Harnessing ICT-based Tool for Improving the
Collaborative Health Planning Debate; the
Case of Logan- Beaudesert, Australia
Presenters:
* Ori Gudes – Griffith University & QUT
The study is Supervised by: Tan Yigitcanlar, Elizabeth Kendall and Virendra Pathak
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Geospatial Revolution clip
 Geospatial Revolution clip
 http://www.youtube.com/watch?v=ZdQjc30YPOk&feature=channel
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Aims of the Study
 To examine the role of decision support systems as a mechanism to
facilitate collaborative health decision-making
 To develop a potential information management framework to underpin a
health decision support system
 To generate a participatory process to create an online tool for health
planners using geographic information systems (GIS)
 To measure and understand the impact and effectiveness of decision
support systems on health decision-makers / health planners
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Problems to address
 Lack of an effective information in health planning
 Lack of collaboration in decision-making practice
 Lack of evidence in decision-making processes
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Decision Support Systems
 Decision support systems (DSS) are set of solution
mechanisms that help all forms of decision makers
to assess complex decision-making processes and to
solve problems faced by the help of ICT tools (Shim
et al., 2002).
DSS aims to
 Improve efficiency of decisions
stakeholders
 Optimise their overall performance
 Minimise judgemental biases
Turban, 1990
made
by
Health Decision Support System
Evidence-based
Policy
Public
Participation
Knowledge
Sharing
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How our data collection efforts have been grounded?

Fundamental / macro level
 most difficult to influence
 greatest benefits achieved

Intermediate / meso level
 more easily influenced

Proximate / micro level





level where we usually work
least effective
necessary for planning
have been given greater scientific
attention in recent years
Individual level
 targeting disease
 not most successful
 influence the individual habitués.
This framework
summarises the
different levels of
factors that impact
upon health and,
therefore, should be
considered in health
planning initiatives.
Health assessment framework
Schulz and Northridge (2004)
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Fundamental Macro Level
Intermediate Meso Level
Our Dataset
Public Housing
Public transportation
Public Housing
Bus stations
Bus routes
Railway Stations
Railway routes
Proximate Micro Level
Health Behaviours and
Social Support
Physical activity
Social support
Dietary practice (BMI Index)
Individual Outcome Level
Health Outcomes
Child health data
Obesity
Cancer
Mental health
Respiratory health (Asthma)
Cardio-vascular
Diabetes
Communicable disease
Avoidable admissions
Hospital admissions (the following diseases: Depression,
Renal, Diabetes, Respiratory)
Demographic
Health facilities
Well-Being
Population
Projected population
Mortality rate
Indigenous
Multicultural (Clustered Nationalities)
Nationalities
Mortality
Pharmacies
Aged care
Breast Screen
Child Health (paediatric)
Medical Services
Mental health
Oral health
Public hospitals
Private hospitals
GP’s
Medicare
Hope / despair
Life satisfaction or life style
Happiness and self fulfilment
Disability
Health status
Socio Economic
Education Facilities
SEIFA Index
Unemployment’s rate
Income average and financial resources
Internet access
Education
Businesses by Industry Division
Public Housing
Has need for assistance with core activities
Child community Services
Higher education
Libraries
Schools
Special education
State Pre School
Youth clubs
Play groups
Universities / TAFE
Crime
Crime rate (based on the Australian Standard Offence
Classification)
Community facilities
Boundaries
Recreation
Statistical Local Area
Postcode
Suburb
Parks
City swimming pools
Sporting facilities
Cycling paths
Environmental
Emergency
Community centres
Community facilities
Community Welfare
Employment services
Religious institutions
Services clubs
Social clubs
Sporting clubs
Youth clubs
Schools, State, Non-state schools
Centre link offices
Biodiversity and Contaminated land
Environmental hazards
Police
Fire
Fire
Ambulance
Terrain
Other
Aerial images (Orthophoto)
Topography (DEM)
Contour
Shopping centres
Non profit organisations
Fast food outlets
Our Spatial Dataset
(derived from Schulz and
Northridge 2004 )
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HDSS Data Sources
ABS
Internet and
public domain
EPA
Others
Data Sources for
The HDSS
Scenic Rim
Regional Council
QLD Police
Queensland
Health
Logan City Council
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Why should we use DSS? Our Conceptual framework
How the
HDSS will
be used ?
Bringing together
diverse datasets using a
spatial methodology to
empower stakeholders
in planning for healthy
communities
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Pre DSS intervention survey
The quantitative data was collected using a 31-item
survey based on several decision-making scales
(Dean & Sharfman, 1993; Flood et al, 2000; Bennet et
al, 2010; Parnell & Bell, 1994). The items measured
the following dimensions of decision-making:
Evidence-based decision-making;
Perceived consensus;
Participation in decision-making;
Perceived satisfaction of decision-making;
Perceived importance of decision-making
Perceived effectiveness of decision-making; and
Perceived equity of decision-making;
Where are we?
South East Queensland
South of Brisbane City
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Population: 270,000
31 SLAs
1 in 4 smokers
13% high risk alcohol
66% overweight
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LBHC structure
A collaborative
arrangement
between
government
and nongovernment
agencies, the
private sector
and community
to develop and
deliver health
programs and
services
Accessibility to
hospitals based on
travel time
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Fast-food outlets and
their proximity to
schools
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Hotpot analysis
based on people
who admitted to
hospitals with
diabetes
* (Based on Logan-Beaudesert
Hospitals data 2005-2007)
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Accessibility to
internet as key
variable for future
E-health initiatives
and planning
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Preliