 4.8 Correlation coefficients between weighted constructs and overall satisfaction item

Weighted Performance

Construct

Weighted

Mean

Weighted

SD

N Coefficient

Information quality 32.3 5.7 12 0.62 *

Planning 29.2 8.0 12 0.36

Staff and services 39.7 6.2 12 0.37

System supports for

decision-making

32.3 6.8 12 0.59 *

User involvement 32.3 3.3 12 0.28

p<.05, p<.01 * Significant

Respondents were also asked to describe their overall satisfaction in open-ended

questions. This data provided important insights into the areas that were not yet meeting

performance requirements. For instance, the majority of comments confirmed that the system

was not yet fully developed or had not been in use for long enough to evaluate. However, the

participants acknowledged the potential of the system:

“As the system contains more relevant and current information, it will become

more useable and appropriate”;

“It is premature to respond - system hasn't been implemented long enough”;

“I am impressed with the constant improvements within a limited capacity”;

“Given its stage of development, the HDSS is a useful and innovative tool”; and

“The HDSS will be an invaluable tool for anyone connected to the LBHC. It is

clear to see that improvements are constantly taking place”.

65


Some participants noted that they found the system to be user friendly, but indicated

that they could not access some information or had not used some features. For instance, one

participant commented that, “I have used the mapping facility in a submission and found it

user friendly”, while another commented on the absence of features that could be included in

future versions of the HDSS: “I would like to see more overt decision support tools included

in the HDSS, for example, that ask users to step through a series of questions to consider

ways they can answer using the HDSS data and tools”.

Although some participants were highly satisfied with the system, further

improvements were required to make the tool more applicable. Analysis of the comments

made by participants demonstrated a focus on further development of the system and data

expansion. The majority of comments made by participants also revealed difficulty in using

the system extensively due to work priorities or other commitments, meaning that they had

insufficient contact with the system to judge its utility effectively. As a result, some

participants felt ‗not ready‘ or that the HDSS had not yet addressed their current needs, even

though they could see how it would do so in the future.

Summary

The quantitative and qualitative findings of the User Satisfaction survey confirm that

overall there was high level of satisfaction with the HDSS (Mean=5.8, SD=1.0, N=12) among

its users. Findings indicate that items associated with system supports for decision-making

and the Information quality constructs were highly important to participants. However, these

constructs were only rated by HDSS users as performing at a moderate level. The correlation

findings indicate that System supports for decision-making and Information quality were

positively associated with overall satisfaction of HDSS users. Although the Planning

construct was considered important by participants, it was found to be moderately correlated

to the Overall satisfaction construct, presumably due to attaining lower level of performance.

Qualitative data suggests that lower levels of overall satisfaction may be due to the lack

of current information as the system was still under development process, or the short time

involved since the system had been implemented. Conversely, participants indicated the

presence of constant improvement in the system, which they appreciated. However, they

wanted improved functionality which would enhance the system‘s applicability to their local

decisions and planning attempts. In summary, the findings show that there was moderate to

high satisfaction with the HDSS. However, the findings also show that significant

66


development and data expansion was required to improve its utility as a tool for decision-

making, and consequently to attain higher levels of satisfaction among users.

4.6 SYSTEM DESIGN AND ARCHITECTURE

4.6.1 SYSTEM DESIGN

The HDSS required a flexible web-based GIS interface. Amongst the expected

system requirements were:

An interface that invites exploration and visualisation of the extensive GIS dataset;

A flexible user-centred web interface for the HDSS compiled through this GIS

dataset;

Capability to modify features and dataset based on ongoing feedback from HDSS

users.

One of the primary challenges was to design a simple, engaging, and usable interface

that helped users to make informed decisions. The final interface linked users to an extensive

dataset relating to the Logan Beaudesert district. Information in the HDSS was based on

Schulz & Northridge‘s (2004) framework (see Figure 2.2), and the information item survey

findings (see Table 4.1). The final HDSS can be viewed at the following link:

http://gis03.rcs.griffith.edu.au/HDSS/HDSSViewer/index.html. Figure 4.4 shows a snapshot

from the HDSS, while the following section provides architectural details of the system.

Figure 4.4. HDSS snapshot


67


4.6.2 SYSTEM ARCHITECTURE

To assess the analytical capabilities of HDSS, such as geographical-based queries,

attribute-based queries and map-based outputs, it was imperative to design the system

architecture in an adequate manner. Figure 4.5 presents the overall architecture of the HDSS.

Specifically, the HDSS is comprised of three tiers, namely: HDSS user interface tier, HDSS

application tier, and HDSS database tier. HDSS database tier represents the system

geodatabase which is based on the ESRI SDE product on top of the SQL Server. This tier

enables the saving, maintaining and updating of the different GIS layers and system users.

The HDSS application tier is the core engine of the system, and is based on the ESRI ArcGIS

Server product hosted on the Griffith University server. This tier enables managing of the

different geographical queries requested by HDSS users (e.g., to turn on and turn off a GIS

layer). The HDSS user interface tier represents the interface between users and the system.

This component is based on a web browser which verifies HDSS user access. HDSS users

were earlier provided with a user name and password that is requested upon start-up of the

system. Once approved, the client computer (i.e., HDSS user) can access the system.

HDSS User

Interface Tier

HDSS Database

Tier

(SDE on top of SQL

server)

HDSS Application

Tier

Secure Map Services

ArcGIS Server

GeoDatabase

HDSS

Users (client

computer)

Figure 4.5. System architecture

4.7 SUMMARY

Chapter 4 provided technical details about the PAR intervention approach adopted for

designing and implementing the HDSS. The PAR approach consisted of three cycles that

were executed: PAR Cycle 1 (introduction stage), PAR Cycle 2 (interaction stage), and PAR

Secure map services

ArcGIS

Viewer

HDSS

users

68


Cycle 3 (trialling stage). In PAR Cycle 1 the primary purpose was to raise awareness of the

GIS concept for decision-making, and that was implemented by a series of GIS introductory

presentations with the LBHC board members. In PAR Cycle 2 the technical requirements of

the HDSS were designed in a collaborative manner. In PAR Cycle 3, the system was

deployed and trialled for three months. Findings indicate that although the system was

designed in a collaborative manner and in accordance with the LBHC board needs,

substantial development and expansion was still required (based on the user satisfaction

survey conducted during PAR Cycle 3). Furthermore, findings suggest that more analytical

tools were required to improve the use of evidence in decision-making and make the HDSS

more applicable tool. In addition, the chapter provided information about the HDSS interface

and its technical architecture. Thus, Chapter 4 showed the way in which PAR Intervention

was implemented in order to collaboratively develop, design and trial the HDSS by LBHC

board members.

69

Chapter 5: Participatory Action Research Intervention Study

Chapter 5: Participatory Action Research

Intervention Study

5.1 PREVIEW

Chapter 5 examines the PAR intervention through the three cycles executed to develop,

design and trial the HDSS. The chapter presents the personified narrator‘s point of view

throughout a series of events recorded in a Logbook. In addition, referrals to the Logbook

were used to support the findings, and to better understand the characteristics of each PAR

cycle.

5.2 BACKGROUND

To attain better understanding of the PAR intervention through the three PAR cycles

implemented, Logbook records were used and analysed. The Logbook recorded the numerous

actions, including meetings, consultations, workshops, emails, webinars, and other

interactions that occurred during the PAR intervention. Specifically, the Logbook items and

statements recorded were used to understand the characteristics and notion of each PAR

cycle. This, in turn, provided important evidence about the process and key elements

involved in the design, development and trialling of the HDSS. Importantly, the PAR

Intervention study also helped to identify the knowledge that was created throughout this

process. Therefore, the PAR Intervention study played an important role in attempting to

understand, observe and explore the implications of the PAR Intervention in designing the

HDSS framework.

5.3 PAR CYCLE 1

Although the GIS concept was introduced informally on several occasions throughout

2008-2009, it was formally presented to the LBHC board members at a meeting in April

2010, after baseline data has been collected. During this meeting, details and a variety of

maps were presented to explain and clarify the potential role of GIS in health planning.

LBHC board members were encouraged to think about their required data needs. In one of

these presentations, a participant stated: “we need to know what information should be

included in the system” (Appendix 9.4, item 12). As a result of the initial interaction, some

LBHC board members requested additional data. During the presentations, one participant

70


noted: “Yes I agree this is an important marker in the development of evidence used in the

LBHC‖ (Appendix 9.4, item 12). These reactions implied an evolving awareness of the use of

evidence in the LBHC board‘s decision-making processes.

5.4 PAR CYCLE 2

Subsequent to the formal GIS introductory meeting in April 2010, an approval was

sought to commence the HDSS development process. The LBHC board members endorsed

and authorised the project to proceed. According to LBHC board members, “The board

recognises the HDSS potential and approved to continue with the project” (Appendix 9.4,

item 12). In line with the board decision, an HDSS steering committee was established which

consisted of 12 LBHC board members, Griffith University researchers and other LBHC

stakeholders. This smaller but valuable group represented all segments in the LBHC, and

maintained an active role in decision-making about the HDSS (e.g., what functionality and

features should be included in the HDSS prototype). Several consultations were held with

relevant experts at the national level where a simple DSS was being used, such as the

Department of Health in Western Australia (DOHWA), Spatial Vision Ltd (a GIS developer),

the Cooperative Research Centre for Spatial Information (CRC-SI) which hosted experts in

GIS, Griffith Enterprise, and ESRI Australia. The primary purpose of these meetings was to

understand the system specification needs, obtain feedback, and learn from other experiences.

In 2010, DOHWA developed a similar tool named HealthTracks, which is being utilised by a

small group of health planners and decision-makers within the department. This consultation

proved fruitful, and important feedback was obtained (Appendix 9.4, items 11-19). To

demonstrate this valuable input, one of the participants noted the following: “I bet if you put

a prototype together and then asked the same question, you would get lots of ideas. It is a bit

like asking about service delivery - people never really know what to say, but they can always

critique what they have at the moment. It may be a matter of trying to extract the principles -

i.e., think of a software program you currently use in your work, what are the features that

annoy you most, make life easiest for you etc.‖ (Appendix 9.4, item 18). On the basis of this

feedback, it was decided to scope the system with an external partner, Spatial Vision Ltd,

which had developed HealthTracks in West Australia. It was also decided to maintain

dialogue with DOHWA (Appendix 9.4, item 19).

During this period, requests for data or maps began occurring from LBHC members.

For example, on one occasion a LBHC member sent the following request: ―I was wondering

71


if it were possible to send me any data about where patients with type 2 diabetes are living in

the Logan Beaudesert Area? I‟m putting together a business case at present and it would

help me pinpoint areas where there are high concentrations of diabetics, as I‟m proposing

that we extend our level of dietician clinics in the area” (Appendix 9.4, item 25).

Throughout August 2010 (Appendix 9.4, items 24-54), an extensive number of

meetings, discussions, consultations, webinars, teleconference calls and emails were

conducted. As a result of these meetings, an earlier decision made by the LBHC board

members (i.e., to scope the HDSS) was changed, and it was decided to collaborate on system

development with DOHWA and CRC-SI (Appendix 9.4, item 23). The bases of this decision

were that access to HealthTracks would speed the project and enable the HDSS to benefit

from previous developments. However, a few concerns and disagreements about this decision

occurred among members of the HDSS steering committee. For example, one participant

noted: “It will be very naïve for us to think that if we have access to HealthTracks that will

meet our requirements. I have been developing GIS systems for almost 10 years and there is

no way we can avoid the specific design phase” (Appendix 9.4, item 36). Thus, given the

disagreements within the group, it was decided to further investigate the advantages and

disadvantages of potential collaboration with the technical persons of DOHWA and CRC-SI.

Discussions were conducted with a range of GIS experts, and it was decided to request

Spatial Vision Ltd to conduct a specifications report. As one of the participant concluded:

“there is a risk that if we came at it using the HealthTracks system we may not end up with

the system that meets our needs both in the architecture or the interface” (Appendix 9.4, item

51). Thus, it was agreed that once the HDSS specifications report was completed, the HDSS

steering committee would re-assess collaboration with DOHWA and CRC-SI.

In early November 2010, Spatial Vision Ltd sent a specifications draft report which was

circulated amongst the HDSS steering committee members. Their feedback was incorporated

into the final report which was again distributed for their perusal and final endorsement. Once

endorsement was granted, an additional consultation meeting was held on 22/11/2010, when

it was decided to continue the development phase with Spatial Vision Ltd. Potential

collaboration with DOHWA and CRC-SI was not reconsidered at this time.

Development officially commenced in December 2010, and by 07/02/2010, a beta site

was released. A request for feedback was circulated to the HDSS steering committee. Initial

feedback from this steering panel was obtained and incorporated into a Corrections Report

(Appendix 9.3). The corrections were addressed and the HDSS was installed on 22/02/2011.

72


On 01/03/2011, after completing the testing and technical checks, the HDSS was officially

deployed. This deployment concluded the Interaction Stage and all feedback provided beyond

this time became associated with the Trialling Stage.

5.5 PAR CYCLE 3

The trailing stage officially commenced after the system was deployed on 01/03/2011.

An additional technical meeting was held (See Appendix 9.4, item 118) to define the way in

which the system would be supported, and how LBHC users would gain access. In the first

few weeks, LBHC board members were provided with intense training, after which they were

asked to comment on their experience. Their feedback was incorporated into the system.

Some of the feedback (i.e., mostly additional data requests) could not be instantly addressed,

but would be fully incorporated as the system evolved. Once this stage was completed, the

HDSS usage was expanded to all LBHC board members. The board members were provided

with one-on-one HDSS training sessions, telephone support for their questions or needs,

remote assistance, and online support via YouTube channel (http://tinyurl.com/3fafy3v).

During the first few weeks after the HDSS became fully operational, a large amount of

feedback was provided by LBHC board members in regard to layer names, errors or issues in

datasets, and additional data requests. These comments were documented in a Feedback

Report. It was suggested that the HDSS be updated once a month after collecting feedback

from LBHC board members. Their feedback was collected through group emails and a

dedicated HDSS virtual group (similar to a Facebook or other social networking group).

5.6 CONTENT ANALYSIS-BASED FINDINGS

The content analysis tool Leximancer was used to substantiate findings derived from

the Logbook. The data (minutes, correspondences and observer‘s notes) from each PAR cycle

was analysed. Then, some of the main findings (key themes and concepts) associated with the

respective stage of HDSS development (PAR cycle) were identified. The following sections

present findings driven by the use of the Leximancer content analysis3 tool. Figure 5.1

presents key themes which were associated with PAR Cycle 1 (introduction stage). In this

regard, themes that were located closer to the introduction stage were more closely related.

The following themes were identified: access, data, layers, and development. Thus, it can be

summarised that most themes pertained to data, information needs and access. In fact, this

3 In terms of the themes and concepts maps, the closer the themes or the concepts are to the stage‘s label, the better it

interweaves. Furthermore, the brightness of a concept‘s label reflects its frequency in the text. That is, the brighter the

concept label, the more often the concept is coded in the text.

http://tinyurl.com/3fafy3v

73


was mostly discussed in the Introduction Stage at the LBHC meeting held in April 2010

when the GIS concept was formally introduced.

Figure 5.1. Introduction stage themes and concepts map (based on minutes from the LBHC board meeting, April

2010)

As for the interaction stage, the main findings (key themes and concepts) in the

Logbook which were associated with PAR Cycle 2 (interaction stage) were identified.

Figures 5.2 presents key themes which were associated (i.e., closer in the map to the DSS

interaction stage label) with the interaction stage. The following themes were identified:

project, time, take, further, work, LBHC, layers, data, and use. Thus, a likely conclusion was

that most of the themes were associated with the designing elements of the HDSS, its

potential partners, its role, architecture and system, prospective usage, and continual work.

This has required extensive interaction and feedback from all stakeholders and partners

involved in this process.

74


Figure 5.2. Interaction stage themes and concepts map (derived from Logbook items associated with the

interaction stage)

Finally, the main findings (key themes and concepts) associated with PAR cycle 3

(trialling stage) are outlined. Analysis was based on items from the Logbook and minutes of

meetings associated with the trialling stage. Figures 5.3 presents key themes and concepts

(i.e., closer in the map to the HDSS trialling stage label) which were associated with the

trialling stage. The following themes and concepts were identified: meeting, time, use, GIS,

development, and process. Findings indicate that although the system was deployed, further

development was still required, and additional feedback needed to be collected from the

LBHC board members. This required extensive support and cooperation from the HDSS end-

users (LBHC board members), and further discussion about the future and scope of this

system (i.e., HDSS phase 2) was essential. To address this need, HDSS end-users were given

the opportunity to provide constant feedback about the system through a report of corrections

which was sent monthly (e.g., see Appendix 9.3). Ultimately, it improved the system and

maintained the PAR intervention approach introduced in Chapter 3.

75


Figure 5.3. Trailing stage themes and concepts map (derived from Logbook items associated with the

trialling stage)

5.7 SUMMARY

This chapter presented the PAR intervention as a narrative story. The PAR cycles were

described in detail with evidence drawn from the Logbook, with all Logbook entries analysed

using Leximancer to understand the characteristics of each PAR cycle. Findings indicate that

themes and concepts associated with access, information data and layers were correlated to

PAR Cycle 1 (Introduction stage), whereas themes and concepts associated with further

work, action, LBHC board, scoping and needs, were related to PAR Cycle 2 (interaction

stage). Furthermore, themes and concepts associated with development, process, next phase

and projects were linked to PAR Cycle 3 (trialling stage). These findings highlight the

theoretical and methodological links which were discussed extensively in previous chapters,

in addition to addressing the following research question: How is an HDSS (online GIS-

based DSS) developed and implemented? Thus, the chapter provided imperative evidence to

thoroughly understand the PAR intervention characteristics.

77

Chapter 6: Decision-Making Impact Study


6.1 PREVIEW

Chapter 6 presents quantitative and qualitative findings about the way in which

decisions were made by the LBHC, before and after the PAR intervention. The chapter shows

the overall impact of the HDSS on the broader group of LBHC (i.e., the climate or culture in

which decisions were made), and the specific impact of actual decisions made by the group of

decision-makers (i.e., LBHC board members).

6.2 BACKGROUND

The purpose of the chapter is to examine the impact of the design, development and

implementation of the HDSS on decision-making processes. As described extensively in

Chapter 3, two methodological instruments were used (i.e., decision-making surveys and

observational data of actual decision-making). The decision-making surveys provided

understanding of the climate or culture in which decisions were made in the whole LBHC,

while the observational data of actual decision-making was used to understand the way

decisions were made by the LBHC board members throughout the study.

The decision-making surveys were conducted in two iterations. The first round was

undertaken during March 2010 in the pre-PAR intervention phase, and the second round

during July 2011 in the Post-PAR intervention phase. To provide context for the quantitative

findings in the survey, participants were also asked to comment in their own words on their

decision-making processes and experiences.

As for the observational data of actual decision-making, the required data was obtained

by collecting audio-recordings of LBHC board meetings, minutes and observer‘s notes.

Subsequently, a decision-making scale was designed to evaluate the way decisions were

made in the LBHC board meetings (see Table 3.3). The scale embraced the following

constructs: use of evidence in decision-making, degree of participation in decision-making,

and degree of consensus in decision-making. These constructs were observable while

analysing the data from the LBHC board meetings. Both quantitative (findings of surveys)

and qualitative data (observations of actual decision-making) were collected prior to and after

the implementation of the HDSS to explore the decision-making strategies and experiences of

78


the LBHC members. This allowed an evaluation of the implementation process and

intervention impact on decision-making processes. In summary, this chapter sheds light on

the way decision-making processes changed over the period of this study, and presents the

overall impact observed.

6.3 DECISION-MAKING SURVEY FINDINGS

The primary focus of these surveys was to identify the culture in which decisions were

made across the whole LBHC. The subsequent sections provide more detail about the

findings of the decision-making surveys.

6.3.1 PRE-PAR INTERVENTION PHASE: SURVEY FINDINGS

At the pre-PAR intervention phase, 40 LBHC participants completed the questionnaire.

Given that there were approximately 50 LBHC members at the time, this response rate was

considered to be good (i.e., 80%). Satisfaction with information for decision-making was

rated lowest of the five constructs (see Table 6.1). Conversely, perceived participation in

decision-making was rated highest. ANOVA and post-hoc tests were then conducted to

compare different groups within the LBHC. Subsequently, participants were divided into

groups representing the different initiatives that were auspiced by the LBHC. Three groups

were constructed representing the different focus of each initiative: governance (LBHC board

members and administrators), health promoting (early years, health promotion and

multicultural initiatives), and service integration (GP integration, information management,

and optimal health). A one way ANOVA test showed the difference between the groups. For

instance, consensus and participation were rated the highest by the governance group.

Interestingly, importance of decision-making was rated the highest by the health promoting

group. Table 6.2 provides more details.

79


Table 6.1 Means, standard deviations and frequencies of responses to the five dimensions of decision-making

Table 6.2 ANOVA results by LBHC initiatives pre-PAR intervention phase

Decision-making

construct

Governance

group

Health promoting

group

Service

integration group

Sig

Mean (SD)

N=9

Mean (SD)

N=14

Mean (SD)

N=14

Use of evidence in

decision-making

4.2 (0.9)

4.1 (1.1)

4.4 (0.8)

0.780

Importance of

decision-making

4.3(1.2)

5.6 (1.6)

4.4 (1.4)

0.075**

Consensus in

decision-making

4.8 (1.2)

4.3 (1.6)

4.4 (1.4)

0.750

Participation in

decision-making

5.2 (0.9) 4.6 (1.7)

4.6 (1.3)

0.550

Satisfaction with

information for

decision-making

3.5 (1.4)

3.3 (1.4)

3.6 (1.9)

0.840

p<.05, p<.01 * Significant **non-significant (trended towards significant)

Participants were then grouped into two major age groups (i.e., less than 40 years, and

over 40 years). A one way ANOVA revealed a significant difference in the means for the

following constructs: use of evidence, consensus, participation, satisfaction with information,

and importance. Use of evidence in decision-making showed a trend towards significance.

Specifically, the younger age group reported lower scores on all five constructs (see Table

6.3).

Decision-

making

construct

Mean SD Not at

all

A Little Some Moderately Often Mostly Completely

Use of

evidence in

decision-

making

4.38 1.18 3 (1.8%)

18 (11.0%)

27 (16.6%)

40 (24.5%)

32 (19.6%)

35 (21.5%)

8 (4.9%)

Importance of

decision-

making

4.89 1.58 0

(0%)

9

(9.2%)

23

(23.5%)

17

(17.3%)

10

(10.2%)

24

(24.5%)

15

(15.3%)

Consensus in

decision-

making

4.29 1.57 4

(3.2%)

14

(11.1%)

17

(13.5%)

27

(21.4%)

18

(14.3%)

29

(23.0%)

17

(13.5%)

Participation

in decision-

making

4.41 1.81 4 (3.8%)

9 (8.7%)

14 (13.5%)

14 (13.5%)

20 (19.2%)

21 (20.2%)

22 (21.2%)

Satisfaction

with

information

for decision-

making

3.49 1.53 50

(14.9%)

55

(16.4%)

68

(20.3%)

55

(16.4%)

56

(16.7%)

50

(14.9%)

1

(0.3%)

80


Furthermore, participants were then grouped into three major tenure groups. The tenure

groups were constructed to represent duration of members in the LBHC - those who were

new to the LBHC (less than 12 months), intermediate members (12 to 24 months), and

veterans (more than 24 months). One way ANOVA showed no significant difference in the

means for these groups. However, new members and veterans tended to report higher scores

than the intermediate age group (see Table 6.4).

Table 6.3 Comparison of five constructs of decision-making processes with LBHC two major age groups pre-

PAR intervention phase

Decision-

making

construct

0-40 young 40+ veterans Sig

Mean (SD)

N=11

Mean (SD)

N=14

Use of evidence

in decision-

making

3.9 (1.1) 4.7 (0.8) 0.066 *

Importance of

decision-making

4.0 (1.4) 5.5 (1.3) 0.016 *

Consensus in

decision-making

3.6 (1.4) 5.0 (1.2) 0.020 *

Participation in

decision-making

3.7 (1.1) 5.4 (1.4) 0.005 *

Satisfaction

with

information for

decision-making

2.3 (1.5) 4.5 (1.3) 0.002 *

p<.05, p<.01 * Significant, **non-significant (trended towards significant)

Table 6.4 Comparison of five constructs of decision-making processes with LBHC tenure groups pre-PAR

intervention phase

Decision-

making

construct

New to the

LBHC

Intermediate

members

Veterans Sig

Mean (SD)

N=12

Mean (SD)

N=13

Mean (SD)

N=4

Use of

evidence in

decision-

making

4.4 (0.9) 4.0 (1.09) 5 (0.5) 0.190

Importance of

decision-

making

5.0 (2.0) 4.8 (1.4) 6 (0.4) 0.460

Consensus in

decision-

making

4.7 (1.6) 3.9 (1.4) 5.6 (0.6) 0.120

Participation in

decision-

making

4.7 (1.7) 4.2 (1.2) 5.7 (1.1) 0.240

Satisfaction

with

information for

decision-

making

3.7 (1.7) 3.0 (1.4) 4.9 (2.0) 0.140

81


The qualitative data provided by the survey‘s participants revealed further detail. For

example, one participant noted that: “Very few decisions have ever been made by the LBHC

board - most decisions are made by a few outside the meeting, and therefore there is no

rigour or transparency to the processes”. Another participant commented on the relative

absence of decision-making: “I'm not sure if any actual planning for the future is made”. The

lack of control over decisions made by the LBHC was a recurrent theme observed in the

participants‘ comments. Figure 6.1 presents key themes which were identified, with findings

from the content analysis (via Leximancer) indicating that participants felt unable to make or

were unclear about making informed decisions without better input (e.g., information and/or

evidence) and change in the processes of decision-making within the coalition.

The majority of comments made by LBHC participants revealed the difficulty

associated with making decisions in the absence of adequate information. One participant

stated that, “we need to identify priority actions, need to be more pro-evidence in our

decision-making”. Another participant noted: ”There is a serious lack of information and

communication [to guide decision-making]”. Thus, the value of using evidence in decision-

making was clear: “If the LBHC goes down the pathway of prioritising strategic directions

based on evidence, inclusive decision-making processes (including community input), this

will have great potential to more appropriately address issues”. Therefore, despite moderate

scores on consensus and participation constructs, some LBHC participants noted that

problems existed in relation to the sense of disconnectedness of the LBHC as a whole and

that this may have a significant impact on decision-making processes.

82


Figure 6.1. Themes and concepts map (derived from the pre-PAR intervention phase decision-making survey)

In summary, the pre-PAR intervention phase quantitative and qualitative findings

confirm that overall there were low levels of satisfaction with the decision-making processes

across the LBHC. However, some groups within the LBHC were more satisfied than others

(i.e., those who were over 40 years). There was also a tendency for LBHC board members

(new members and veterans) to be more satisfied with information and perceive higher levels

of consensus, participation and use of evidence in decision-making. The qualitative data in

the survey suggested that the lack of satisfaction with information for decision-making may

be due to the complete lack of evidence on which to base decisions. This lack of evidence

seemed to contribute to a sense of disconnectedness between the different elements of the

LBHC. For example, some groups in the LBHC perceived that the decision-making processes

were not being practised consensually and in a participatory manner. The findings indicated

that within some groups (i.e., LBHC board), there were high levels of consensus and

participation, but this may not occur across the whole LBHC. Therefore, the findings showed

that there is some diversity in the way members of a LBHC view decision-making. Thus,

there was an overall sense that decisions were ineffective, presumably because they were not

based on information or evidence.

83


6.3.2 POST-PAR INTERVENTION PHASE: SURVEY FINDINGS

At the post-PAR intervention phase, 42 LBHC participants completed the

questionnaire; however, seven of them were excluded as they participated in the pre-PAR

intervention phase survey. In general, findings showed moderate scores, while Importance of

decision-making was rated lowest of the five constructs (see Table 6.5). Conversely,

satisfaction of information for decision-making was rated highest. ANOVA and post-hoc

tests were then conducted to compare different groups within the LBHC. Participants were

first divided into clusters representing the different initiatives that were auspiced by the

LBHC. Three groups were constructed representing the different focus of each initiative, that

is, Governance group (LBHC board members and administrators), Health promoting (early

years, health promotion and multicultural initiatives) and Service integration (GP integration,

information management, and optimal health) (see also Figure 3.2). A one way ANOVA test

showed the difference between the groups. Use of evidence and participation were rated the

highest while importance of decision-making was rated the lowest. Interestingly, decision-

making constructs associated with the governance group were rated the highest, while

constructs associated with the health promoting group were rated the lowest. However, the

one way ANOVA findings revealed that the difference in means across the groups in the use

of evidence and consensus was significant or trended towards significant. Table 6.6 provides

more details.

Table 6.5 Means, standard deviations and frequencies of responses to the five dimensions of decision-making

processes post-PAR intervention phase

Decision-

making

construct

Mean SD Not at

all

A Little Some Moderately Often Mostly Completely

Use of evidence

in decision-

making

4.51 1.52 9

(5.45%)

7

(4.24%)

24

(14.54%)

28

(16.96%)

19

(11.51)

50

(30.3%)

28

(16.9)

Importance of

decision-making

4.37 1.61 3

(3.22%)

2

(2.15%)

15

(16.1%)

16

(17.2%)

19

(20.4%)

18

(19.35%)

20

(21.5%)

Consensus in

decision-making

4.51 1.75 4

(3.2%)

3

(2.4%)

17

(13.6%)

21

(16.8%)

17

(13.6%)

39

(31.2%)

24

(19.2%)

Participation in

decision-making

4.59 1.95 2

(2.1%)

4

(4.3%)

12

(12.9%)

12

(12.9%)

14

(15.05%)

25

(26.8%)

24

(25.8%)

Satisfaction of

Information for

decision-making

4.48 1.86 6

(1.9%)

11

(3.5%)

35

(11.3%)

47

(15.2%)

76

(24.5%)

97

(31.3%)

37

(11.9%)

Table 6.6 ANOVA results by LBHC initiatives post-PAR intervention phase

84


Decision-making

construct

Governanc

e group

Health

promoting

group

Service integration

group

Sig

Mean (SD)

N=10

Mean (SD)

N=15

Mean (SD)

N=10

Use of evidence in

decision-making

5.36 (0.96) 3.89 (1.84)

4.6 (1.07)

0.050 *

Importance of

decision-making

4.8 (1.56) 3.93 (1.86) 4.60 (1.16) 0.370

Consensus in

decision-making

5.20 (1.45) 3.75 (1.98) 4.97 (1.29) 0.07 0**

Participation in

decision-making

5.63 (1.55) 4.04 (2.31) 4.36 (1.36) 0.120

Satisfaction with

information for

decision-making

5.38 (1.06) 3.96 (2.33) 4.37 (1.47) 0.170

p<.05, p<.01 * Significant **non-significant (trended towards significant)

Participants were then grouped into two major age groups (i.e., less than 40 years, and

over 40 years). A one way ANOVA showed a significant difference in the satisfaction with

information and trended towards significant in the use of evidence and importance of

decision-making. Notably, the younger age group reported lower scores on all five constructs,

similar to findings observed in the pre-PAR intervention phase (see Table 6.7).

Subsequently, participants were grouped into three major tenure groups. The tenure

groups were constructed to represent duration of members serving in the LBHC, those who

were new to the LBHC (less than 12 months), intermediate members (12 to 24 months) and

veterans (more than 24 months). Findings indicated that new members tended to report lower

scores, the intermediate age group rated the highest, and the veterans group rated slightly

lower than the intermediate group. However, a one way ANOVA test showed no significant

difference between the means. Table 6.8 provides more details.

85


Table 6.7 Comparison of five constructs of decision-making processes with LBHC two major

age groups post- PAR intervention phase

Decision-making

construct 0-40 young 40+ veterans Sig

Mean (SD) N=10

Mean (SD) N=25

Use of evidence in

decision-making

3.80 (1.82) 4.80 (1.32) 0.080 **

Importance of decision-

making

3.60 (1.76) 4.68 (1.47) 0.072 **

Consensus in decision-

making

3.80 (2.05) 4.80 (1.58) 0.130

Participation in

decision-making

3.73 (2.34) 4.93 (1.71) 0.101

Satisfaction with

information for

decision-making

3.11 (1.87) 5.03 (1.58) 0.004 *

p<.05, p<.01 * Significant, **non-significant (trended towards significant)

Table 6.8 Comparison of five constructs of decision-making processes with LBHC tenure groups post-PAR

intervention phase

Decision-

making

construct

New to the

LBHC

Intermediate

members

Veterans Sig

Mean (SD)

N=9

Mean (SD)

N=10

Mean (SD)

N=16

Use of evidence

in decision-

making

3.97 (2.03) 4.92 (.98) 4.56 (1.48) 0.410

Importance of

decision-

making

3.92 (2.22) 4.63 (1.03) 4.45 (1.55) 0.620

Consensus in

decision-

making

4.08 (2.23) 4.60 (1.37) 4.70 (1.74) 0.700

Participation in

decision-

making

4.07 (2.49) 4.80 (1.34) 4.75 (2.00) 0.660

Satisfaction

with

information for

decision-

making

4.53 (2.05) 4.53 (1.41) 4.42 (2.10) 0.980

The qualitative data provided by the survey‘s participants revealed further detail. For

instance, one participant noted that: ―the HDSS provided information that forced us to discuss

and consider implications of future options and plan ahead to maximise positive decisions”.

However, although there was some evidence of increasing usage of information, consensus

and participation in decision-making, one participant noted that: “Evaluations and reflections

86


need to be better communicated and time needs to be set aside for groups, programmes and

the LBHC board to understand key messages of evaluations”. Another participant noted that:

“Being a placed-based initiative and from discussions on how effective we are in community

I wonder what direction we are taking and what difference we can make to communities in

health”. However, less positive notes were observed as well; for example, one participant

noted that: “My knowledge of decision-making within teams is limited so this is really a

reflection of governance decision-making”.

Subsequently, a content analysis was conducted across all participants‘ notes in the

decision-making survey. Figure 6.2 presents key themes which were identified. Findings

indicate that survey participants felt that the HDSS developed framework was or could be a

valuable tool for improving decision-making in the LBHC. However, to take it to the next

level, further development should be made with the LBHC board members. The majority of

comments made by survey participants revealed that the HDSS was perceived as a tool that

can provide the necessary information for decision-making. However, some participants

noted the importance of sufficient time allocation and improving communication mechanisms

of decision-making as a crucial and complementary component for any information tool (e.g.,

HDSS). Thus, the value of the HDSS was clear. However, despite evidence of positive

comments by LBHC participants, the major HDSS impact was observed across the following

specific groups: governance group, veterans, and those who served in the LBHC between 12

and 24 months.

87


Figure 6.2. Themes and concepts map (derived from the post-PAR intervention survey)

Overall, during the post-PAR intervention phase participants rated the decision-making

constructs higher than the pre-PAR intervention survey (out of the importance of decision-

making construct). However, some groups within the LBHC were more satisfied than others,

for example, those who were over 40 years, had served between 12 and 24 months in the

LBHC, and were associated with the governance group. Consequently, these groups were

more satisfied with information and reported higher levels of consensus, participation and use

of evidence in decision-making. To validate these findings, qualitative data from the survey

was also analysed. The qualitative data in the survey suggested that the HDSS was perceived

as a tool that can provide the necessary information for decision-making. However, some

participants noted the importance of sufficient time allocation and improving communication

mechanisms of decision-making as a crucial and complementary component for any

information tool (e.g., HDSS). The quantitative findings indicated that within some groups

(i.e., governance group, veterans and those who served in the LBHC between 12 and 24

months), there were high levels of decision-making constructs, but that this may not occur

across the whole LBHC. This was also supported by the qualitative findings, as negative

comments by participants were mostly associated with lower scores across the decision-

making constructs. Therefore, the findings showed that there is some diversity in the way

88


members of a LBHC view decision-making. Indeed, there was an overall sense that decision-

making were affected by the HDSS mostly within these groups.

6.3.3 COMPARISON BETWEEN PRE AND POST-PAR INTERVENTION DECISION-MAKING

SURVEY FINDINGS

To identify the impact of the HDSS intervention on decision-making, two independent

samples were used. The t-test for independent samples was conducted to compare the means

of five decision-making constructs (see Table 3.2). The literature indicates that the t-test for

independent samples is suitable to compare between averages in a research array of Pre and

Post examination between two independent groups (Sarid & Sarid, 2006). Table 6.9 presents

the results of this analysis in more detail. Findings indicated that there was a significant

difference in the scores for satisfaction with information for decision-making construct

(M=3.49, SD=1.53) in the pre-PAR intervention phase and (M=4.48, SD=1.86) in the post-

PAR intervention phase. This difference was found to be significant. As for the use of

evidence, consensus, and participation constructs, at the post-PAR intervention phase the

scores were slightly higher than at the pre-PAR intervention phase; however, the difference

was not found to be statistically significant. Surprisingly, the importance of the decision-

making construct was rated lower at the post-PAR intervention phase; however, this finding

was not found to be statistically significant. Figure 6.3 illustrates the results of decision-

making constructs throughout the study. The findings suggested that there was a difference

between the ways decisions were perceived in each phase; however, only the difference in the

satisfaction with information for decision-making construct was found to be statistically

significant.

89


Table 6.9 Comparison between the means of five decision-making constructs (pre- and post-PAR intervention)

Decision-

making

construct

Mean

(SD)

N Levene's

Test

T DF Sig. (2-

tailed)

Use of

evidence in

decision-

making (Pre and Post)

Pre:

4.38 (1.18)

Post:

4.51(1.52)

Pre: 36

Post: 35

.219 -.394 69 .69

Importance of

decision-


Pre:

4.89 (1.58)

Post:

4.37(1.61)

Pre: 37

Post: 35

.38 1.38

70 .171

Consensus in

decision-


Pre:

4.29 (1.57)

Post:

4.51 (1.75)

Pre: 35

Post: 35

.34 -.54 68 .58

Participation

in decision-


Pre:

4.41 (1.81)

Post:

4.59 (1.95)

Pre: 37

Post: 35

.67 -.38 70 .701

Satisfaction

with

information

for decision-


Pre:

3.49 (1.53)

Post:

4.48(1.86)

Pre: 35

Post: 35

.419 -2.42 68 * .018

p<.05, p<.01 * Significant, **Non-significant (trended towards significant)

Figure 6.3. Decision-making construct results pre-and post-PAR-intervention phases

90


6.3.4 DECISION-MAKING SURVEYS: OVERALL FINDINGS

The survey findings showed that (in general) most of the decision-making constructs

were rated higher in the post-PAR intervention phase. However, some groups within the

LBHC were more satisfied than others (i.e., those who were over 40 years, had served 12 to

24 months in the LBHC, and were associated with the governance group). This, in turn,

implies that the decision-making processes in the LBHC had changed over time towards

greater use of evidence, participation, consensus, and information. However, the qualitative

data in the surveys suggested that there was still a lack of allocated time and communication

mechanisms for decisions makers, and that this area required further attention and

development. Therefore, the findings indicated that there was an overall sense that decisions

were more effective, presumably because they were made with greater information, use of

evidence, participation and consensus. However, the survey only supported these findings in

a partial manner due to non-significant difference across some of the decision-making

constructs (i.e., use of evidence, consensus, and participation). Thus, to overcome this

problem, and to achieve validated findings across these constructs, another methodological

instrument was used (see Section 6.4).

6.4 ACTUAL DECISION-MAKING FINDINGS

As explained in Chapter 3, to identify trends in the number and, more importantly, to

identify the nature of the decisions made by the LBHC board members, two meetings were

selected in each year to provide a sample, commencing from the outset of this study (2008) to

the post-PAR intervention phase (2010 and 2011). To examine whether any change has

occurred in the way actual decisions were made, the analysed meetings were clustered into

two groups. Specifically, four analysed meetings were associated with the period before the

PAR intervention (pre-PAR intervention phase) and four meetings after (post-PAR

intervention phase). Evaluation of the actual decision-making processes was conducted by

listening to audio-recordings of the selected LBHC board meetings, analysing the minutes

and preparing observer‘s notes (see also Table 3.3). To attain a deeper understanding of the

actual decision-making made by the LBHC board members, content analysis has been

conducted for each meeting that was analysed. Through this analysis, key themes and

concepts were identified4. This type of analysis revealed the themes or concepts that were

4 The brightness of a concept‘s label reflects its frequency in the text. That is, the brighter the concept label, the more often

the concept is coded in the text.

91


frequently discussed in each LBHC board meeting. In addition, when appropriate,

participants‘ statements were used to substantiate and highlight major findings.

6.4.1 PRE-PAR INTERVENTION PHASE: ACTUAL DECISION-MAKING FINDINGS

Actual decision-making findings: LBHC board meeting conducted on the 08/05/2008

The meeting conducted on 08/05/2008 contained a large number of updates on a variety

of topics. Upon completion of updates, there was a thorough discussion about the key

performance indicators (KPIs) of the LBHC board. This was followed by a discussion on the

GIS as a tool for data collection. Figure 6.4 (Frequency of themes) presents the main themes

and concepts5 identified in this meeting. The content analysis (via Leximancer) indicated that

the discussion was mostly attributed to one participant (i.e., Participant in Figure 6.4), who

led this discussion. Also, Figure 6.4 shows that the GIS and KPI were frequently discussed

during this meeting. During the discussion, one of the participants noted: “the whole point of

getting together in a coalition and make decisions, is to get more as a group”. This

participant noted the importance of making decisions in a consensual or agreed manner (see

Figure 6.4).

Upon completion of the KPIs discussion, one of the participants led a discussion about

the GIS. During this discussion, one of the participants noted: “we need a base line datasets

and measurements”. In response, other participant addressed that comment, saying: “given

the adopted Social Determinant of Health (SDH) approach in the LBHC, further discussion is

required, and the GIS team should be invited to the next LBHC board meeting”. Moreover,

another participant noted that: “we need to be able to consult as the process develops, what

needs to be looked up more thoroughly”.

Two decisions were made during this board meeting. One decision was associated with

KPIs, and one decision with the use of GIS as a tool for data collection within the LBHC.

Overall, the decisions were based on some level of evidence, predominantly provided by the

research team from Griffith University. However, only a few board members participated in

the discussion (three participants). Therefore, decisions made were characterised by a limited

amount of evidence and limited level of participation and consensus. Table 6.10 summarises

the actual decisions according to the three decision-making constructs: use of evidence, level

of participation, and level of consensus.

5 Themes and concepts coloured by red or orange denote the most relevant, and cool colours (e.g., blue, green) denote the

least relevant, whereas the size of the themes and concepts denotes its frequency in the meeting minutes.

92



08/05/2008)

Table 6.10 Summary of the actual decisions according to the three decision-making constructs (derived from

meeting conducted on the 08/05/2008)

Actual decision made Use of evidence Level of

participation

Level of consensus

It was decided to include

KPIs to form evidence of

new partnerships and how

these changed over time

Discussion of knowledge





local reports)

Only a few

members (i.e., less

than 50%) played

an active role in the

discussion by

speaking or

presenting a

viewpoint

Only a few members (i.e., less

than 50%) verbally agreed with

the decision, or no

opportunities were available to

disagree (i.e., no requests to

indicate agreement or active

disagreement was suppressed)

***Inclusion of the GIS as

a tool for data collection

within the LBHC

Evidence limited to



Only a few

members (i.e., less

than 50%) played

an active role in the

discussion by

speaking or

presenting a

viewpoint

Only a few members (i.e., less

than 50%) verbally agreed with

the decision, or no

opportunities were available to

disagree (i.e., no requests to

indicate agreement or active

disagreement was suppressed)

*** Decision was not reflected in the meeting‘s minutes

93


Actual decision-making findings: LBHC board meeting conducted on 13/11/2008

The meeting conducted on 13/11/2008 also contained a large number of updates on a

variety of topics. As a result, substantial time in this meeting was associated with sharing

information. Upon completion of updates, there was a discussion about the Public Health

Plan for Beaudesert followed by a thorough discussion about a new programme, namely the

Q2 Health Promotion Scholars. The Public Health Plan for Beaudesert (which is associated

with planning) was the most frequently discussed topic. In this regard, a thorough discussion

was held and some participants raised questions as to whether this plan should focus on

Beaudesert solely or should also include other nearby rural areas. Figure 6.5 supports the fact

that the public health plan for Beaudesert was extensively discussed in this LBHC board

meeting.

Another discussion was led by the head of the LBHC and focused on the required

expertise needed for the Q2 Health Promotion Scholars programme. Some participants were

more active in this discussion than others. One participant contributed significantly due to his

expertise in the relevant area and his contribution enabled others to interpret the available

information. This participant noted the following: “we are seeking people who are team

workers, aiming to achieve outcomes for the local community, and have knowledge in health

promotion”. However, this input was not noted in the minutes and not documented

elsewhere.

In summary, two decisions were made during this board meeting, one associated with

the public health plan for Beaudesert, and another with the inception of the Q2 Health

Promotion Scholars programme. Although many of the LBHC board members were involved

in the discussion on the public health plan for Beaudesert, little use of evidence was

observed. Indeed, one of the participants noted: “this kind of discussion should be based on

more evidence”. Furthermore, another participant noted: “the more talk the better”,

presumably because it assisted the group to make a decision in a participatory manner.

Overall, both decisions were characterised by only a limited level of participation and

consensus, which was predominantly led by one or two LBHC board members. For example,

during the discussion about the Q2 Health Promotion Scholars Programme, the head of the

discussion rushed the LBHC board members in making the decision by saying: “so are you

happy to support this decision so we can move on?”. Thus, in both decisions, a limited use of

evidence was observed. Table 6.11 summarises the actual decisions according to the three

decision-making constructs: use of evidence, level of participation, and level of consensus.

94



13/11/2008)



Actual decision made Use of evidence Level of participation Level of consensus

The LBHC board agreed

to sponsor and fund the

development of a social

plan for Beaudesert

Evidence limited to





active role in the


presenting a viewpoint.

Only a few members



the decision, or no

opportunities were



indicate agreement or

active disagreement

was suppressed)

The LBHC board

supported the initiation of

the Q2 Health Promotion

Scholars Programme

Evidence limited to





active role in the



Only a few members



the decision, or no

opportunities were



indicate agreement or

active disagreement

was suppressed)

95



The meeting conducted on 14/05/2009 contained a large number of updates on a variety

of topics. As a result, substantial time at this meeting was associated with sharing

information. For instance, it was observed that at least a quarter of the time allocated for the

meeting (30 of 120 minutes) was associated with sharing information. In addition, a large

portion of this meeting focused on evaluation feedback conducted by a group of researchers

from Griffith University. A thorough discussion was held on this topic. Some participants

raised questions in regards to the identified areas of challenges that required further action by

the LBHC board members. For instance, one of the participants noted: “where are the

tangible things?”. Another participant stated: “we need to make our decisions more

grounded in evidence” and, “how do we know that we have achieved what we were aiming to

achieve?”. These statements imply that LBHC board members felt they were not using

enough evidence in making their decisions and that this was amongst their primary concerns.

Figure 6.6 illustrates some of the topics discussed during this meeting: health coalition, the

need for collaboration with a research group that will provide a feedback to the LBHC board

members and the Logan community in the longer term.


14/05/2009)

96


Given that the LBHC board meeting was mostly utilised for updates and receiving

evaluation feedback (provided by the Griffith University research group), only one decision

was made. The decision was associated with endorsement to publish the LBHC board

members activity in the community, by advertising in local newsletters. Overall, the decision

was characterised by a limited level of participation and consensus, with the discussion

predominantly led by only a few board members. The decision was characterised by a limited

level of evidence. Table 6.12 summarises the decision according to the three decision-making

constructs: use of evidence, level of participation, and level of consensus.



Actual decision made Use of evidence Degree of collaboration

and participation

Degree of consensus

Endorsement to publish

the LBHC board activity

in the local community,

by advertising in

newsletters

Evidence limited to





active role in the




less than 50%) verbally

agreed with the decision,

or no opportunities were

available to disagree (i.e.,

no requests to indicate

agreement or active

disagreement was

suppressed)


The meeting conducted on 08/10/2009 contained a long informal discussion about the

LBHC board role (approximately 60 minutes). A thorough discussion was held and most

participants raised questions about the ways in which LBHC board members should operate,

and whether the LBHC board had achieved its designated goals. Another discussion was led

by a few board members about the Optimal Health Programme (OHP). The meeting

concluded with a brief discussion about data collection. Figure 6.7 illustrates some of the

topics discussed during this meeting: the different programmes the LBHC board oversees,

and the need for a thorough discussion which will be based on the LBHC programmes

reports.

During the discussion about the role of the LBHC board, one of the participants

stressed that: “if we keep the formal models, we are going back to formats that have not

achieved results in the past”. The discussion pointed out that although the board

acknowledged that there was sufficient evidence to make informed decisions, it was not being

used effectively. One of the participants noted that: “So much knowledge is tossed away, but

97


not being channelled to the right place”. Another participant stated that: “It is not just „here

is the money [the LBHC funds health programmes] and that‟s it‟, we need to revaluate these

projects we approve throughout the whole process‖. Another participant critiqued the process

by saying: “shouldn‟t we need to make decisions about the ongoing programmes, and then

revaluate things?”. These statements indicate that the LBHC board members recognised that

(to some extent) it had the necessary evidence to make informed decisions, but not the

capacity or the mechanism to effectively use it. To support this statement, one of the

participants noted that: “we have the evidence but we don‟t have the time to revisit this”.

Another participant noted: “the board has a good base line, but we would like to see some

action plans to address our goals”. There was a consensus that these types of informal

discussions were beneficial for the LBHC board members in exploring their role. In this

sense, one of the participants noted: “At least we had this conversation which was very

good”. Although the discussion was thorough and included many of the LBHC board

members, only one decision was made, that is, to hold regular informal discussions in future

meetings.

Another discussion was led by a few LBHC board members about the OHP. The topic

was introduced, and then a brief discussion held about the collection of data. One participant

noted that: “the reports we are getting are quite good”. However, only a few

LBHC board members took an active part in the discussion. One decision was made in regard

to the OHP, that is, to continue the programme. One of the participants asked: “Are people

happy with this?”, but no response was observed.

Two decisions were made during this LBHC board meeting, one associated with the

inclusion of informal discussions during LBHC board meetings, and the other with the

approval to proceed with the OHP. Overall, one decision was characterised by a high level of

participation and consensus, as a very fruitful discussion was observed in which all LBHC

board members were involved. The other decision was characterised by a high level of

evidence, but a low level of participation and only moderate level of consensus. Table 6.13

summarises the actual decisions according to the three decision-making constructs: use of

evidence, level of participation, and level of consensus.

98



08/10/2009)




Have an informal

session at the start of the

LBHC board meetings

(rather than trying to fit

it in at the end of

meetings)

Evidence limited to




evident from all members

or a comprehensive and




members

All members agreed


decision

The board is supportive

of the contract (i.e.,

OHP) to continue and

proceed up to director

general for signing

External evidence from


reviewed and

incorporated in the

discussion



active role in the



Most members agreed


members agreed, but


were available to disagree

6.4.2 POST-PAR INTERVENTION PHASE: ACTUAL DECISION-MAKING FINDINGS


The meeting conducted on 11/03/2010 focused on a range of topics. First, there was a

thorough discussion about the frequency and duration of the LBHC board meetings. Second,

there was a discussion about the way meetings and decisions are minuted. Third, there was a

99


discussion focussed on the Logan Public Health Plan. At the end of this meeting, there was an

open discussion about the LBHC board role and the way decisions were made in practice.

The discussion about the LBHC board meetings focused on improving the board

meetings‘ effectiveness. For example, it was suggested that LBHC board meetings would be

held bi-monthly, but each meeting would be held for a longer time. One of the participants

suggested using video conferencing technology for this purpose. Eventually, the LBHC board

agreed to conduct these meeting as suggested (less frequently but longer). Many LBHC board

members were involved in the discussion, although limited use of evidence was observed.

One of the participants raised the need for evidence asking: “does making longer meetings

improve its effectiveness?”.

Another discussion was held about the way decisions were minuted in the LBHC board

meetings. One participant noted that: “decisions are being made elsewhere and then suddenly

appear in the minutes”, and another participant mentioned that: “some time the decision was

made before you walk through the door”. The discussion seemed complex and generated a

broad participation. For instance, one participant stressed that: “there were cases when the

minutes did not accurately reflect discussions or decisions that occurred during board

meetings or decisions reflected in the minutes had not been discussed in board meetings”

Another participant stated that: “decision to me is a process you go through, and in the end of

it we all agree”. Consequently, a decision was made to revisit the way decisions were

minuted. Ironically, this decision was not reflected in the minutes.

The LBHC board meeting concluded with a thorough discussion about its role, and

whether they should “get monthly reports or become an advisory group”. Furthermore, one

of the participants noted: “Either we become more autonomous, because Queensland Health

is not a willing partner”. Another participant summarised it by stating the following:

“leadership groups are not about where budgeting goes, and get reports, it is about

influencing and making a change”. Notably, in this regard, a positive comment was made by

another participant: “it took us many, many talks and time to get comfortable with each other

to make conversations at this level”. As a result of this discussion, a decision was made to

conduct a further discussion with one of the LBHC partners (i.e., Queensland Health);

however, this decision was not reflected in the meeting minutes. Figure 6.8 depicts the main

themes identified in this meeting. For example, it supports that the Logan public health plan,

the LBHC board, its agenda were extensively discussed during this meeting.

100



11/03/2010)

In summary, three decisions were made during this board meeting: one associated with

the LBHC board meetings; another with the way decisions were made and reflected in the

minutes; and a third associated with the LBHC board role. Overall, all decisions were

characterised by a high level of participation and consensus. However, in all decisions, a

limited to moderate level of evidence was observed. Table 6.14 summarises the actual

decisions according to the three decision-making constructs: use of evidence, level of

participation, and level of consensus.

101





LBHC board members

agreed to make bi-

monthly meetings for a

longer time

Evidence limited to









members

All members agreed


decision

The board should revisit

the way decisions are

made and then minuted

Evidence limited to









members

All members agreed


decision

To conduct a further

discussion with one of the

LBHC partners (i.e.,

Queensland Health) in

regard to the LBHC

board members‘ role






local reports)







members

All members agreed


decision


The meeting conducted on 14/10/2010 was mostly based on a series of updates and

discussions. First, there was a long updates discussion about the LBHC activities. Second,

there was a thorough discussion about the evaluation feedback provided by LBHC research

partners (i.e., Griffith University and The University of Queensland) about the LBHC board

practice. Third, there was a discussion about the strategic plan for 2011. Also there was a

short discussion about the recommended competencies of LBHC board members as

suggested by the research partner (i.e., Griffith University). Subsequently, there was an open

discussion about the procedures for presentations in the LBHC board and what should be the

process in case of conflict of interest. Finally, the meeting concluded with an open discussion

about the way decisions were made in the LBHC board.

The discussion about the evaluation focused on improving the LBHC board capacity

and overall practice overtime. It was noted that one of the state-wide evaluation reports stated

that there was a slight improvement in the chronic disease health status on the LBHC area in

comparison to overall Queensland‘s statistics. One of the participants noted that: “the LBHC

is saving life”. It was mentioned that: “we are trying to make less operational decisions and

102


instead make more strategic ones”. Despite the importance of this discussion, however, it

ended without any decisions being made.

Another discussion was held about the strategic plan for 2011 and KPIs. One

participant stressed the need to have: “tangible outcomes”, while another asked: “what is our

mission statement, does anyone have the document?”. The discussion generated broad

participation among LBHC board members and, consequently, a decision was made to review

and feedback KPIs for endorsement at the next board meeting

Afterwards, there was a short discussion about the recommended LBHC board

procedures outlined in the report produced by the research partner (i.e., Griffith University).

For example, the report suggested that the LBHC board should define criteria for recruiting

new board members, and improve its practice in a number of areas. In this regard, one of the

participants noted: “I think now, there is a greater level of confidence in the board”. As a

result, the LBHC board endorsed the recommended actions suggested in the report.

The board meeting concluded with an open discussion about the procedures for

presentations to the board and the process in case of conflict of interest. One of the

participants noted that: “criteria need to be established to manage conflict of interest

regarding what is appropriate to bring to the board agenda”. Consequently, the LBHC

board defined criteria to address these issues.

Notably, by the end of the LBHC board meeting, there was an open discussion about

the way decisions were made. One of the participants complained: “actually I can‟t

remember when we had a vote on something”. Another participant stated that: “votes need to

be minuted, especially budget decisions; it is important because we need to protect ourselves

if somebody comes one day and asks why they got budget”. Thus, there was a consensus that

this should be the case, especially for decisions associated with funding. It is important to

note that this decision was not reflected in the minutes. Figure 6.9 depicts the main themes

identified in this meeting. For instance, the board‟s action, its evaluation and the role of

LBHC board members were frequently discussed.

103



14/10/2010)

In summary, four decisions were made during this LBHC board meeting: one

associated with the LBHC KPIs, another about the report of recommendations by Griffith

University, one about procedures for presentations, and another un-minuted decision about

the way decisions are minuted in board meetings. Overall, all decisions were characterised by

moderate or high levels of participation and consensus. However, in all decisions, a limited or

moderate use of evidence was observed. Table 6.15 summarises the actual decisions

according to the three decision-making constructs: use of evidence, level of participation, and

level of consensus.

104





LBHC Board members to

review and feedback KPIs

for endorsement in next

LBHC board meeting






local reports)




enthusiastic discussion was

held that engaged more

than 50% of the members

All members agreed


decision

Competencies /

recommendations report

endorsed by LBHC board






local reports)




enthusiastic discussion was

held that engaged more

than 50% of the members

All members agreed


decision

Agenda item posing a

conflict of interest –

Member tabling agenda

item needs to consider the

people around the table,

level of discussion,

sensitivity (withhold

information) – declare a

closed meeting when a

conflict of interest is

identified. Presenters do

not have the right to ask

people to leave the room

Evidence limited to




played an active role in the

discussion or provided

input that influenced the

decision-making process

Most members agreed

with the decision OR all

members agreed, but


were available to

disagree

***Votes to be minuted

(especially funding

decisions)






local reports)


played an active role in the

discussion or provided

input that influenced the

decision-making process

Most members agreed

with the decision OR all

members agreed, but


were available to

disagree

*** Decision was not reflected in the meeting‘s minutes


The meeting conducted on 10/02/2011 was mostly based on a series of updates and

presentations by guest stakeholders in the LBHC. First, there was a long discussion about the

government health reform. Second, there was a thorough discussion about the Dietetic Clinic

proposal by the OHP. Third, there was a presentation and discussion on the evaluation

feedback by the research partner (i.e., Griffith University). Finally, the meeting concluded

with an open discussion about the governance manual, which constituted the board members‘

role and practice.

105


The discussion about the health reform focused on its influence on the community and

health organisations within the LBHC, including its impact in terms of contracts and

budgeting on one of the LBHC advisory groups which was likely to be at risk (i.e., OHP). It

was noted that: “there are a lot of unknowns at the present and how this is going to influence

our local organisations”. It was then decided that consultation between representatives of the

LBHC board members and Queensland Health needed to continue, but this was not reflected

in the minutes as a decision.

Another discussion was held about the Dietetic Clinic proposal by the OHP. The LBHC

board suggested that the programme should address issues such as organisational

development, capacity building and an overarching framework to guide the program to

sustainability. One of the participants asked: “are we supposed to make any kind of

decisions?”, indicating some confusion or dissatisfaction with the current situation.

Eventually it was decided that LBHC board members should be involved in developing this

programme, with revaluation in 12 months. This decision was not reflected in the meeting

minutes.

Subsequently, there was a discussion about the evaluation feedback by the research

partner (i.e., Griffith University). Some areas of success along with the areas for future

attention in the LBHC board practice were thoroughly discussed. Despite the importance of

the contents of this discussion, however, no decisions were made by the LBHC board.

The LBHC board meeting concluded with an open discussion about the governance

manual, which in practice constitutes the board members‘ role and practice. The LBHC board

members were requested to review this report prior to the meeting. The report included

sections on the board‘s role, the board manager‘s role, appointment of a new board member

and so forth. The LBHC board made a separate decision on each section related to its

practice. None of these decisions were minuted. Figure 6.10 presents the main themes

identified in this meeting. For instance, optimal health programme, the board‘s position in the

future, its plan, LBHC role, and required programmes to develop future capacity, were

frequently discussed. Also, the figure outlines that during this meeting one participant (i.e.,

participant 1) led the discussion.

106



10/02/2011)

Although the LBHC board meeting was long (i.e., more than four hours) and thorough,

only a few decisions were made. In summary, a few decisions were made during the LBHC

board meeting: one associated with the impact of the government health reform, and another

linked to the revaluation of the OHP. Three additional decisions were made concerning the

governance manual. Notably, two decisions were not minuted accurately, and others were not

minuted at all. Overall, the decisions were characterised by moderate or high level of

participation and consensus, and moderate or high level of evidence usage was observed.

Table 6.16 summarises the actual decisions according to the three decision-making

constructs: use of evidence, level of participation, and level of consensus.

Participant 1

107





**** Consultation

between representatives

of the LBHC board and

Queensland Health will

continue






local reports)



the discussion or

provided input that


making process

Most members agreed


members agreed, but


were available to disagree

****The LBHC board

members will be involved

in developing this

programme (i.e., Optimal

Health), which will be re-

evaluated in 12 months






local reports)







members

All members agreed


decision

***The LBHC board

commented and decided

on three sections in the

governance manual

linked to the LBHC

board‘s overall role and

practice

External evidence from


reviewed and

incorporated in the

discussion







members

All members agreed


decision

*** Decision was not reflected in the meeting‘s minutes **** Decision was not reflected accurately in the

meeting minutes (the decision was minuted but not under the title of ‗decision‘ in the minutes document)


The meeting conducted on 09/06/2011 consisted of a series of discussions. First, there

was a thorough discussion about the future of the HDSS and the LBHC website. Second,

there was a discussion about the contract renewal of the research partner (i.e., Griffith

University). Third, there was a discussion on the Health Promotion Programme. Finally, the

meeting concluded with an open discussion about the governance manual, and minor changes

were taken.

The discussion about the HDSS focused on its future development and how it will be

hosted in the near future. Specifically, there was a discussion about the sustainability of the

HDSS and the way access should be expanded within the LBHC. One of the participants

noted: ―we need to stick with the development, and the system needs to be relevant for the

users”. Another participant asked: ―is there enough information there; the system has not yet

achieved the next level of analysis”. It was decided that the HDSS would be hosted by

Griffith University until the end of 2011, and access would be expanded to all LBHC

108


members, including specific stakeholders from Logan City Council and the Logan Public

Health team of Queensland Health.

Another short discussion was held about the Research and Innovation contract. One of

the participants stated: ”there was a cultural shift in the LBHC since the involvement of the

research team” (i.e., Griffith University). The LBHC board decided that the Research and

Innovation contract would be endorsed.

Subsequently, there was a discussion about the Health Promotion Programme. The

board discussed the tangible achievements of the programme, along with areas for future

attention. One of the participants asked: ―do we have less tangibility from this programme?”.

Another participant noted that the Health Promotion Programme should continue, but with

some modifications. He also noted that: ―we had huge load on the management role”.

Eventually, the board decided to support this programme.

The LBHC board meeting concluded with an open discussion about the governance

manual, which as mentioned previously constitutes the board members role and practice.

Specifically, in this meeting the LBHC board made a separate decision on each section

related to access for information in the LBHC. Notably, the earlier discussion about the

HDSS prompted some of the questions raised by LBHC board members in this discussion.

One participant asked: “who has access to information in the LBHC?”. Another participant

addressed this comment by stating: ―LBHC board members, and members of the LBHC

advisory groups and associated programmes”. Consequently, it was decided to define and

clarify in the governance manual exactly who is a LBHC member. Figure 6.11 presents the

main themes identified in this meeting. For instance, HDSS, funding, LBHC, and required

changes were frequently discussed. The LBHC board meeting was productive and a high

level of participation was observed. In summary, four decisions were made during the LBHC

board meeting. One was associated with hosting and expanding the access of the HDSS,

while another was linked to the Research and Innovation contract. In addition, decisions were

made about the Health Promotion Programme and the governance manual. Overall, decisions

were characterised by a high level of participation and consensus and moderate or high level

use of evidence. Table 6.17 summarises the actual decisions according to the three decision-

making constructs: use of evidence, level of participation, and level of consensus.

109



09/06/2011)




The HDSS would be

hosted by Griffith

University until the end

of 2011, and access

would be expanded to all

LBHC members,

including specific

stakeholders from Logan

City Council and the

Logan Public Health

Team of Queensland

Health






local reports)







members

All members agreed


decision

Research and Innovation

contract endorsed (with

minor changes)






local reports)



the discussion or

provided input that


making process

All members agreed


decision

Health Promotion

Program endorsed










All members agreed


decision

110



local reports) was held that engaged


members

Operational Governance

manual endorsed






local reports)







members

All members agreed


decision

6.4.3 ACTUAL DECISION-MAKING: OVERALL FINDINGS

The observed decisions were grouped into two phases (i.e., pre-PAR intervention phase

and post-PAR intervention phase). Once decisions were evaluated, it was possible to examine

whether there was any distinction between the two phases. The pre-PAR intervention phase

(four meetings) included seven decisions. Five of these decisions were characterised by

limited use of evidence, six decisions were characterised by limited level of participation, and

five decisions were characterised by a low level of consensus (See Table 6.18). Thus, only a

few decisions were characterised by a moderate or high level of any of the decision-making

constructs.

In the post-PAR intervention phase (four meetings), 14 decisions were observed. The

findings indicate (see Table 6.18) that ten of these decisions were characterised by moderate

use of evidence, ten decisions were characterised by high level of participation and eleven

decisions were characterised by high level of consensus. Furthermore, only three decisions

were characterised by limited level of evidence.

In summary, findings show that more decisions were characterised by either moderate

or high level of any of the three decision-making constructs in the post-PAR Intervention

Phase. This, in turn, implies that the decision-making processes of the LBHC board had

changed over time towards greater use of evidence, participation and consensus. It was

observed that the LBHC board had been through a cultural shift. For instance, less negative

comments were observed in the post-PAR intervention phase about the way the LBHC board

operates and the fact that decisions were made out of the meetings. In addition, more positive

comments were observed in the LBHC board meetings about the level and thoroughness of

discussions. For example, one of the participants noted: ―There was a cultural shift in the

111


LBHC”. Another participant stated: ―I think now, there is a greater level of confidence in the

board”. To support this statement, one participant summarised it by stating that: I am

impressed with the constant reflection, assessment, and improvements within a limited

capacity. Given its stage of development, the HDSS is a useful, innovative and impressive

tool. Thus, the evidence suggests a shift in the way discussions and decisions were made over

the course of the study.

Table 6.18 pre- and post-PAR intervention phases summary of decisions by the three decision-making

constructs

6.5 SUMMARY

Chapter 6 provided evidence about the impact of the HDSS or PAR intervention on

decision-making. The chapter consisted of two methodological instruments: decision-making

surveys and observational data of actual decision-making. The decision-making surveys‘

primary purpose was to evaluate the culture or climate in which decision-making are made

across the LBHC. As for the observational data of actual decision-making, the primary

purpose was to evaluate the way actual decisions were made. The findings of the surveys

indicated that there was an overall sense that decision-process was more effective during the

post-PAR intervention phase, given that they were made with greater information, use of

evidence, participation and consensus. In relation to the observational data of actual decision-

making, findings indicated that more decisions were characterised by either a moderate or

high level of any of the three decision-making constructs in the post-PAR intervention phase.

Pre-PAR intervention

phase


Limited level 5/7 6/7 5/7

Moderate level 1/7 ----- 1/7

High level 1/7 1/7 1/7

Post-PAR intervention

phase


Limited level 3/14 ----- -----

Moderate level 10/14 4/14 3/14

High level 1/14 10/14 11/14

112


This, in turn, implied that the decision-making processes of the LBHC board had changed

over time towards greater use of evidence, participation and consensus. Thus, Chapter 6

provided the required evidence in order to identify whether the HDSS or the PAR

intervention impacted on the way decisions are made across the LBHC. This, in turn,

addresses study objective 4 (see Section 1.3.1)

113

Chapter 7: Discussion and Conclusion


7.1 PREVIEW

Chapter 7 discusses the major findings of this study and draws conclusions in relation

to the original research questions. The chapter first reviews the study objectives and reiterates

the procedures undertaken to address them. After summarising the findings and exploring the

implications of these findings, it then highlights the study limitations and provides a series of

recommendations for future research.

7.2 BACKGROUND

The primary aim of the study was to develop a conceptual planning framework for

creating healthy communities and examine the impact of DSS in the Logan Beaudesert area.

The study focused on a case study of a health planning coalition (the Logan Beaudesert

Health Coalition – LBHC) located in the Logan Beaudesert area which was dedicated to find

new ways to address chronic disease. The study commenced with a comprehensive literature

review focused on healthy cities and communities, collaborative planning, decision support

systems, and the potential outcomes associated with using a DSS. To design and develop the

DSS, the study adopted a participatory action research design (PAR) which was enacted in

three consecutive cycles:

PAR Cycle 1: Introduction Stage;

PAR Cycle 2: Interaction Stage; and

PAR Cycle 3: Trialling Stage.

In PAR Cycle 1 the primary purpose was to raise awareness about GIS and the use of

DSS in informed decision-making. This stage consisted of a series of GIS introductory

presentations and discussions with LBHC board members. In PAR Cycle 2, the collaborative

process was used to explore the requirements of a DSS and to refine the technical

characteristics, features and functions of the system. In PAR Cycle 3, the system was

deployed and trialled within the LBHC for four months. During the PAR process, data was

collected continuously from LBHC members to understand their experiences and responses

to the system. In addition, using a pre- and post design, two waves of data were collected to

114


assess decision-making - one prior to the beginning of the PAR intervention, and one

following the completion of the PAR intervention. Thus, all elements of the research

provided important evidence to address the study objectives.

7.3 REVIEW OF THE STUDY OBJECTIVES

The study aimed to develop a conceptual planning framework for creating healthy

communities and examine the impact of DSS in the Logan Beaudesert.

In order to achieve the aim of the study, four objectives were identified:

o To identify the key elements and domains of information that are needed to

develop healthy communities;

o To develop a conceptual planning framework for creating healthy

communities;

o To collaboratively develop and implement an online GIS-based Health DSS

(i.e., HDSS); and

o To examine the impact of the HDSS on local decision-making processes.

These objectives were achieved through a comprehensive literature review and a case

study based on various forms of data collection during the PAR cycles, including a user

satisfaction survey, a Logbook of interactions with stakeholders, information priority survey,

feedback sessions and usage statistics. In addition, two iterations of a decision-making survey

and observation of the actual decision-making were conducted before and after the PAR

intervention. Table 7.1 presents the data collection methods which were used to achieve the

study objectives. Table 7.2 illustrates in more detail the theoretical links between the

literature review and the study objectives. It also provides information about how the study

objectives were addressed.

115


Table 7.1 Data collection tools used to achieve the study objectives

Data collection tools /

Methods

Objective

To identify the

key elements

and domains of

information that

are needed to

develop healthy

communities

To develop a

conceptual

planning

framework for

creating

healthy

communities

To collaboratively develop

and implement an online

GIS-based Health DSS

(i.e., HDSS)

To examine the

impact of the HDSS

on local decision-

making processes

Literature review

Case study

PAR intervention in

three cycles

Decision-making

surveys

Observational data of

actual decision-making

116


Table 7.2 Summary of literature review findings and empirical tools developed to address study objectives

Literature review and

empirical findings

Study Objective

To identify the key

elements and domains

of information that are

needed to develop

healthy communities

To develop a conceptual planning framework for

creating healthy communities

To collaboratively develop

and implement an online

GIS-based Health DSS (i.e.,

HDSS)

To examine the impact of the

HDSS on local decision-making

processes

Relevant conceptual-theoretical

sources

The six areas

characterising a

healthy city and

community (WHO,

1997)

Public health

framework for health

impact assessment

and health profiling

(derived from Schulz

& Northridge, 2004)

Elements of each level of collaboration (derived

from Mattessich et al., 2001, p. 61)

Communicative planning theory (Healey, 1997)

The six areas characterising a healthy city and

community (WHO, 1997)

Public health framework for health impact

assessment and health profiling (derived from

Schulz & Northridge, 2004)

Decision support systems (DSS) as a tool to

help decision-makers assess complex problems

and solve those problems in a meaningful way

(Shim et al., 2002)

Potential DSS outcomes (Igbaria & Guimaraes,

1994; Phillips et al., 2000; Higgs & Gould

2001; Buckeridge et al., 2002; Cromley &

McLafferty, 2002; Waring et al., 2005)

PAR approach (Minkler,

2000; Israel et al., 2001;

Krasny & Doyle, 2002;

Minkler & Wallerstein,

2003)

Decision-making scales (Dean

& Sharfman, 1993; Parnell &

Bell, 1994; Flood et al., 2000;

Mattessich et al., 2001;

Bennett et al., 2010)

User satisfaction (Omar &

Lascu, 1993)

Relevant empirical tools used to

address study objectives

PAR intervention PAR intervention study

Decision-making survey

Observational data of actual

decision-making

117


7.4 MAJOR FINDINGS

7.4.1 OVERVIEW

The findings of this study revealed a meaningful framework for organising information

to guide planning for healthy communities. This framework provided a comprehensive

system within which to organise existing data. The PAR process was useful in engaging

stakeholders and decision-makers in the development and implementation of the online GIS-

based DSS. Through three PAR cycles, this study resulted in heightened awareness of online

GIS-based DSS and openness to its implementation. It also resulted in the development of a

tailored system (i.e., HDSS) that addressed the local information and planning needs of the

LBHC. The study revealed important features of the development and implementation

process that will contribute to future research. In addition, the implementation of the DSS

resulted in improved decision-making and greater satisfaction with decisions made within the

LBHC.

7.4.2 KEY ELEMENTS AND DOMAINS OF INFORMATION THAT ARE NEEDED FOR

DEVELOPING HEALTHY COMMUNITIES

To address the first study objective, a comprehensive literature review was conducted.

This provided the required knowledge to identify the key elements and domains of

information that are needed to develop healthy communities. For example, Schulz and

Northridge (2004) suggested a solid and validated framework for health impact assessment

and health profiling. This framework was then used as a baseline to collect information for

the HDSS. In addition, the WHO (1997) defined the six areas characterising a healthy

community. This, in turn, helped to identify the qualities that a healthy community needs in

order to be established.

7.4.3 A CONCEPTUAL PLANNING FRAMEWORK FOR CREATING HEALTHY COMMUNITIES

To address the second study objective, a comprehensive literature review was

conducted and a case study (i.e., LBHC) selected. Specifically, and inspired by the WHO

(1997), Duhl and Sanchez (1999), and Schulz and Northridge (2004), a comprehensive

planning framework for creating healthy communities was developed. As suggested by

Schulz and Northridge (2004, see Section 2.2.2), the health information framework guided

the development of a health profiling for the LBHC. Specifically, it supported the

establishment of a community knowledge-base (i.e., LBHC health profile), with information

derived from multiple sources. The Schulz and Northridge (2004) framework was then used

as a baseline to collaboratively prioritise what information would be included in the HDSS.

118


Derived from the literature review, the study hypothesised that the ability to present this

information in a meaningful, accessible and usable way (i.e., HDSS) could positively

influence the way decisions are made, in order to create (in the long term) a healthy

community. In this regard, Duhl and Sanchez (1999), and the WHO (1997) defined a list of

six fundamental characteristics (health public policy, innovation, community participation,

intersectoral action, policy decision-making and commitment to health) that are needed to

create a healthy community. The conceptual framework then suggested that if these elements

are adopted, it is likely that a healthy community will emerge. Further, this conceptual

planning framework suggested that a DSS that exists as part of a broader health planning

process should facilitate these qualities.

7.4.4 PARTICIPATORY ACTION RESEARCH INTERVENTION

To address the third study objective, a comprehensive literature review was conducted,

a case study (i.e., LBHC) selected, and a collaborative PAR intervention implemented. The

HDSS design and implementation process consisted of a series of consultation meetings with

LBHC participants. As a result of this collaborative process, the HDSS information items,

features, functionality, and health scenarios were defined. Upon completion of the design and

development processes, the HDSS was deployed. Findings suggested that throughout the

planning process (i.e., PAR intervention), different components and elements were identified

as vital. For instance, it was found that, early in the project, scoping the technical components

associated with access, information and data were crucial. This was executed by an HDSS

steering committee which was established for planning purpose. Furthermore, it was

recognised that this planning group required further action, substantial time investment and

‗in-house‘ interactions (e.g., consultation meetings) to define the objectives, deliverables,

needs‘ assessments, system scope, conceptual design and technical design for the HDSS.

Upon completion of the design and development process the HDSS was deployed.

However, it was then found that further development and data expansion was required to

make the system more usable. Specifically, the LBHC board members suggested that its

information could be expanded, which would make the HDSS more applicable for their day-

to-day role. It was also found that to sustain the HDSS in an adequate manner it was essential

to establish a new group to advance the system. Thus, the information communication

management (ICM) LBHC advisory group role was to define and help with the following

tasks:

119


How the system would be supported;

What and when updates would occur;

How users would be trained;

What further development and functionality were required;

How new findings could be attained; and

To market and promote the system further, as well as determine its future goals.

In addition to the PAR intervention, helping to scope the HDSS and its technical

requirements, the evidence suggested that the knowledge created by the PAR intervention

helped to generate the notion of „collaboration‟ in the planning process. This, in turn,

positively contributed to the overall impact of the HDSS, as LBHC participants sensed they

were contributing in the planning process and playing an important role in developing the

system. All parties were committed to constantly improving the HDSS by enhancing and

refining the system based on users‘ feedback, and the sense of collaboration increased.

Furthermore, it was noted that DSS can produce the type of information and effectiveness

that facilitates collaborative planning, with research indicating that online DSS environments

have a positive impact on decision-making processes (as also reported by Kingston et al.,

2001).

7.4.5 DECISION-MAKING IMPACT STUDY

To address the fourth study objective, a comprehensive literature review was

specifically conducted into decision-making scales and measurements, a case study (i.e.,

LBHC) was selected, and two waves of data collection (i.e., pre-and post-PAR intervention)

were used to explore and understand decision-making before and after the PAR intervention.

The decision-making impact study consisted of two instruments:

Decision-making surveys; and

Observations of actual decisions-making.

Decision-making surveys encompassed the whole LBHC and aimed to understand the

climate or culture in which decisions were made in the LBHC, whereas the observations of

actual decision-making made in the LBHC board aimed to identify and evaluate how

decisions were made, and whether decisions in the LBHC board had changed as a result of

the PAR intervention throughout the study period.

120


The way decision-making were rated in the pre-PAR intervention phase showed

generally poor to moderate scores. Findings indicated that the LBHC did not use solid

evidence as an integral part of their decision-making processes and, as a result, some

decisions were made outside the LBHC board. Consequently, this was found to be

contributing to the overall sense of disconnectedness, non-participation, and low level of

consensus in the LBHC.

However, in the post-PAR intervention phase, findings showed that more decisions

were characterised by either moderate or high level of participation, consensus and use of

evidence in that phase than in the pre-PAR intervention phase. This implied that the decision-

making process of the LBHC positively changed and improved over time. However, and

although not significant in all cases, findings indicated that there was some diversity across

the LBHC. For example, LBHC veterans tended to be more satisfied with the way decision-

making processes were made, as did those who had been members of the LBHC for either

longer or intermediate periods or associated with the governance group. This finding

indicated the likelihood of an acculturation curve for LBHC participants, that is, new

members were enthusiastic, but become more critical of decision-making over time and then

eventually resolved this situation in some way, either by withdrawing or seeking other

sources of information. In addition, finding showed that the age of members had an important

influence on the way decision-making was perceived. It is possible that younger people could

be more demanding in terms of their need for involvement in the decision-making processes,

whereas veterans are likely to have access to more intrinsic sources of information based on

years of experience in the area. As a result, they may be less demanding of the decision-

making processes. Thus, the findings of the decision-making survey indicated that as a result

of the PAR intervention, the climate or culture in which decisions were made had positively

changed. However, the findings also showed some diversity in the way members of a LBHC

view decision-making processes, and it is important to be aware of this, particularly in terms

of designing and making future decisions and polices in the LBHC.

As for the actual decision-making observations, findings stressed that more decisions

were characterised by either moderate or high level of participation, consensus and use of

evidence in the post-PAR intervention phase than the pre-PAR intervention phase. Thus, this

implies that the decision-making processes of the LBHC board have positively changed and

improved over time. However, it was observed that only limited or moderate levels of

evidence were used in the decision-making processes. This was explained by the fact that

121


although decisions were made through participation and in a consensual manner, LBHC

board members still lacked the required evidence in some occasions. This finding was also

supported by the user satisfaction survey (see Section 4.5.1), where the request for

information expansion was noted. In this sense, findings also showed that more analytical

tools were requested which were likely to improve the evidence needed by LBHC board

members to make decisions as part of their day-to-day role. Thus, overall, a cultural shift was

observed in terms of the way decisions were made by the LBHC board. However, the use of

evidence in decision-making was still perceived to be moderate and further improvements

were required.

7.4.6 SUMMARY OF MAJOR FINDINGS

Table 7.3 summarises the theoretical and empirical findings identified in the study. The

study findings related to the following research components: Framework for organising

health data, GIS-based DSS, and online tools (see Table 7.3). While all components were

aligned with the literature evidence, study findings associated with the PAR intervention

component substantially extended the literature line of knowledge. For example, these

findings suggested that the PAR intervention improved the sense of ‗collaboration‟,

commitment, use of evidence, and positively shifted the „culture‟ in which decisions were

made in the LBHC.

Based on the study findings, LBHC board members comprised the major group

influenced by the PAR intervention (see Figure 7.1). This is explained by their being

primarily involvement in the design, development and implementation process of the HDSS.

However, positive impact was observed in the secondary circle (i.e., LBHC). This impact was

mostly associated with the culture in which decisions were made and perceived by LBHC

members. In addition, and in line with Figure 3.3 (i.e., a conceptual framework for planning a

healthy community), long term impact is anticipated in the external circle, in which decision-

making made by the LBHC board members will positively impact the community, the health

planning practice and their health outcomes in the long term. Figure 7.1 illustrates the „spread

effect‟ impact of the HDSS or the PAR intervention.

122


AccessScrutiny HDSS

Impact on

LBHC board

members

• HDSS

• LBHC board

• LBHC

Impact on LBHC

(advisory groups)

Long term

impact in the

community

Long term

impact on

health planning

practice

Long term

impact on

health output

Figure 7.1. The spread-effect impact of the HDSS

123


Table 7.3 Theoretical and empirical outcomes of HDSS framework for planning healthy cities and communities based on research component

The quality that research

component is likely to create in

decision-making processes

towards development of healthy

communities

Framework for

organising health

data

GIS-based DSS Online tools PAR intervention (in three

cycles)

Theoretical findings Ability to assess health

impact and to profile the

community through the

social determinants of

health (derived from

Schulz & Northridge,

2004)

Increase collaboration between

stakeholders and communities;

Improve the accuracy and quality of the

decision-making processes; and

Improve the availability of data and

information for health decision-makers.

(Igbaria & Guimaraes, 1994; Phillips et

al., 2000; Higgs & Gould 2001;

Buckeridge et al., 2002; Cromley &

McLafferty, 2003; Waring et al., 2005)

Online tools broadening

the extent of usage and

having a positive impact

on decision-making

processes (Kingston et al.,

2001)

Achieved input from participants;

and

Translation of research findings

into informed action (Minkler,

2000).

Findings and evidence from the

study Helped to direct and

channel data collection

efforts for health

assessment

Improved the use of evidence in decision-

making processes across the LBHC

Overall, the online HDSS

had a positive impact on

decision-making processes

in the LBHC

Improved the sense of

‗collaboration‘ in the LBHC;

Increased the use of evidence,

consensus and participation in

decision-making processes;

Positively shifted the culture or

the ‗climate‘ in which decisions

were made in the LBHC; and

Helped to create the sense of

commitment by whole LBHC

board members

124


7.5 CONCLUSION

Despite growing awareness that decisions about social policies and health programmes

have a significant impact on health outputs, decision-makers still lack the frameworks and

tools to make these decisions in an informed manner. To enable health planners to make

effective decisions, this study highlighted the importance and need for a comprehensive

information framework, collaborative process, and useful tools to underpin planning for

healthy communities. The literature supported the premise that healthy communities will

need to encourage decision-making processes which is based on the use of evidence,

participation and consensus that subsequently transfer into informed actions.

However, to make informed decisions, simply increasing access to effective

information through online GIS-based DSS may not be sufficient to generate the type of

decision-making that can lead to healthy communities, unless health planning is also

practiced in a collaborative manner. This study utilised a PAR approach (i.e., PAR

intervention) throughout three PAR cycles to collaboratively design, develop and implement

the HDSS. The findings indicated that knowledge and shared understanding were created by

the PAR intervention rather than solely through the DSS technical design processes. For

example, it was observed that the knowledge that data was accessible to the LBHC board

members in a form that had not been dominated by any other parties (e.g., governmental

domination) and in a visual form (i.e., spatially), positively contributed to the notion of

„collaboration‟ and the sense of vision, in that all parties were committed to constant

improvement and refinement of the HDSS. The process used to develop the HDSS modelled

the type of process that might be applied to other health planning applications as part of a

process to develop healthy communities.

In terms of the decision-making impact, it was observed that the HDSS brought more

self-sufficiency to the LBHC board and improved the way in which decisions were made and

discussed. As for the variation across the LBHC, the tendency towards significant differences

between the sub-groups of the LBHC indicated that there may be considerable diversity in

decision-making processes that may require different approaches to health planning. In

addition, the surveys‘ findings showed that satisfaction with information for decision-making

was the most prominent change observed across all decision-making constructs; however,

this was to be expected. However, after more time passed, it could also influence actual

125


decisions, and this pattern was identified by the observational data for actual decision-

making.

In summary, it can be concluded that the PAR intervention had a positive impact on the

way decisions were made; however, further development (i.e., HDSS development) is

required to attain higher levels of evidence, consensus and participation in decision-making

processes. HDSS is still in its early days (i.e., pilot study), but it is anticipated that these

decision-making constructs will be improved in the near future. Hence, it is concluded that

HDSS can produce the type of information and effectiveness that facilitates elements of

collaborative planning, which improves decision-making and supports informed action by

health planners.

7.6 VALUE AND SIGNIFICANCE OF THE STUDY

The study introduced a new HDSS framework for the Logan Beaudesert region which

had not been implemented previously. The framework provides a guideline that health

planners elsewhere can develop and adapt to their own practice. To the present, health

planners had been told only ‗what‘ they should develop (i.e., healthy communities), but they

had not been provided with practical tools of ‗how‘ to achieve these goals. The framework in

this study was grounded by previous models from the literature (e.g., WHO, 1997; Schulz &

Northridge, 2004); therefore it is validated and practical for other health planners.

The findings are believed to be useful for health planners, decision-makers and

stakeholders who are either considering or involved in planning healthy communities.

Decision-makers involved in existing planning projects can better understand what

information should be collected to make informed decisions. Also, decision-makers would

now be aware of the process they should go through, and this is particularly important for

planning and funding purposes. Once the framework is implemented, it is likely to generate a

positive impact not only within the group of decision-makers, but also on the quality of

decisions made. This, in turn, provides several benefits in terms of planning to the local

communities.

Given this, the HDSS framework provides an alternative to traditional methods.

Besides the obvious practical uses of this framework, the findings from this study have also

contributed to the body of knowledge in the following fields: health planning, collaborative

planning and DSS. In addition, the development of this framework has presented a PAR

126


methodology that could be adapted in other areas. Thus, it may be concluded that the study

contributes in the following areas:

Theoretical – This study improves the understanding of the complex planning

processes required to develop healthy communities.

Practical – In the current literature there are many records of using DSS for health

planning. However, only a few studies employed DSS. Also, there was no previous DSS

framework that was able to effectively bring about better decision-making in the planning of

healthy communities in Queensland, Australia. Therefore, adopting this framework is

considered novel. The framework proposed in this study would not only encourage health

planners to engage with evidence and information about the entire range of health

determinants, but would also provide a platform for collaboration and shared engagement in

the decision-making processes. Even in its early development stages, the HDSS disclosed

very relevant health profiling information and insight into different data acquisition and

analysis methodologies.

Social – The study findings indicate that the HDSS framework generated a positive

effect in the processes of decision-making. Therefore, if other local authorities adopt this type

of framework and approach, it could improve the health services for their communities in the

long term.

7.7 LIMITATIONS OF THE STUDY

Several limitations were noticed whilst conducting this study, these include:

A longer time frame would have provided a more spacious period for HDSS users

to learn and use the system. Accordingly, it would have enabled a longer

evaluation time as adopting new technology is a long and challenging process;

The findings suggest that HDSS benefits the culture in which decisions were

perceived. For example, satisfaction of information for decision-making observed

as the major improvement. Subsequently, this conveyed more consensus and

participation in the way actual decisions were made. However, the findings suggest

that the initial satisfaction of information for decision-making was only the first

step. HDSS users requested specific health information and analytical tools to

better meet their day-to-day needs, and this was also supported by observation of

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moderate level based on the actual decision-making findings under the use of

evidence construct.

Literature suggests that some decisions are made informally due to political issues.

This is an important aspect of decision-making processes; however, this was

beyond the scope of the current study.

A longer time frame would also have increased evaluation time of the actual

decisions made in the LBHC board, thus improving the validity of the decision-

making observations and evaluated impact by the HDSS;

A larger number of the user satisfaction survey responses would have increased the

credibility of the survey analysis;

The findings may have achieved greater validity if they could have been compared

to similar cases in the literature. However, as the designed framework and

methodology were innovative, this was not possible;

Derived from Schulz and Northridge (2004) framework, and according to their

recommendations for collecting data for health assessment, health information

should be collected. In this case study, Queensland Health is the primary source of

health information. However, due to accessibility and strict policy, Queensland

Health data could not be included in the HDSS. The literature suggests that

accessing health information particularly as point-level (point features) is hard.

Therefore, alternative sources of health information were utilised. For example, the

HDSS used health information which was more accessible (i.e., the Social Health

Atlas of Australia). It is recommended, however, that accessing and utilising

Queensland Health information should be made possible in the foreseeable future.

Due to time constrains of the PhD study, the HDSS framework was tested only in

the decision-making processes level (See the dashed line in Figure 3.1). Thus, it

could have been more robust if it had been tested at the health outcomes level (see

bottom of Figure 3.1); and

Higher scores (in the user satisfaction survey) could support the development of a

better HDSS. This, in turn, would have generated a quicker positive impact on the

decision-making processes and better usage of the system in the trialling stage.

128


The interesting question: ―What is the right balance between technology and the

human experience factor in making the best decision‖, was not addressed as it was

beyond the scope of this study.

Early planning for the HDSS also recognised that plenty of data was collected

(e.g., more than 130 GIS layers) in the Logan Beaudesert area; however, there was

a debate about whether it was at the expense of developing spatial analysis

methods of interpreting the data.

7.8 RECOMMENDATIONS FOR FUTURE RESEARCH

Some recommendations for further research are also proposed. The user satisfaction

survey could be repeated in the near future to enable continual evaluation overtime. This, in

turn, may provide important evidence as to whether any improvement has been observed as

the HDSS evolves. Based on the findings of this study, it would be interesting to examine the

methodological links with the theory. For instance, it would be beneficial to test the HDSS

framework in the longer term, and clarify whether it could achieve a positive impact not only

at the decision-making processes level, but also at the health outcomes level in the

community (see Figure 3.1). This would involve testing the framework‘s ability to facilitate

collaborative health planning at a broader level. Thus, one of the potential development

directions of the HDSS could be its use as a collaborative tool to encourage public

participation or knowledge sharing in a certain community. As for decision-making, the

literature suggests that some decisions are informally made due to political issues, and it is

acknowledged that this is an important aspect of decision-making processes. Thus, while this

was beyond the scope of the current study, it could be further examined in future studies. As

the access to health data improves, it will be useful to include real-time data (e.g., people who

are hospitalised with chronic diseases, diseases‘ ratios and health statistics etc.) in the HDSS

(in a secured manner for the information of health planners solely) which will provide them

with better evidence in their day-to-day role. There is an intention to expand the data sets as

much as possible as it was found to be one of the most cost-effective ways to improve the

HDSS and broaden its potential. Furthermore, the HDSS framework should be further refined

for different projects, locations, governments, scopes and communities.

7.9 SUMMARY

The study provides a solid basis for health planners to improve their practice through

the implementation of a HDSS framework. It is anticipated that this framework will become a

129


pillar for decision-makers to inform their decision-making processes. The study has

addressed this particular gap in the knowledge, and as a result, local communities and the

general public will benefit in the future.

131

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137

Chapter 9: Appendices


9.1 DECISION-MAKING PROCESSES QUESTIONNAIRE

LBHC Decision-Making Survey

In answering the questions below, please consider the following definition of the

LBHC. The term LBHC has been used to refer to the entire coalition including all its

components (i.e., the Board, program areas and advisory groups or networks). In responding

to these questions, please consider how the LBHC operates as a whole entity.

Section A: Please answer the following questions about yourself (please place an X

in the appropriate place).

**** Have you completed this survey previously?

Yes____

No_____

1. What is your Gender?

____________

Male____Female_____

2. In what year you were born____________

3. What is your current marital status?

___ Married

___ Living with a partner or de facto

___ Separated / Divorced

___ Widowed

___ Never married

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4. What is the highest level of education completed?

___ Up to year 10 or equivalent

___ Year 11/12 or equivalent

___ Certificate/diploma

___ University degree or higher

5. In which component of the LBHC are you involved? (please specify)

____________________________________

6. Approximately how long have you been involved in the LBHC? (please specify)

______________________________________

Section B: Please answer the following questions about your feedback on the way

decisions are being made in the Logan Beaudesert Health Coalition.

Please rate your level of agreement with the following statements by

placing an X in the appropriate box.

N

ot

at

all

A L

ittl

e

So

me

Mo

der

ate

ly

Oft

en

Mo

stly

Co

mp

lete

ly

1. The LBHC looks for information when making a decision.

2. The LBHC analyses relevant information before making a decision.

3. Analytic techniques are important for making decisions in the LBHC.

4. The process of decision-making within the LBHC tends to be intuitive.

5. The LBHC focuses on crucial information and ignores irrelevant stuff.

6. The LBHC decisions are important.

7. The LBHC decisions have the desired impact.

8. The consequences of delaying LBHC decisions are serious.

9. LBHC decisions are not final until all relevant members agree.

10. Everyone‘s input is incorporated into important LBHC decisions.

11. It is worth more time to reach consensus on important decisions.

12. When making decisions, the LBHC works hard to reach agreement.

13. Everyone has a chance to participate in decision-making in the LBHC.

14. The LBHC uses a participative approach to reach effective decisions.

15. Group decisions in the LBHC are worth any extra time required.

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Section C: Please use this page to add any other comments you would like to make about the

way in which the LBHC currently makes decisions or plans for the future.

___________________________________________________________________________

___________________________________________________________________________

___________________________________________________________________________

___________________________________________________________________________

___________________________________________________________________________

___________________________________________________________________________

___________________________________________________________________________

___________________________________________________________________________

___________________________________________________________________________

**** This question appears only on the second round decision-making survey

Please rate your level of agreement with the following statements by

placing an X in the appropriate box.

The information that is currently available to me through the LBHC:

No

t a

t a

ll

A L

ittl

e

So

me

Mo

der

ate

l

y

Oft

en

Mo

stly

Co

mp

lete

l

y

16. The information helps me to recognize that a decision needs to be

made.

17. The information prepares me to make better decisions.

18. The information helps me to think about the pros and cons of each

option.

19. The information helps me to think about which pros and cons are most

important.

20. The information helps me know what matters most to the decision.

21. The information helps me to organise my own thoughts about the

decision.

22. The information helps me to think about how involved I want to be in

each decision.

23. The information helps me to identify questions I want to ask about the

decision.

24. The information prepares me to talk to about the decision.

25. The information prepares me for follow-up discussions about the topic.

140


9.2 USER SATISFACTION QUESTIONNAIRE

The HDSS User Satisfaction Questionnaire

The following questions require you to rate the importance and performance of different features of the HDSS. In rating these items, please

consider how the HDSS operates as a decision support system for the LBHC based on the exposure you have had to it so far.

Please tick the rating you feel most represents your evaluation of the HDSS feature – both

performance and importance responses need to be given for each item.

Importance

Please provide your rating of

the importance you attach to

each feature, on a scale of 1-7

where 1 is low importance

and 7 is high importance

Performance

Please provide your rating of

the performance of the HDSS

on each feature, on a scale of

1-7 where 1 is poor

performance and 7 is

excellent performance.

Lo

w

Med

ium

Hig

h

Po

or

Med

ium

Ex

cell

ent

1. Availability and timeliness of information provided by the HDSS 1 2 3 4 5 6 7 1 2 3 4 5 6 7

2. Ability to access the system without support from the system administrator 1 2 3 4 5 6 7 1 2 3 4 5 6 7

3. Accuracy and completeness of the information provided by the system 1 2 3 4 5 6 7 1 2 3 4 5 6 7

4. Flexibility of the data and its applicability to a range of scenarios 1 2 3 4 5 6 7 1 2 3 4 5 6 7

5. User confidence in the system 1 2 3 4 5 6 7 1 2 3 4 5 6 7

6. Ease of access for users to the HDSS 1 2 3 4 5 6 7 1 2 3 4 5 6 7

7. Current and up-to-date information provided by the system 1 2 3 4 5 6 7 1 2 3 4 5 6 7

8. Efficiency of the system in setting up, update and maintenance 1 2 3 4 5 6 7 1 2 3 4 5 6 7

9. Relevance of the system outputs to LBHC 1 2 3 4 5 6 7 1 2 3 4 5 6 7

10. System priorities that reflect the overall LBHC objectives 1 2 3 4 5 6 7 1 2 3 4 5 6 7

11. Defining and monitoring information systems policies for the HDSS 1 2 3 4 5 6 7 1 2 3 4 5 6 7

12. Level of LBHC involvement in defining and monitoring the system 1 2 3 4 5 6 7 1 2 3 4 5 6 7

13. Existence of a planning agenda to develop the system 1 2 3 4 5 6 7 1 2 3 4 5 6 7

141


14. Improvements to the system 1 2 3 4 5 6 7 1 2 3 4 5 6 7

15. System responsiveness to changing user needs 1 2 3 4 5 6 7 1 2 3 4 5 6 7

16. Quality and competence of the system 1 2 3 4 5 6 7 1 2 3 4 5 6 7

17. Technical competence level of the system administrator 1 2 3 4 5 6 7 1 2 3 4 5 6 7

18. Communication between users and the system administrator 1 2 3 4 5 6 7 1 2 3 4 5 6 7

19. Data analysis capabilities of the system to support the decision-making process 1 2 3 4 5 6 7 1 2 3 4 5 6 7

20. Availability of tools in the system to analyse issues related to the Logan Beaudesert area 1 2 3 4 5 6 7 1 2 3 4 5 6 7

21. User‘s feeling of participation in the HDSS 1 2 3 4 5 6 7 1 2 3 4 5 6 7

22. User influence on the development of the system 1 2 3 4 5 6 7 1 2 3 4 5 6 7

23. Helpfulness of the system administrator 1 2 3 4 5 6 7 1 2 3 4 5 6 7

Please tick the rating you feel most represent your evaluation of the following question

Satisfaction

Po

or

Med

ium

Hig

h

24. Overall, how would you rate your satisfaction with the HDSS system?

1

2 3 4 5 6 7

Please type below any other comments you would like to make about the HDSS, particularly in the context of user

experience and satisfaction.

........................................................................................................................................................................................................................

........................................................................................................................................................................................................................

........................................................................................................................................................................................................................

........................................................................................................................................................................................................................

........................................................................................................................................................................................................................

142


9.3 HDSS CORRECTIONS AND UPDATES REPORT

Feature /

name

comment Action taken

Printing When printing to JPG it opens in a new window; however,

some HDSS end-users are blocked by their browser default

settings

Message displayed in

Login Window. All

HDSS users should

enable their pop-up

windows for the HDSS

site

Search

feature

In this regard, it is important to state / note what facilities

are being searched. So, the layer named ―all facilities‖

needs to explicitly present its included sub-layers (in

parentheses).

Added sub-layer names in

parenthesis

Identify tool It is preferable that recent results will not be presented after

closing the tool, it confuses HDSS end-users. Further,

cancel the zoom to object when clicking the info icon;

HDSS end-users don‘t understand why they suddenly

zoomed-in to a particular object.

Previous results will not

be shown

* Zoom to geographical

object was cancelled

Refreshing

the maps

At the moment, the only option to refresh the map / system

is via F5 or refresh button. Yet, it forces HDSS end-users to

log-in again to the system.

Refresh button has been

added (can see it in

action, when you draw

something and then click

on the refresh button)

Layer names In all health layers (based on Health Atlas of Australia), we

need to add the following: rate per 1,000

Multicultural layers should be named at the following

order: Percentages African from total population,

Percentages Middle Eastern from total population,

Percentages Pacific Islander form total population and

Nationalities (it also appears in the metadata list)

Indigenous layers should be named in the following order:

Percentage Torres Strait Islander from total population,

Percentages Aboriginal from total population, and

Percentages Both Torres Strait Islander and Aboriginal

from total population

Layer names were

changed, and it is now

clear. All health layer

names (e.g. chronic

diseases) include now the

following note: Rate per

1,000

Centrelinks Changed layer name Centralinks to Centrelinks Layer name was changed

143


9.4 LOGBOOK

Item Phase Stage date Method of

Interactio

n

Participants Affiliation Content Decision has

been made in

response to the

interaction or

comment 1 Pre

Interventi

on

---------

----

9/11/2009 email Cheryl

Wardrope

EYI Hi Ori,

I have finally made a list of information that I would like to use from your GIS system. • Public housing

• Primary schools - state and private

• parks • child health

• oral health

• state pre-schools • community welfare

• employment services

• libraries • SEIFA index

• population

• population projections If some of these are not in a format that I can use, just let me know.

I have sent an e-mail off to Shannon Rutherford at GU Nathan to see if she knows anyone who may

be able to help with the air quality section.

So far we have identified that the mapping the Australian Early Development Index (AEDI) would be useful and possible the boundaries of our partners/MOUs.

Kind regards, Cheryl

144



Interactio

n


been made in

response to the

interaction or

comment Pre

Interventi

on

---------

----

9/11/2009 email Elizabeth

Kendall

GU

Thanks Ori and Cheryl

Happy to approve Cheryl's use of the data for EYI development - on the topic of the AEDI data, we

had planned on entering this data into the mapping, but were unable to access raw data at the time. It

would be ideal to map the AEDI against all the other determinants in the database. Ori or Cheryl, can you please follow up. I recall sending the name of the AEDI manager to Hoon at some stage for

follow-up but haven't heard anything. Cheryl, Hoon also has some family data from the Logan

survey and the Environments for healthy living survey. We can also make summaries of this data available to you.

Ori, Naomi, Cheryl's request is an important piece of data about the information-seeking and planning activities of the LBHC - probably our first real unsolicited request for planning data other

than Debbie's regular meetings and Council meetings! Thank you for asking Cheryl!

1 Pre

Interventi

on

---------

----

9/11/2009 email Naomi

Sunderland

GU E

Hi EK

yes I agree this is important marker in development of evidence use in the

LBHC

go Cheryl (and Ori!)

cheerio Naomi

I agree that exporting the data into pdf or excel files would be beneficial, especially for large

projects such as healthy city plans. Being able to visually see the data assists in developing a picture of the area much quicker but being able to present the data in tables etc for reports would be

helpful. We are still thinking of ways to use the system...

145



Interactio

n


been made in

response to the

interaction or

comment 2 Pre

Interventi

on

---------

----

9/11/2009 email Ori Gudes GU Dear Cheryl

Please find the requested files, sorry for the delay, as I had to extract the data separately from each GIS layer, hopefully in the near future you and the other end-users will able to do it straight-forward

from the HDSS.

(data has been delivered)

Kind regards Ori

Data has been

provided

3 Pre Interventi

on

-------------

12/11/2009 email Cheryl Wardrope

EYI • Does the system contain the location of the food banks in the area? • Is it possible to access data on children accessing speech pathology/therapy (occupational therapy

and dieticians as well) services in the Logan Hospital or through other services?

4 Pre Interventi

on

-------------

19/11/2009 Workshop with EYI

EYI, Ori Gudes GU, EYI GIS Presentation + feedback session, Forms were disseminated and feedback has been collected

146



Interactio

n


been made in

response to the

interaction or

comment 5 Pre

Interventi

on

---------

----


Wardrope

EYI Hi Ori and Elizabeth,

This is the information that I was hoping may be available (just to save me some time):

Ori,

I have had another look at your framework and you may or may not have any of this information accessible or easily accessible:

• Paul mentioned that you may have information on avoidable hospitalisations for the area that I

may be able to access. • I am wanting to include information on rates of disability.

• I haven't included transport, is there any information that can be easily taken from your system?

Or are there areas that clearly lack transport options. • Public housing - is this % of public housing in areas?

One of the populations that the EYI needs to engage with is the Cultural and Linguistically diverse

population • I have included % born overseas in my scan but am not sure if you have additional data that may

be useful.

• Would you have anything specific on refugee populations? • is there specific information for the CALD population on income, education, internet access, etc?

6 Pre

Interventi

on

---------

----


Kendall

GU You mentioned in an earlier e-mail that Hoon has some family data from the Logan survey and the

Environments for healthy living survey and you could make summaries of this data available. Is it

possible to access this?

I appreciate any assistance you can give me.

The 2009 AEDI community profiles will be available in March and I will pass these on.

Kind regards, Cheryl

147



Interactio

n


been made in

response to the

interaction or

comment 7 Pre

Interventi

on

---------

----

4/02/2010 email Ori Gudes GU Dear Cheryl

Thanks for your detailed email (invaluable contribution...), this is the kind of feedback we are expecting to have from the board and EY members, which may improve our future HDSS.

Since, few of the suggestions are also related to other colleagues, with your permission I cc'ed Hoon to this email.

Kindly, view my comments highlighted in red:

148



Interactio

n


been made in

response to the

interaction or

comment 8 Pre

Interventi

on

---------

----


Wardrope

EYI Hi Ori and Elizabeth,

This is the information that I was hoping may be available (just to save me some time):

Ori,

I have had another look at your framework and you may or may not have any of this information accessible or easily accessible:

• Paul mentioned that you may have information on avoidable hospitalisations for the area that I

may be able to access. - We are expecting to have an access to the 'avoidable hospitalisations' data through QH application, which is currently under process.

•

• I am wanting to include information on rates of disability. - Hoon, is that part of the QH application? Alternatively, we may include the disability data from the LBHC survey findings?

•

• I haven't included transport, is there any information that can be easily taken from your system? Or are there areas that clearly lack transport options. - Good one (-; our dataset encompasses /

includes few layers of public transportation, such as: bus stations, rail train, rail train stations, bus

routes and all the attributes data which is associated to this topic. • Public housing - is this % of public housing in areas? No, (and I did not noticed any % there?), in

fact these layers represent the scale of prices of 1-4 bedrooms public housing in our AOI.

One of the populations that the EYI needs to engage with is the Cultural and Linguistically diverse population

• I have included % born overseas in my scan but am not sure if you have additional data that may

be useful. Yes, we have the following layers in the system: Multicultural (Clustered Nationalities according to ABS, for instance Africa, Middle Easterian etc;), Indigenous groups (Aboriginal, TS,

both etc;), Nationalities (not clustered) layers based on every SLA in our AOI.

• Would you have anything specific on refugee populations? Yes, we have (to some extent) like Sudanese etc; Yet, specific request may lead to further investigation and implantation of new layers,

in this regard.

• is there specific information for the CALD population on income, education, internet access, etc? Hoon, could you refer to this one?

Elizabeth,

You mentioned in an earlier e-mail that Hoon has some family data from the Logan survey and the Environments for healthy living survey and you could make summaries of this data available. Is it

possible to access this? Hoon?

I appreciate any assistance you can give me.

The 2009 AEDI community profiles will be available in March and I will pass these on. Could you outline, to what geographical entities it will be associated (e.g. SLAs, LGAs, Suburbs, Postcodes

etc;)?

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Interactio

n


been made in

response to the

interaction or

comment 9 Pre

Interventi

on

---------

----

4/02/2010 email Ori Gudes GU I hope, I have addressed most of the points?

Regards Ori

10 Pre Interventi

on

-------------

26/03/2010 Teleconference call

Ori Gudes, Malcolm

Wolski, Ian

Miller

GU, SV Teleconference with Ian Decision has been made to conduct a

webinar with SV

11 Pre Interventi

on

-------------

4/04/2010 Webinar Ori Gudes, Malcolm

Wolski, Natalie

Kent, Naomi Sunderland, Jon

Shuker , Ian

Miller, Ishara.Kotiah

GU, SV Wemminar with Spatial Vision Suggestion has been made to scope the

DSS project with

SV

12 Interventi

on

Introdu

ction

8/04/2010 LBHC board

meeting

LBHC, Ori

Gudes

GU, LBHC

board

HDSS presentation (introduction of the system vision) The board

recognised its potential added

values, and

accordingly approved continuing

with the project

13 Post Interventi

on

Interaction

21/04/2010 Meeting Ori Gudes, Natalie Kent,

Narelle Mullan,

Naomi Sunderland

GU, WA, LBHC

Meeting with West Australia department of health Decision has been made, to maintain

the dialogue

14 Post

Interventi

on

Interact

ion

23/04/2010 Meeting Ori Gudes,

Natalie Kent,

Naomi Sunderland,

Malcolm

Wolski

GU DTS meeting

15 Post

Interventi

on

Interact

ion


Naomi

Sunderland, Malcolm

Wolski, Naveed

Khan, Jens

GU IP and commercialisation aspects Introduction

meeting

150



Interactio

n


been made in

response to the

interaction or

comment Tampe,

Elizabeth

Kendall

16 Post

Interventi

on


Malcolm

Wolski

GU, ESRI ESRI Meeting Information for

quote has been

collected

17 Post Interventi

on

Interaction

12/05/2010 email Ori Gudes GU Dear Colleagues

As part of our HDSS development process we have consulted with our potential end-users (LBHC board members and EY) in regard to the features they are expecting to have in the interface.

Accordingly, they have been asked to address the following question:

What system‘s features do you need to inform your decisions/ actions in this area? (for instance,

where is the closest GP location, hospital location etc ;).

Surprisingly, we have observed the following comments (concise).

Participants tend to associate their information needs to this question. For example, they have indicated what sort of information components would be beneficial for their day-to-day use.

Participants tend to say everything needs to be developed in the system (in terms of features).

Participants indicated that they would like to view other examples of evidence-based health

solutions.

Participants indicated that hands-on demonstrations are necessary, especially when the HDSS tool is

more developed.

Given that, I interpret that either the question was not clear enough or perhaps end-users interpret

features as information components rather than a technical component of a system (very interesting, indeed...). Alternatively, perhaps I should discuss this matter in the next GIS board meeting.

Subsequently, I have created a file consisting of all suggested features that seem (based on my view

and some of the given feedback) to be relevant for the HDSS interface. So, it also may be very

useful for the scoping phase with Spatial Vision

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Interactio

n


been made in

response to the

interaction or

comment 18 Post

Interventi

on

Interact

ion


Kendall

GU Hi Ori

I think this is a matter of not knowing what they don't know really - they probably need to see some

examples operating to then say "oh, I need it to do .......". I bet if you put a prototype together and then asked the same question, you would get lots of ideas. It is a bit like asking about service

delivery - people never really know what to say, but they can always critique what they have at the

moment. It may be a matter of trying to extract the principles – i.e., think of a software program you currently use in your work, what are the features that annoy you most, make life easiest for you etc.

See you

19 Post

Intervention

Interact

ion


Naomi Sunderland,

Malcolm

Wolski, Naveed Khan, Jens

Tampe, Elizabeth

Kendall

GU Decision-making meeting Decision has been

made to move into the scoping phase

and conduct a

teleconference call with WA

20 Post

Intervention

Interact

ion

11/06/2010 Teleconferenc

e call

Ori Gudes,

Naomi Sunderland,

Malcolm

Wolski, Naveed Khan, Elizabeth

Kendall, Natalie

Kent, Narelle Mullan

GU, LBHC,

WA

Introduction with WA and Health Tracks More information

has been provided

152



Interactio

n


been made in

response to the

interaction or

comment 21 Post

Interventi

on

Interact

ion


Naomi

Sunderland, Malcolm

Wolski, Naveed

Khan, Elizabeth Kendall, Natalie

Kent, Debbie

Cowan,Steven Keks, Jon

Shuker, Ben

Simpson, Jens Tampe, Ian

Miller,Ishara

Kotiah

GU, LBHC,

SV

Scoping meeting Introduction

(inc. project scope, plan and outputs) Identification of intended HDSS user groups and their

requirements of the system in terms of functionality, data and security Health Tracks Gap Analysis – a review of the capabilities of the CRC-SI/WA DoH application to

identify which requirements identified in item 2 it currently supports and which it doesn‘t

Griffith Uni GIS and IT Infrastructure – review the current IT (hardware, software and network) and GIS data (types and formats)

Administration Requirements: Discuss how the HDSS would be administered and supported

including the IT infrastructure, data and application, as well as training requirements IP/Potential Commercialisation

Summary and Roadmap

Discussion and

scoping of the

project with the external HDSS

board members

22 Post Interventi

on

Interaction

9/07/2010 Meeting Ori Gudes, Malcolm

Wolski, Naveed

Khan, Elizabeth Kendall, Jens

Tampe

GU Consultation meeting Option 1 was recommended by

GE (Griffith

Enterprise), Starting with Health Tracks

23 Post Interventi

on

Interaction

28/07/2010 Meeting Ori Gudes, Naomi

Sunderland,

Malcolm Wolski, Naveed

Khan, Elizabeth

Kendall, Natalie Kent, Steven

Keks,

GU, LBHC Consultation meeting Discussion and decision on the

project with the

external HDSS board members,

LBHC approved to

go with WA

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Interactio

n


been made in

response to the

interaction or

comment 24 Post

Interventi

on

Interact

ion

2/08/2010 email Ori Gudes,

Natalie Kent

GU, LBHC Hey Ori,

Congratulations on a very productive meeting last week. How exciting to have a prototype so soon!

Let me know if there's anything I can do to help.

Have you heard of the GIS system used by Queensland Transport called Luptai? Thought you might

want to look into it and how it's used.

Catch you soon

Nat

25 Post

Intervention

Interact

ion

2/08/2010 email Sarah Gruber,

Ori Gudes

GU, LBHC Hi Ori,

I hope you had a good weekend. I was wondering if it were possible to send me any data about were patients with type 2 diabetes are living in the Logan Beaudesert Area? I‘m putting together a

business case at present and it would help me pinpoint areas where there are high concentrations of

diabetics, as I‘m proposing that we extend our level of dietician clinics in the area.

Hope this makes sense,

Sarah

Dietician & Primary Care Liaison Officer

P- 3290 3733 E- [email protected] W- www.sphn.org.au A- Wembley Place, 91 Wembley Road, PO Box 6008, Logan Central 4114, Qld

Southeast Primary HealthCare

154



Interactio

n


been made in

response to the

interaction or

comment 26 Post

Interventi

on

Interact

ion

3/08/2010 email Sarah Gruber,

Ori Gudes,

Elizabeth Kendall

GU, LBHC Dear Elizabeth

This is actually a great example of how GIS and spatial awareness has been evolved within the LBHC.

Specifically, I reckon this is great lead for our data set collection efforts, yet it is confidential data, so I am advising

you before addressing her question.

Regards Ori

27 Post

Intervention

Interact

ion


e call

Naveed Khan,

Elizabeth Kendall, Jens

Tampe, James

Semmens, Narelle Mullan

GU, WA Consultation with WA I was not attending

in this meeting, but it seems that there

were discussion

about future collaboration with

WA and CRC-SI

(and expected to

attend in this

meeting...)

155



Interactio

n


been made in

response to the

interaction or

comment 28 Post

Interventi

on

Interact

ion


Kendall, Ori

Gudes

GU Hi Ori

I have very good news! Following on from my discussions with James Semmens in WA, we can

now access Health Tracks immediately to put in your data framework and get started. We can then put our funds to improving the system for the future with no limits on our usage of the data. Naveed

is finalising the confidentiality agreement and you can start talking to Narelle immediately. We are

planning a trip for you, Malcolm and Naveed to Perth to work more closely with them in future. Things worked out perfectly! Thus your thesis will focus on health tracks as an interface for your

data frame.

E

29 Post Interventi

on

Interaction

3/08/2010 email Elizabeth Kendall, Ori

Gudes

GU Thanks for updating me, I have documented yesterday's HDSS meeting and both emails (in my LOG book). Practically, if we would have immediately access to their code, then we also need to

tell Spatial-Vision (so they can update their quote) and this is indeed very good news (-:.

Given our last HDSS meeting decision, I suggest we should arrange another external HDSS

meeting, so we can have the consent from the whole HDSS forum.

Regards Ori

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Interactio

n


been made in

response to the

interaction or

comment 30 Post

Interventi

on

Interact

ion


Kendall, Ori

Gudes

GU Yes, I am letting them know now. I will check with Naveed about whether or not we should advise

Spatial Vision at this stage. If so, you could have this conversation with them. Naveed/Jens, could

you please confirm what we should say to Spatial Vision? Regards

E

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n


been made in

response to the

interaction or

comment 31 Post

Interventi

on

Interact

ion


Naomi

Sunderland, Malcolm

Wolski, Naveed


Kent, Debbie

Cowan,Steven Keks, Ben

Simpson, Jens

Tampe,

GU, LBHC Hi Natalie, Debbie, Steve and Michael

Following our last meeting, I have had several meetings with the leaders of the work in WA that resembles our work. They have designed the basic Health Tracks system that you saw during one of

the presentations. We have negotiated an extremely good deal with them:

1. free access to the existing health tracks system now so Ori can insert our data framework and we

can get started immediately

2. an agreeable IP arrangement so we have complete rights to use the system how we like 3. agreement that any funds we contribute to the project will be focused on our identified outcomes

4. the opportunity to use our funds to improve the base Health Tracks system so it meets our basic

needs and beyond 5. engagement in a broader national roll-out of a population health GIS system

This way, our funds will go much further, we can start immediately and we make sure we are part of any bigger progress in this area.

I hope that you agree with this move given that we have resolved the major concerns which were timing and ability to have free use of the tool. Please let me know what you think. As soon as we

have agreement from our partners, we can have access to Health Tracks to start importing our data.

If we go ahead, Naveed and Jens are going to continue organising our contractual arrangements on

our behalf. We are also hoping to send some bodies over to WA to see the system in operation - we

thought this group should include Ori (technical system details), Naveed (contractual stuff), Malcolm Wolski (security/privacy and hosting concerns). If any of you are keen to also be part of

this visit, let me know.

If needed, we can call another steering group meeting (or phone link up) to discuss this development

further, but Naveed and Jens think there are no concerns.

I look forward to hearing from you.

Kind regards

Elizabeth Malcolm, not sure if you can travel, but it would be great if you were there to talk to their privacy expert.

This is the best outcome that we could have hoped for.

Explanation for this

decision

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been made in

response to the

interaction or

comment 32 Post

Interventi

on

Interact

ion


Malcolm


Kendall, Jens

Tampe, Ian Miller

GU, SV Dear Ian

As you know, we had decided as a group to go ahead and start a new system from scratch. However, we had a very productive discussion with WA CRC yesterday, which has led to an agreement that

we can use Health Tracks as our base. No agreements or contracts have been signed yet, but things

are looking very positive. We are just waiting for confirmation from our partners here in Qld that they are happy to take this direction. The implication of this is that we will be able to proceed in the

following steps:

1. Import our data into Health Tracks - we will need to understand the procedure for making this

happen

2. Test this version of Health Tracks with our local population to gain clarity about the improvements required

3. Make significant improvements to this system rather than starting from scratch

The arrangement with WA allows us free use of the new system. Given that we can start

immediately, this is clearly the best option for us and it means that we are all in a better position at

the end of our project.

I hope this arrangement makes sense to you. Can you please let us know how you want to proceed

from here - it seems as though there might be some early work associated with adapting the Health Tracks to suit our data and then later work building onto the program - there may be things we have

already identified in our scoping that can be costed now, but there may also be other changes as we

roll out Health Tracks as a pilot in Qld. Thus, it may be possible to continue with a quote based on the first round of changes we require or you may prefer to wait until we have clarity about all the

changes that are required.

If you want to have a chat about this, please let me know and we can talk by phone. If not, just let

me know what you think is the next best step.

This is a great outcome and is what we initially thought was the most sensible way forward.

Kind regards Elizabeth

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comment 33 Post

Interventi

on

Interact

ion


Malcolm


Kendall, Jens

Tampe

GU Dear Elizabeth / Malcolm / Naveed

Re: Thus, it may be possible to continue with a quote based on the first round of changes we require or you may prefer to wait until we have clarity about all the changes that are required.

The way I see this, the changes necessary from Health Tracks to HDSS, would be determined as part of Spatial Vision specific design process (specifications phase). So, just think about it as

another scoping round which is embedded as part of their new quote. I don't see this happens in any

different way, and any delay (at this stage) would be critical for making the end-of the year dead line. Thus, this is not an additional component, as they would have had to run the specifications

phase (no matter what option would have been selected). Also, this is a very common process in

every ICT development process.

Regards Ori

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Kendall, Jens

Tampe, Ian Miller

GU, SV I agree that this is a good outcome for you, although I‘m sure you‘ll forgive me for finding the

timing a bit frustrating, given the effort we‘ve put in over the last week on a proposal.

Regarding how to move forward, I‘d prefer for you to confirm that the partners are happy to take

this approach before we do anything more. I‘m assuming you want our assistance to implement

Health Tracks. If you look at Table 1 in the scoping report, it sets out the various requirements and functions and has a column for Health Tracks – i.e. whether HT already delivers the requirement or

whether additional work is required, and a cost estimate for that work.

For us to produce a proposal on the implementation of Health Tracks, you would need to advise us

of whether you want to:

a. Do the minimum necessary to get HT implemented at GU, without any functional changes, or

b. Whether there are functions which HT does not provide which you would want implemented

in the initial deployment. Key amongst these may be the Login Capability in No 28.

Based on your decision regarding a or b, and if b, then the additional requirements, we would

provide a simple table of tasks and costs for you to consider and agree to, after which we could produce a proposal for the project.

Note also that in order for us to make use of the Health Tracks code for you, we will need something in writing from the CRC-SI authorizing this and clearly setting out any conditions or

requirements they may have regarding this use from a technical perspective (i.e. anything we need

to take into account in the way we use the code).

Regards

Ian Miller

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Tampe

GU Dear All,

I am writing to share some thoughts about the recent developments with CRCSI as well as looking at the email trail over the last few days.

It is certainly very encouraging that CRCSI is flexible in terms of IP sharing arrangement. Now that we have signed the CDA we can openly share further info and discuss the proposed

GeoVisualisation System in much more detail.

However, at this stage we DO NOT have a license to use Health Tracks, i.e. nothing concrete on

paper yet. That very well may depend on the face-to-face meeting between our team and CRCSI

where we all scope the project and negotiate license terms.

From our last conversation with Jeff and Narelle, I know that Elizabeth got the go-ahead for Ori to

start using the Health Tracks. However, it is not clear to me what that means exactly.

Does it mean that we can access Health Tracks for testing purposes with existing data layers to

address the immediate questions of our Coalition partners? Will this also suffice for Ori's thesis? Or, does it mean that we are allowed to take Health Tracks to add further features based on scoping

done by Spatial Vision. Keep in mind if we do this, CRCSI did not have any input in our scoping

study.

I believe it is important to ensure that our expectations and CRCSI's expectations are matching.

My understanding is that us and CRCSI should sit down face-to-face and jointly work out the scope

of the collaborative project. The scope will clearly outline where Health Tracks is at the moment

and how it maybe enhanced (based on specific requirements of both CRCSI and us). This will be a joint exercise where both parties will have input.

Once the scope of the joint collaboration is done, then we all will be in a better position to cost the project, i.e. what features need to be added to Health Tracks to come up with an enhanced system

that serves both our needs as well as CRCSI's. It is at this stage that Spatial Vision (or any other 3rd

party developer) maybe engaged to develop the enhanced system.

Please let me know if my understanding is correct or if I am going off at a tangent.

I would recommend that we hold-off from contacting Spatial Vision any further until we all agree

that we are on the same page.

Regarding the meeting with CRCSI, I am available from the week of 23rd August to fly to Perth.

Feel free to give me call if you have any questions. I'm at Gold Coast all day on Monday 9th August

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Kind regards,

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Tampe

GU Thank for your email,

Given the fact that I was not attending in this meeting, I am not aware explicitly of what was

promised by CRC-SI / WA. However, it is clear from your email that there is a gap between our expectations (which is certainly varies also within our group too) and the CRC-SI. The way I view

this, there are two separate discussions with CRC-SI / WA:

1. Get a license to use Health Tracks (code) and then use this as our base for customising, adapting

into our HDSS (this will decrease Spatial Vision offer and will address our short term needs); and 2. Another discussion which is focused upon future collaboration (long term) with CRC-SI and any

future system to be developed. For instance, HDSS in the national level etc.

I have thoroughly reviewed WA specifications‘ document (which was sent by Narelle few days ago,

see attached for your convenience), I understand the way / process which Health Tracks has been

developed. Clearly, we may need to go through a similar process, which is literally part of the overall development phase (i.e. specific system design) that will be suggested by Spatial Vision.

Having said that, just get an access to Health Tracks is not suffice, because the system is much more

complex than just exporting our layers into a new MXD file and deploy onto the web-based GIS interface. For instance, Spatial Vision will need to re-design, re-structure and re-define our spatial

dataset so it will address our HDSS needs. Thus, it will be very naïve from us to think that if we

have an access to Health Tracks that will meet our requirements. I have been developing GIS systems for almost 10 years and there is no way we can avoid the specific design phase which will

be undertaken by Spatial Vision as part of the overall development process. Practically, it means we

are allowed to take Health Tracks and to add further features based on our scoping process (I don‘t see any problem thereof, even though that CRC-SI did not have any input in our scoping study,

given that the HDSS prototype is for our / LBHC purpose).

However, Naveed, I agree with you that we will be in a better position to cost the project (i.e. what

features need to be added to Health Tracks) to address our HDSS needs and the future collaboration

needs with CRC-SI / WA, therefore it will be worthy to wait with any decision till we have this discussion after visiting Perth.

Re: 23rd (Perth) is fine with me.

Best regards Ori

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Tampe

GU Hi Everyone

this is my understanding:

1. we have opened the way to partner with the CRC on Health Tracks 2. they have given us the right to look more closely at Health Tracks to be sure it suits us - for this

reason, we have signed confidentiality agreements

3. we have the opportunity to import our data into the HT system through whatever means is appropriate (SV seems to be the most appropriate place for this to happen as they have already

quoted on this activity and they understand the HT system). I would not be willing to do anything

further to HT until we are sure we have everything bedded down. 4. we can continue with our local test of the usability etc. of HT once our data has been imported

once we are satisfied that the agreement with the CRC allows us to continue developing and using

the HT model (the meeting with CRC indicated that this would not be an issue, but we have to negotiate the actual agreement).

5. we need to think about partnering with them in a bigger way through their submission for further

funding

We need to confirm with them who is travelling to Perth in the week of 23rd. Malcolm, are you able

to go also?

Naveed, do you have someone who could attend to bookings etc.? Or should I ask Claire to do this?

Regards E

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Tampe, Narelle Mullan

GU, WA Dear Narelle

Not sure that I clearly understand (you saying) "While we can't solve all the requirements" I have just sent this file as part of our collaboration etc; Re: access to HT / IP code, it is clear to me as

someone who has a lot of experience on developing GIS systems in Israel, that this is only one

component (in the overall development process) required to develop an adequate interface, and I have not even mentioned the following steps / components:

• specific design and scoping

• re-configuration / re-constricting the data set, • code developing and customisation of it,

• testing the system

• and training.

Yes, please send the agenda proposal.

Regards Ori

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Tampe, Narelle Mullan

Hi Narelle

Thanks so much for your time on Friday. It was extremely helpful.

Following on from my conversation with you, we wondered if we could have a phone link up with

you and your GIS people before sending Ori and Naveed to WA.

As I mentioned to you, there is just a bit of confusion on our end about exactly what will be required

in order to transfer our data into the Health Track system to support a pilot trial of the system. As I have to find the funds to cover this work, I need to be very clear what we need to budget for.

We have always understood that our data structure is more complicated (more layers) than Health Track currently holds, so we would have to restrict the number of layers we wish to work with. This

is fine for our trial (which, as you know, focuses on useability of GIS data in health coalition

decision-making). Ian also mentioned this to us.

After the trial, we would then work on increasing the capacity of HT (possibly with Ian and Spatial

Vision via your agreement with him). I also understand that there will be a potential for recoding time (if needed) so our data and HT are compatible. It would be very helpful if we could reach some

clarity about what is needed here? Is there anything else your GIS people think might be necessary?

Based on Ori's evaluation, it seems possible that using HT as a base may end up costing quite a bit

and we want to be sure we are doing the right thing for our partners.

Sorry to take more of your time Narelle, but it would be good for us all to be really clear about this

before we move forward. For those of us who are not so savvy with programming terminology, it

would be really helpful to have a discussion with the GIS people.

We could meet after 4 pm (Qld Time) Tuesday-Friday. Hopefully this time might suit most people.

Please let me know if any day this week suits you? If everything is sorted out, Ori and Naveed could still travel to WA on 23rd with the partner's approval.

Look forward to talking to you more soon.

Regards

Elizabeth

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Kendall, Ben

Simpson, Jens Tampe,

GU Jens wrote

Dear all,

I would like to learn what the benefit will be, if we would do a deal with the CRC and could use the

Health tracks HDSS system. I also would like to understand the similarities and differences between our system and the WA system, on each level: data, GIS, HDSS.

What are the differences between the Griffith data set and the WA data set (not relating to content, but to data format, structure etc.)?

Do we have to change the data structure to use the WA HDSS system? If yes, how long will this take and who will do this?

What are the differences between the Griffith GIS system and WA GIS system (not relating to content; are they similar, can data be swapped between them, or not)?

If not, can we (Ori) adopt our GIS system and how long will this take?

Does the WA HDSS system work on top of Griffith GIS system? If not, why and what has to be

changed? How long will this take and who will do this? If yes, how many questions can be asked with it and which questions?

I think we should contemplate a deal with the CRC only if there is a value preposition.

Best regards

Jens

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GU Dear All

I think the questions you have all highlighted / raised are ‗right on the spot‘, I will try to address

them based on the actual knowledge I have on both systems. Obviously, some of my answers will

be ‗new questions‘ or points for further discussion. Thus, it seems that another discussion with WA including all Griffith group members is unavoidable.

First, I would like to refer to ―one point‖ which has been emphasised in one of our last

correspondences "what will be required in order to transfer our data into the Health Track system to support a pilot trial of the system", this note describes a different approach to what I have initially

thought / expected. To the best of my knowledge, I was certain we are going to utilise their software

code for developing our HDSS by Spatial Vision. Practically, I am not clear / sure (yet) if this is doable, and if it is? It may be limited to only few GIS layers. The reason for this is that a lot of data

re-configuration and data re-formatting work needs to be done (normally) prior to developing

effective web-based GIS interfaces. The result (in this scenario) could be a very slim interface with limited data and functionality which is unlikely to address our prototype needs (particularly in terms

of usability manners). Regardless to what I have said, we may need to obtain more information

about the ―transfer our data into the Health Track system to support a pilot‖ approach / option in order to thoroughly understand the applicability of this solution. As for the technical aspects, it is

pretty challenging to address all these very important questions in an adequate and effective manner

(by email). But to make the ―long story short,‖ I have attached the following table (I hope it will make some sense?:-):

Question / point HDSS HT comments GIS layers More than 100 GIS layers based on range of sources 10-20 GIS layers not including their

Epi-Reporting tool

Do we have to change the data structure? Yes, we will have to do this, so our dataset will be re-

structured in the most effective way for deployment stage. For example, (and to simply illustrate

this point), let's say we have many GIS layers presetting different attributes of our 31 SLAs in our area of interest (e.g. SIEFA index, Education, Crime, Diabetes type 2 etc;) to manage the dataset

more effectively, these GIS layers should be converted into one huge table in SQL server (dataset)

identified by one unique field (i.e. SLA name or SLA code) for future data retrieval quires by end-users. Yes, they have re-structured their data to SQL server database prior to interface deployment

stage. Re: for who will do this, it is part of the overall development process which will be quoted

and processed by Spatial-Vision, if you look in the specific scoping paper which was prepared by

them

(SV2649_Software_Architecture_Description_v2 document in page 17, attached) you can view this

clearly. In this regard, the specific scoping document is part of the overall development too (stage 1 from 5) and literally it outlines the system ―road map‖ or ―the system story‖ with much more detail.

What are the differences between the Griffith GIS system and WA GIS system (not relating to

content; are they similar, can data be swapped between them, or not)? This is one of the key questions to be further discussed with them. Given that they have already undertaken the full

process and re-structured their dataset (in the background) it is not clear to me whether this is

doable, and if yes? To what extent? In terms of infrastructure, Malcolm, I think it would be easier as we can always add missing components for our architecture. For instance, we have license of

ArcGIS server, but it is likely that we might need to add SQL server too. If not, can we (Ori) adopt our GIS system and how long will this take? Jens, Not sure if I clearly

understood this question? But, if you are referring to develop from scratch that will take 3-4 months,

subject to many aspects. Yet, it is likely, that if we use HT code it will be decrease the time necessary for the overall development.

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GU • Thanks Ori, I will try to digest all this during the week.

• I was always clear that we were going to use their Health Tracks and put our data into it rather than prepare our own HT system. This is important because we will all be contributing to the same

"national" resource and there will be good version control through the CRC. Other partners from

other states might do the same as us, so we then all benefit from improved functionality of the central HT system. We are part of a national consortium, but are the leaders for Qld and have the

right to undertake consultation in our state.

• I wonder if we could organise a "Meeting Place" meeting so that we can each dial in from where

we are on the day. Are you able to organise that? Otherwise, we might need a teleconference that

dials out to everyone - Naveed, do you have someone who could organise that? Claire is not in now until Thursday and that would be too late.

Regards E

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GU Hello

I've probably missed most of the emails and I am playing catch up. I've now got a PA ruling my diary so I don't sink any further :)

One comment I wanted to make about Health Tracks was not to assume we can use their entire product as it is. In fact it might be quite risky to try to load our data onto their system and then

modify Health Tracks.

I would recommend (if Ori hasn't already done so) doing the requirements specification first (as per

the quote). THEN if Spatial Vision thought it was suitable we could load data into Health Tracks.

We have to be certain that their underlying "engine" (or alternative called the architecture) is compatible with Griffith's long term development plans.

We should rely on Spatial Vision's experience and recommendations here as they would be working for us and not WA Health - after all WA Health would prefer us to use Health Tracks as it is for

obvious reasons ... including emotional ties to their system :)

I suspect we can use the base Health Tracks "engine" or parts of it, but we shouldn't assume we can

nor spend too much time on trying to load data into it unless Spatial Vision recommends it.

That is my 20 cents worth.

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Kendall, Ben

Simpson, Jens Tampe, Narelle

Mulan

GU, WA Discussion with WA Decision was

postponed to

Monday 23/08/2010 (additional

conference meeting

with them)

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Mullan

GU, WA Discussion with WA Not enough details

were provided,

some issues with CRC-SI

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Kendall, Ben


GU Dear Naveed

Following our yesterday's discussion, I think we may need to emphasise few of the following points

to Narelle, ascertaining that Monday's discussion would be productive.

1. They have to outline what it really means "access to HT"? Does it mean access to their code /

software which we will be taken and used on our server, or it means that we shall add our layers to

their software which will be hosted and managed in their own central server? (and if this is the case, what does it mean in terms of data re-configuration and cost required). Obviously, they won't have

all the answers, but they have to present their view in the light of these technical components / costs.

2. They need to think as they are in "our shoes", what would be the necessary process from now onwards? what are the technical components, and who is going to engage with Spatial Vision and

for what tasks? For instance, specific requirements, data reconfiguration, programming and code

customisation, testing and trialling etc. They have to address these topics (from the technical perspective) before we can make informed decision.

3. They may need to gather all their technical persons to this discussion, so we can obtain as much

as information possible.

In sum, I suggest to forward this message to Narrele (beforehand), so they can be ready this time.

Regards Ori

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Kendall, Ben


GU Malcolm wrote:

Hi

Unless I misunderstood Narelle, she said they would not want to host the server/HT version there

(i.e. WA Health) and that we would have to host it (at least as far as WA Health were concerned - the CRC guy didn't say anything and we would need that in writing). I then assumed that if we

hosted it we could do what we liked with it - code and all. However .... we had better check as the

might not give us the source code :)

It did occur to me yesterday that I was still unclear about:

a) the CRC guy said they would employ Spatial Vision to do any development work but he was silent on who actually paid for the work (or did I miss something)

e.g.

will we have to pay for the development work - who would we contract to do the work and under what arrangement e.g. a contract with the CRC who subcontracts the work or do we just give our

specifications to CRC and money changes hands somehow else, or we don't pay for the work - then

what is the catch - nothing is for free. Do we have to contribute to become a member/partner and that is the benefits of the partnership. Also if CRC employ Spatial Vision to do coding would they

do it on our version or a version over at the CRC who would provide us with the new version. That

could add layers to the process but it is still workable. If it costs us $50k to become a partner then you'd have to ask why bother.

b) I still didn't understand the development control process. If the CRC employs spatial vision can we just tell the CRC what we want and they have to do it or do they have some rights over what

development we do - if so, who decides what development we do. That could be ugly.

Again what worried me was that I didn't quite get the relationship between the CRC and WA

Health. Who speaks for who - obviously there is a lot of goodwill with WA Health but do the WA

Health people speak for the CRC.

We'd need to see the answers to the above in writing and be happy with it before we signed any

agreement. It would be good if Narelle could answer the above questions tomorrow.

Malcolm

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GU

Thanks Malcolm Yes, that is what I heard Narelle say also - it seems that we will work separately, but there will be

one version of HT that will be available to all partners (not sure who will manage version control). I

agree with all your concerns about how and who controls what is happening - it seems as though WA health is a strong financial partner of CRC and Narelle is on the governing board. We have

been offered a place on the board also. My dealings have been full of goodwill with James and

Narelle, but not sure if they have the capacity to turn that into action via Mike and the CRC. As you say, all steps need to be stated in writing from the CRC.

Thank you for spending the time on Monday to sort through this with Narelle.

I look forward to hearing your views. Regards

E

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Mulan

GU, WA Dear Narelle,

Thank you very much for the meeting on Friday and for providing us the opportunity to ask

questions. Our understanding is that WA Health is ok for us to obtain access to HT. Our understanding is also

that this would allow us to contribute to development of the next generation of the software.

One of our key requirements is to use HT for early testing and to pilot with our partners. We are looking to do this in a short timeframe (over the next 6 months)

We discussed a number of points in our meeting last week. I have listed them below as we hope to

clarify them further today: • How do you see the pilot fit into a long-term development plan for the next generation of HT.

• Is the existing HT software amenable to adaptation onto our framework, data layers, GIS

architecture and server? • Would this require additional work (e.g. data reconfiguration, re-programming etc.). How

will the scope of this work be determined? Would Griffith have control over what we want in the

pilot? • If additional work is required to deploy HT as a pilot, can this be done by a GIS expert (e.g.

Ori) or would this require Spatial Vision? What might be the potential cost of this work?

• What would be the development control process for the pilot – i.e. would CRCSI be the lead party who will contract with Spatial Vision? How will this impact on the control we have about the

software development?

• If other Councils, agencies want to access HT to deploy their data (which might be in a different format, structure), would they have to go through the same process as we do? Is Spatial

Vision always required to do the work? Is there any part of the software development process that

can be automated and does not require Spatial Vision services (to reduce cost).

I hope that in the meeting today we can get better understanding about some of the technical issues between our GIS experts.

Look forward to talking to you.

Kind regards,

Naveed

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GU Decision has been

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from SV

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Kendall

GU Hi

I had a chat with Ian from Spatial Vision about how to proceed with a requirements specification that would help us understand the overlap with Health Tracks but ensure we develop a system that

meets our (i.e. our partner's) needs.

He is going to have a chat with his technical person but he agreed there is a risk that if we came at it

using the Health Tracks system we may not end up with the system that meets our needs both in the

architecture or the interface (keeping in mind the three areas we wanted to focus on such as Healthy Cities, Child Friendly Cities plus one other). He also said that there were a couple of areas in

Griffith's system which needed fully fleshing out to see how it would work, what was required etc.

He thinks they may be able to do our specification from scratch and keep referring to the Health

Tracks specification for each function to see how much they could use from that system. He has

gone away to talk to the technical person (I can't remember her name) and come back with a more detailed proposal for the specification process (which is $10k in the quote).

He said he'd send something in the next day or so.

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Kendall

GU Thanks Malcolm

As usual, you are very helpful. I have asked Scott if we could have a chat to his GIS programming contact in Beijing. I think this conversation is an important one so we can be sure whether or not

Spatial Vision/Health Tracks can provide us with the solutions we need for our partners and our

own sustainability/future growth in this area. There may be other options/directions for us take that we should explore.

If you all go ahead and have a meeting before I get back, could you please invite Scott also?

Thanks

E

Decision has been

made to request SV

to provide a quote for the

specifications report

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Kendall

GU Dear all

Thanks for that Malcolm, I have thoroughly reviewed their quotation, please find my comments: 1. I think they need to clarify clause 6.3 before we sign this document; and

2. They have motioned the three case scenarios (Healthy Cities, Healthy child, and another case which was not specified in the scoping day). Given the limited time of these meetings and the level

of attendance, I am not confident we can determine in 1-2 hrs what data items could be incorporated

in each scenario. Alternatively, and this is just a suggestion / thought, we could disseminate beforehand a form that would ask our target group members (LBHC board) to rate the different data

items to be included in phase 1. For example, tick 1 for (yes, this group of layers is "must to have"),

tick 2 (this group of layers could be included in phase 2), tick 3(this group of layers is not necessary at all). This way, board members could have the opportunity to get a better look into the data which

is available for them, and accordingly, give feedback and rate these items which are the most

important for their day-to-day work. I can prepare this form to be included in our next LBHC board meeting on the 9th.

Kindly let me know if you think this is useful?

Suggestion has been

made, to create the

information items survey,

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Kendall

GU Hi All

I would agree with Ori regarding the scenarios - the discussion at the meeting was only tentative and suggested by Ian rather than by our partners. I think the difficulty with the scenarios is that they will

still require broad range of datasets, so we simply need a system that can manage a diverse dataset.

E

Elizabeth has noted

that the scenarios suggested in the

scoping report are

too broad

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Ori Gudes,


Khan, Elizabeth

Kendall, Ben Simpson, Jens

Tampe

GU Decision-making meeting Decision has been

made to go solely with Spatial vision,

to conduct the

specifications and design phase

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Elizabeth

Kendall, Debbie Cowan, Leonie

Roney

GU, LBHC Hi Debbie

Can Ori have a short session on Thursday to get some rankings of information types from board

members (i.e.., what they really want to have access to in the first prototype of the HDSS). This will help us to refine the framework for the development of the interface.

They will be given the list of items groups (about 15) and will be asked to nominate how critical

each cluster is to their decision-making. It shouldn't take too long and could be combined with an update about the HDSS?

Thanks

E

Elizabeth has

suggested to include

the information items survey in the

LBHC board agenda

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Elizabeth Kendall, Debbie

Cowan, Leonie

Roney and all LBHC board

members

GU, Hi All

As discussed in the Board meeting today please find attached the HDSS survey requiring your response.

The survey is to assist Ori in identifying the relevance/ urgency of including particular types of information in the Health Decision Support System i.e. what you really want to have access to in the

first prototype of the HDSS.

The information you provide will assist Ori to refine the framework for the development of the

interface.

Can you please complete the form and send to Ori via e-mail [email protected] by COB

Thursday, 23rd September.

Thanks

Leonie

The information

items survey has been disseminated

within the LBHC

board

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Elizabeth

Kendall, Malcolm

Wolski, Naveed

Khan

GU Hi Naveed

I have just talked with Scott about our next step - he has a colleague in Uni of Newcastle who has created interactive websites for GIS use and currently has a programmer working with him. Scott is

going to ring him for a chat.

I will send you all an update when Scott has any information

Regards E

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15/09/2010 email Ori Gudes, Elizabeth

Kendall,


Khan

GU Hi Naveed

I have just talked with Scott about our next step - he has a colleague in Uni of Newcastle who has

created interactive websites for GIS use and currently has a programmer working with him. Scott is going to ring him for a chat.

I will send you all an update when Scott has any information

Regards

E

More options for development phase

have been suggested

by Elizabeth

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Elizabeth

Kendall, Malcolm

Wolski, Naveed

Khan

GU Dear Colleagues

The more options we have in development phase, the merrier, particularly in terms of flexibility,

rates etc;

However, last week we have decided to go into the specifications phase which is earlier to the

development phase, this needs to be done, (regardless and conditionless to any future decisions of

who will literally develop the interface. Actually, it positions us in a very strong point where we have a lot of knowledge on what we want to achieve and how it should be developed in terms of

architecture, data, infrastructure and design etc; Also, this still gives an option to collaborate with

WA/ CRC-SI if we are convinced it is beneficial for us.

Regards Ori

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Elizabeth

Kendall,


Khan

GU Hi

Ori is correct in that normally we would give the specification document to the programmer to

build. The main benefit of using Spatial Vision for this specification task is that they have already built a similar site so hopefully we would get a better specification (taking the learning from the first

site). Of course his might limit their creative thinking :). From other industry work for this task I

wouldn't consider their prices too high.

Scott - it would be useful to know what other skills the programmer at Newcastle e.g. experience &

analyst skills or is he/she just a coder. Is it the sort of person who is capable of programming from a specification or a junior. Malcolm

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Elizabeth

Kendall, Malcolm

Wolski, Naveed

Khan

GU Dear Elizabeth,

Do we wait to hear from Scott's contact first or shall we go ahead and ask Spatial Vision to start making arrangements to come here for the specification phase?

I'm mindful of the logistics involved in organising an appropriate time for consultations meetings with Spatial Vision. And it seems that we can start the specification phase whilst we continue to

explore other options for further development phase.

I'm happy to contact Spatial Vision and forward the contract related paperwork.

Please let me know.

Kind regards, Naveed

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15/09/2010 email Ori Gudes, Elizabeth

Kendall,


Khan

GU Hi Naveed Ori mentioned that Ian at SV needs official approval to go ahead with the next step. Are you OK to

give him that approval? We said you would be the link for us, so should probably stick to that.

Regards E

Decision has been made, Elizabeth has

officially approved

to approach SV and proceed with

Specifications phase

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Elizabeth

Kendall, Malcolm

Wolski, Naveed

Khan, Ian Miller, Ishara

Kotiah

GU, SV

Dear Ian and Ishara,

I understand that Spatial Vision provided a Proposal (attached above) outlining the Specification

phase of HDSS. This was sent to Malcolm on 31st Aug 2010.

My understanding is that the 'Specification Report' will help to:

1) build an application from scratch based on our requirements, and 2) compare its functionality against Health Tracks application

My understanding is that this Specification quote excludes adoption of Health Tracks as a complete code base for development of HDSS application.

The University approves the proposal. Please find attached the Services Agreement for your review.

Could you please fill in empty fields (e.g. Project commencement date, completion date) and if all is

ok, send back two signed copies (via mail) to the address below.

Naveed I. Khan, PhD

Business Development Associate - Health Griffith Enterprise

Bray Centre (N54), Rm 1.06

Nathan Campus, Griffith University Brisbane, QLD 4111, Australia

I will organise counter signatures and forward one copy back to Spatial Vision.

Please feel free to contact me if you have any questions.

Kind regards, Naveed

Official engagement

with SV regarding

specifications phase

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Elizabeth

Kendall, Malcolm

Wolski, Naveed


Kotiah

GU, SV Dear Ian,

We can confirm the Wed 13th Oct as project commencement date and associated timeline.

I look forward to the Services Agreement.

Ori will be in touch with you draft agenda for the meeting.

Kind regards,

Decision has been

made regarding the

day of specifications meeting to be held

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Elizabeth

Kendall, Malcolm

Wolski, Naveed


Kotiah

GU, SV Ori,

Yes, the consultation is set for the 13th, 10:00 AM to 4:00 PM.

Note that I am very concerned about your statement that none of the

scenarios covered in the scoping document are relevant any more. This next stage of detailed specification is based on the scoping document

and we will not be re-scoping the application as part of this process.

We need to get down to details immediately and we cannot afford to spend the day back at the start, trying to work out what this application is

about.

By the end of the week, we will send up a more detailed agenda for the

consultation day, including a form for participants to specify in more

detail the scenarios, including the map data layers they believe are required to demonstrate their scenarios. Prior to the 13th, you will

need to ensure that if the originally identified scenarios are no longer

relevant, those three scenarios are selected and their details documented.

Regards

Ian Miller

Ian has noted his

concerns about the

scenarios of the HDSS specifications

phase

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Elizabeth

Kendall, Malcolm

Wolski, Naveed


Kotiah

GU, SV Dear Ian

Both sides agree that the purpose of the specifications' stage is to move into details, which is vital for developing the HDSS prototype. No re-scoping is required and all the HDSS stakeholders

understand its purpose. However, both I and Elizabeth have noted that the scenarios which have

motioned are too broad. For instance, the Healthy Cities scenario (according to the literature) could contain the whole layers in our framework. Yet, we did not specify these scenarios in the scoping

day (in terms of what data each scenario contains), so that is changeable. As a result, and given that

it may be impossible to include all layers in the HDSS prototype, we have recognised that a subset of layers must be selected from the overall available dataset. Consequently, we have disseminated

the enclosed HDSS Information items form (2 weeks ago). Thus, our future HDSS end-users (i.e.

LBHC board members) have prioritised their informational needs, based on their own preferences and day-to-day requirements. Therefore, now we have a great and updated feedback from the

LBHC. This, in turn, help us narrowing-down and deciding what information items and layers

should be included in the HDSS prototype.

In sum, to my view, it does not really matter if you wrap this under this term or another (e.g.

Healthy Cities scenario or Chronic Diseases scenario etc), the most important thing is that these scenarios are addressing our end-users needs and providing their required evidence (much

specified). Overall, I believe we have managed to collect new feedback from our LBHC end-users

which will be very helpful, beneficial and useful for our specifications phase.

I hope that this time I have managed to explain my self clearly?

Best regards and many thanks for your feedback Ori

P.S

Ian, kindly let me know if you prefer to talk about it by phone?, as emails are (sometimes)

interpreted Incorrectly.

Ori has suggested

practical solution

for addressing Ian concerns

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Elizabeth

Kendall, Malcolm

Wolski, Naveed


Kotiah

GU, SV Ori,

Please find attached an agenda for our visit next Wednesday 13th.

Also attached is a questionnaire which aims to gather more detailed information about the three

specific scenarios to be supported by the prototype. This document includes a worked example to show the level of detail we are after and uses your data breakdown as the basis of the suggested data

question. We have allowed time to work through these scenarios on the day but it is preferable if

you can have filled out most or all of the details for three scenarios.

I understand that you believe the two scenarios documented in the scoping report are no longer

appropriate. If this is the case, it is critical that you decide on the appropriate scenarios and complete the details required before the consultation day, as we cannot afford the time to revisit the

selection of scenarios on the day – we need to get down to details of what is required in the

prototype to support them.

If you have any questions, please get back to Ishara or myself.

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Elizabeth

Kendall

GU Dear Elizabeth

Please view Ian's email, "I had to get out of my PhD cave" ((-; for this.

Also, I am attaching the preliminary findings from the information items questionnaire. At this

stage, I think Griffith University see this differently from Spatial-Vision. So, before I am addressing

his email, I thought it would be a better idea if I ask your advice. Literally, what I suggest is to include the layers which have been selected in the information items questionnaire, as it reflects

LBHC board members views. However, in case Ian states that this is still too much for the HDSS

prototype, we may decide to pick a subset of layers from this reduced package, by discussing this on the specifications day. Generally, the scenarios approach is very good approach (and I support this),

I just don't see this happen in the required thoroughness in this meetings. For this reason, I have

disseminated the Information items questionnaire within the LBHC board members.

Thanks Ori

Ori and Elizabeth,

has suggested to

conduct a teleconference with

Ian (regarding the

scenarios)

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e call

Ori Gudes,

Elizabeth

Kendall, Ian Miller, Ishara

Kotiah

GU, SV It was endorsed to

prepare a list of

workflows for the HDSS meeting on

the 13th, in addition

to including maximum GIS

layers (based on the

findings from the information items

survey) for

addressing different needs in the LBHC

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Elizabeth

Kendall

GU Dear Elizabeth

Thanks for the conference telephone call today, I think it was important for bother sides.

Kindly view their example, I shall create a similar list of proposed critical work flows (like this one)

and we may amend it on Tuesday? In addition, we make sure we have max GIS layers (based on our survey results).

Thus, the hybrid approach may address variety of LBHC needs.

Regards Ori

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Elizabeth

Kendall

GU Elizabeth wrote:

Ori, the data you have clusters into groups:

Population Factors (demographics, SES etc.)

Environmental Factors (built environment/natural environment) Health Services (facilities, programs, services)

Community Services (organisations and community groups/resources)

and Health Outcomes (disease, mortality, health service usage etc.)

I think these clusters form the basis of the scenarios - i.e., predicting Health Outcomes from the other four clusters - different people on the board will be interested in different clusters.

For smart users, we need access to all layers in line with the broad Northridge model so they can map anything they want

Then we need the opportunity for them to select a combination of variables from within each cluster to compare with health outcomes (i.e., health outcomes can be the reference point - i.e., the standard

layers - and then we add their choice of layers)

Finally, we need some pre-established equations/indices/algorithms that allow us to explore

interesting combinations (the focus of these algorithms should be things that give us an analysis of

"access" to something in each level or a multi-variant outcome indicator etc.

Talk to you next week.

E

The primary

outcome of this

correspondence, has supported to finalise

the layers list for the

HDSS as well as points and

principles for

further discussion in the specifications

phase

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Elizabeth

Kendall, Naomi Sunderland,

Debbie Cowan,

Steven Keks

GU, LBHC Dear Debbie, Steve, and Elizabeth

I have been approached by a talented GIS practitioner Joyce Lee who is seeking to do a PhD in the area of GIS for community health and wellbeing. She was directed toward us by Natalie Kent's

current student intern.

I have met with Joyce and Ori has provided her with documentation on the existing HDSS. Ori and I

see a great opportunity to work with Joyce to lead Phase II of the HDSS roll out in the Coalition and

participating organisations and networks. Joyce has spent the past two years teaching planners how to use GIS systems and doing modelling using GIS data. She has an excellent grade point average

(see CV attached).

I have asked Elizabeth if there is any way the Centre can support a PhD scholarship for Joyce that

would provide us with three years full time 'employment' on the project with a PhD resulting at the

end of the term. The total cost of a scholarship of this kind is $30K per year x 3 years total $90K. Elizabeth indicated she could cover $10K per year for three years from existing centre funds. I

wonder if Council and LBHC are interested in discussing a potential three part partnership around

this project/position to cover the remaining $20/yr required. Perhaps we could also ask Virginia if she feels that SRSS would be interested as well. An ideal outcome would be that the PhD student

would spend time in partner organisations across the LBHC and return to Griffith for direct research

tasks and supervision.

This could be a chance to cover off a few things:

• Transition from Ori's PhD on design and pilot implementation of HDSS with Board to broader implementation across the LBHC. A new staff member will allow Ori to focus on writing up and

submitting his PhD thesis in 2011.

• Updating new census and other figures due out next year. • Developing link between HDSS and more public access Digital Town Square (DTS) access to

data.

• Developing ways to collaboratively continue to update local data via DTS from multiple local sources e.g. Ben soc, CfC, Beaucare, etc.

• High quality handover of the HDSS system and supervision from Ori for at least 12 months.

There is no particular start date or due date required for PhD scholarships though we would try to

get her started early in 2011. All signs are excellent that Joyce would make a valuable addition to

the team. We would undertake a formal interview with Joyce prior to offering her the scholarship

though.

Any thoughts you all have on this opportunity would be very welcome.

best wishes

Naomi

Naomi has

suggested new

(GIS) PhD student for implementation

of HDSS phase 2

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Naomi

Sunderland, Malcolm

Wolski, Naveed


Kent, Steven

Keks, Jens Tampe, Ian

Miller,Ishara

Kotiah

GU, LBHC It was decided to

pick 3 workflows

and a reduced amount of layers has

been defined. SV

will send the specifications report

for feedback in two

weeks time

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15/10/2010 email Ori Gudes, Naomi

Sunderland,


Khan, Elizabeth

Kendall, Natalie Kent, Steven


Miller,Ishara

Kotiah

GU, LBHC Dear HDSS colleagues

Thank you for attending in the HDSS specifications meeting last Wednesday, your time, effort and feedback are valuable and greatly appreciated. It was very productive

discussion and we have managed to define the scope of GIS layers, functionality and 3 workflows

(addressing 3 types of health problems) required in the HDSS (phase 1). Next, I shall forward you the HDSS specifications report (draft 1) by early November. Consequently, we would ask your

feedback before wrapping this document (mid November) and moving into development phase.

Best regards Ori

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Naomi Sunderland,

Malcolm


Kendall, Natalie

Kent, Steven Keks, Jens

Tampe, Ian

Miller,Ishara Kotiah

GU, LBHC Dear HDSS colleagues

Kindly find the enclosed HDSS specifications document with my comments and suggestions

(incorporated as track changes), I would greatly appreciate if you can provide some additional

feedback until the 09/11/2010.

Best regards Ori

Request for

feedback (regarding the HDSS

specifications

report) has been sent

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Naomi

Sunderland, Malcolm

Wolski, Naveed


Kent, Steven


Miller,Ishara

Kotiah

GU, LBHC

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9/11/2010 email Ori Gudes, Malcolm

Wolski,

Elizabeth Kendall,

Michael

Asnicar

GU, LBHC Ori,

I agree that the scenario relating to indigenous groups was mentioned last time. The 'proximity

analysis' (s3.3.3) I think is new, so were there going to be 4 scenarios, not 3?

Support other comments.

Re 6.1, I envisage the reference to 105 (as corrected) spatial layers only relates to the pilot (?). I

thought that the earlier HDSS information items form collated by GU following stakeholder feedback suggested at a possible wider scope (which would be further expanded again in future).

Regards

Steve

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Malcolm

Wolski, Elizabeth

Kendall,

Michael Asnicar

GU, LBHC Thanks Steve

Your feedback is important,

1) Yes, we will have now 4 scenarios;

2) Yes, we have almost 200 GIS layers (under the HDSS framework) but we had to reduce the number to address specifications requirements and the scope of the pilot.

However, I am expecting that phase 2 of the HDSS will include more layers, this is subject to further discussion (i.e. HDSS phase 2) early next year- Feb 2011?

Regards Ori

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Wolski,

Elizabeth Kendall,

GU, LBHC Hi Ori Thanks for this document - it looks great. I don't have the expertise to provide comment on all

sections, but have commented where I can. Give me a buzz if you want to talk through any of it.

cheers

Deb

Feedback has include in the

revised

specifications draft

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Malcolm

Wolski, Elizabeth

Kendall,

GU, LBHC Hey Ori,

I have been off work the last few days with the flu so sorry for the delay. Is it too late to submit my

feedback? Will have to look at it this afternoon. Natalie

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Malcolm

Wolski, Elizabeth

Kendall, Debbie

Cowan

GU, LBHC I was hoping you weren't going to ask me that question. I will have to think this through a bit more

and get back to you, but it might be something along the lines of:

'what is the correlation between unemployment and chronic disease by location. or What is the correlation between 'green space' and injury or

walkability factors in relation to chronic disease

I have a meeting with Peter McKeown (Health Promotion) next week, so will get back to you after

that if you can wait that long.

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Malcolm

Wolski, Elizabeth

Kendall, Debbie

Cowan

GU, LBHC That's fine, we always can construct new GIS layers based on different

measurements and update the framework / HDDS accordingly. So, this

dialogue is valuable for the LBHC and the HDSS.

Regards Ori

84 Post

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Malcolm Wolski,

Elizabeth

Kendall, Debbie Cowan, Natalie

Kent

GU, LBHC Sorry Ori, I am still off sick from work. I have had a very quick flick through the spec and for the

sake of moving forward, it looks all good to me. I will have a more detailed look when I am back on deck.

Sorry again for the delay.

Bet you're getting excited now!

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Wolski,


Cowan, Natalie

Kent

GU, LBHC Hi Ishara

Another important comment is, adding a form where end-users can send their comment / feedback

to the HDSS admin person's email (myself at phase 1) regarding data set issues, updates or any other requirements from the HDSS etc

Regards Ori

86 Post Interventi

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Wolski,


Cowan, Natalie

Kent

GU, LBHC Sorry - I've been away and offline using an iPhone for email most of the time.

I had a read through and it all looked okay. I think someone from my team has been talking to

Ishara about some of the IT stuff.

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Malcolm

Wolski, Julian Gibson

GU Hi,

Can we check with the vendor the need for use of the VPN by clients?

Our preference is to use SSL/HTTPS connections and local accounts on the web server.

If VPN is required, can it only be for a sub-set of power users?

We have checked with our networking section and they are happy to open the network ports required by the server.

If any further clarification is required, I am happy for the vendor to contact me.

Thanks

Julian

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Malcolm

Wolski, Julian Gibson

GU Ori,

Ishara is out of the office for a few days on training. I am in a meeting all morning but will be at my

desk this afternoon if you would like to ring and discuss where you‘re at regarding the project.

Regarding Julian‘s query, VPN access is not a requirement of ours. It was suggested by Griffith

Uni staff as potentially being the easiest way to provide external HDSS users with access to the

application, since they were unsure as to the possibility of opening up firewall ports, implementing SSL etc. We were happy to go along with this approach.

We have no problems with direct external access via HTTPS, assuming that GU IT staff take care of

the firewall issues and obtain and deploy the SSL certificate to the IIS web server. There is little difference at an application level in being accessed via a VPN or via HTTPS.

Regards

Ian Miller

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Malcolm

Wolski, Elizabeth

Kendall,

Naveed Khan

GU, SI Dear Ian

Is there a problem with the numbers of table 3?

Regardless of that, kindly exclude section 26 (time slider, -3000$). From the additional table (i.e.

Additional requirements table) please include the following sections: 34 (print jpg +3000$), section 38 (identify +2000$) and section 40. According to our calculation, by reducing the specifications

and deign section (which was already conducted), it makes 40,500$ in total (does that make sense?).

Also, could you revise the table and send a final updated report, so Naveed will be able to process the signatures etc (by the next few days or so?)

Best regards Ori

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Malcolm

Wolski, Elizabeth

Kendall,

Naveed Khan

GU

Hi

I rang Ian to advise him about finance cut-off dates etc. He said it was fine as that is usually what

his Govt clients do also. He'll work it so as not to expect any payments until after mid-January.

He said the way forward would be for us to sign off on the specification document and then attach

that to a contract the same as the one we have already used (from Griffith Enterprise I assume).

When the contract is signed orders raised and work begins.

Malcolm Wolski

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Wolski,

Elizabeth

Kendall,

Naveed Khan

GU Hi Malcolm I have a surplus this year, so we could pay in advance and that would suit me. What do you think?

E

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Malcolm

Wolski, Elizabeth

Kendall,

Naveed Khan

GU Elizabeth

I was thinking if we could turnaround the contract and raise the order this year it basically shows up in the finance system that you have committed the funds (i.e. you are waiting for delivery of the

goods to pay the invoice).

It depends on how your Group works. In INS if we commit funds at the end of the year it is

understood that money is carried forward into the following year to pay for those commitments (i.e.

payment doesn't come out of next year budget :)

Depending on how much you have you could always split the risk - pay half up front (i.e. get them

to invoice you for 50%) from this year's money and half on delivery. You would still raise an order for the total but get them to invoice you for a percentage upfront payment. The unpaid half of the

order is the bit you carry forward as a commitment into 2011 and argue with your Group about

rolling unspent funds over into 2011 to cover the invoice you are expecting in Jan :)

I think Spatial Vision are trustworthy but then an auditor may disagree with us :).

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Malcolm

Wolski, Elizabeth

Kendall,

Naveed Khan

GU Ori,

It may be, if testing goes smoothly and quickly. At this stage however, we are not able to guarantee

this and will need to work to the schedule in the updated document.

If all goes quickly and well, we‘ll be happy to deliver into production ahead of schedule.

Regards

Ian Miller

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Wolski,

Elizabeth Kendall,

Naveed Khan,

Ian Miller

GU, SV Thank you Ian.

Dear Ori and Malcolm, when the final revised document is signed off from your side could you

please forward to me. I will attach the new contract to the document and forward to Spatial Vision.

Thank you.

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Malcolm

Wolski, Elizabeth

Kendall,

Naveed Khan, Ian Miller

GU, SV Ori,

Yes, your figures match my calculations. I‘ll get a revised version of the doc to you by midday

tomorrow.

Regards

Ian Miller

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Malcolm

Wolski, Elizabeth

Kendall,


GU development phase consultation Decision has been

made to continue to

the development phase

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Malcolm

Wolski, Elizabeth

Kendall,


GU Dear Ian,

Thank you for the updated version.

I will be follow up with you next week regarding the contract. In the meantime I will confirm with

everyone that they are happy with the changes and agree to the final draft.

Kind regards,

Dr. Naveed Khan

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Malcolm

Wolski, Elizabeth

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GU Ori

Hope you are well.

On the assumption that we will proceed with the HDSS development, I wish to address a few items:

1. Can you please let me know when you will be on leave? Just so that I am aware of when you

will be available and can plan accordingly.

2. Would you be able to provide all the data as well as an MXD containing the layers you wish

to publish in ArcGIS Server? Please note that the MXD must be set up with a single group layer hierarchy; scale threshold and field aliases and visibility should also be set up in the MXD.

3. I assume that you have created composite layers for Scenario 2 and will send them with the data supply.

4. Can you also please provide a combined facilities layer for Public Hospitals, GP Clinics and Chronic Disease Centres. Please ensure that the name of the facility is stored in one field as the

search function will only search through a single field.

Many thanks,

Ishara Kotiah

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Wolski, Elizabeth

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GU,SV Dear Ishara

Thanks for your email, I know that Naveed has been working on the contract for the development phase, so hopefully he will update us in the next few days (or so).

As for the technical GIS tasks, I have made a list of GIS tasks which need to be completed before the 30/12/2010, the day I am heading overseas (coming back after the 7/2/2011).

However, It would be easier to discuss it on the phone?

Regards O

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3/12/2010 email Ori Gudes, Ian

Miller

GU,SV Ori

As documented in the Final Specification, metadata will be accessible from the Help Screen and

read from an HTML file that you will be able to edit.

Ishara

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Malcolm

Wolski, Elizabeth

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GU Dear Ian,

Please find attached above the draft contract for your review. Could you please confirm the details (start and finish date), sign and send two hard copies back to my office for counter signature.

I will return your copy via post.

Please feel free to contact me if there are any questions.

Regards,

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Malcolm Wolski,

Elizabeth

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Ian Miller

GU, SV Naveed,

Contract looks fine – I have signed and mailed 2 copies to you.

Regards

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Malcolm Wolski,

Elizabeth


Ian Miller

GU Dear Naveed

Thank you for progressing this, I and Ishara will be focused now upon finalising the pre-GIS tasks

before x-mass time,

Best regards Ori

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GU Dear Ian,

Thank you for sending the partially signed contract.

I'm organising signature from the University and will return your fully executed copy to you as soon

as it is ready.

Regards,

Dr. Naveed Khan

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Ishara Kotiah

GU, SV Ori

The combined layer is a merged layer from the GP Clinics, Public Hospitals and ―Chronic Disease

centres‖ into one layer and the name of each in a single field .

Ishara

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Ishara Kotiah

GU, SV Ori

Great to hear almost everything is ready!

Yes, please if you could have two fields in the layer:

1. Name of facility (eg. Logan Hospital, Clinic AAA)

2. Type of facility (eg. Public Hospital, GP Clinic)

Thanks,

Ishara

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Malcolm

Wolski, Natalie Kent, Naomi

Sunderland,

Debbie Cowan

GU,LBHC Dear LBHC members

It gives me a great pleasure to update you, that after more than 2 years of GIS data collection, consultations and collaborations, we are about to commence the development of the HDSS

prototype.

In the last few months we have met several times. These meetings were used to collect important

information, feedback and insights, which, in turn, made this a fruitful, insightful dialogue.

Amongst the discussed topics were GIS information items, functionality and workflows (health scenarios). Subsequently, during this process your feedback has been documented, synthesised and

accordingly incorporated into the HDSS prototype. Thus, it is expected that by early next year

(March 2011) the HDSS prototype will be deployed. Accordingly, I shall coordinate one-on-one sessions with all LBHC board members, to deliver information sessions and basic training (Leonie

will help coordinate these meetings early next year).

I would also like to use this opportunity to thank you for your invaluable effort, support, feedback

and time that you have invested in this long process, and you will all have a part in the future

success of this project.

Happy X-mass

Regards Ori

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Wolski,

Elizabeth Kendall,

Naveed Khan,

Ian Miller

GU,SV Hi Ian

Could you explain us what is the invoice which has been posted to us?

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Malcolm

Wolski, Elizabeth

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GU,SV Ori,

This is the second and final invoice from the specification project, due once the specification was

accepted.

As per the contract signed between us for this work, the total cost was $12,000 plus GST,

comprising two invoices; $10,000 plus GST on delivery of the draft specification document and

$2,000 plus GST on acceptance of the final specification.

Please get back to me if you have any concerns about this.

Regards

Ian Miller

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GU,SV

Dear All,

The price was always 12K. (see page 7 on the pdf and see schedules of the Services Agreement)

Two instalments - 10K and 2K on final report.

I think you guys kept the 10K figure in mind and may have forgotten about the second instalment.

Hope this clarifies.

Regards,

__________

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Malcolm

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Elizabeth


Ian Miller

GU,SV elizabeth kendall ✆

to me, Naveed

show details 13:15 (22 hours ago)

Hi Naveed

Please see below from Ian. Do you recall that we were to pay $12K for the specification - my

understanding was $10K. This means we have already paid $5K, $10K and $2K plus everything else in the invoice. I just need

to budget for the total amount, so if it keeps growing, we won't be able to afford it.

THanks E

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Malcolm

Wolski, Elizabeth

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GU,SV Dear Ishara

Next week is my last working days until 07/02/2011, so please let me know if there are any additional or adjustments requests from your side, which need to be undertaken before x-mass time?

Regardless to that, I have backup the data, so will have an access to the data from overseas too.

Regards Ori

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Malcolm Wolski,

Elizabeth


Ian Miller

GU,SV Ori

I have unpacked the data and now have the following queries:

1. I can‘t find a suburbs layer, which we need to undertake queries and Scenarios 1, 2 & 3.

2. Do you wish for me to use the network geodatabase that you sent previously for the drive time analysis?

3. I recall you saying that you didn‘t use the term ―Chronic Disease Centres‖. Can you please advise what the equivalent layer is?

Otherwise, the data looks good and ready for incorporation into the ArcGIS Server components now.

Best regards, Ishara

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Malcolm

Wolski, Elizabeth

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GU,SV Dear Ishara

These are fantastic news,

1) Yes, use our SLAs layer as an equivalent layer

2) Yes, use my networking model 3) Use, the GPs layer instead (as this is the place where they will initially go before heading to a

more specific clinic etc)

Regards Ori

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Malcolm Wolski,

Elizabeth


Ian Miller

GU,SV Hi All

As you may be aware Ori Gudes is planning to meet with you all individually in March to provide

you with an update and basic training for the HDSS.

I am assisting Ori with coordinating these sessions, and in order to do so I require your meeting

availability for 1-2 hours during 1st - 14th March 2011.

Thanks

Leonie

Leonie Roney

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Naveed Khan, Naomi

Sunderland

GU,SV Dear Colleagues

It is very exciting days, at the moment we are testing the HDSS beta site, It would be very useful if you can deliver some of your own feedback until next Tuesday?

For instance, the GP's layer background is not shown clearly, SLAs boundary layer is masking other layers, the title should be HDSS etc

http://203.21.120.58/HDSS/HDSSViewer/index.html

Regards Ori

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14/02/2011 Trialling stage, email were sent to collect feedback and report of correction has been prepared

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22/02/2011 meeting Ori Gudes,

Malcolm Wolski, Jon

Shuker

Technical meeting to discuss of the HDSS will be maintained A decision was

made that Griffith University will

support and main

the system in the trialling period

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Chapter 9:

* GU = Griffith University

* EYI = Early years advisory group

* SV = Spatial Vision

* LBHC = Logan Beaudesert Health Collation

* QH= Queensland Health

* WADOH= Western Australia Department of Health

* CRC-SI= Cooperative Research Centre for Spatial Information

http://www.google.com.au/url?sa=t&source=web&cd=1&ved=0CBcQFjAA&url=http%3A%2F%2Fwww.crcsi.com.au%2F&rct=j&q=crc-SI&ei=y26_TYLeJ8_KiAKXt4CeAw&usg=AFQjCNEIb3_DkWAHdkaIkRM7mW4JuTVudw&sig2=6_ms5QPQJCNqzWh5pMGLqA&cad=rja

Documents

DEVELOPING A FRAMEWORK FOR PLANNING ...eprints.qut.edu.au/50783/1/Ori_Gudes_Thesis.pdfDEVELOPING A FRAMEWORK FOR PLANNING HEALTHY COMMUNITIES: THE LOGAN BEAUDESERT HEALTH DECISION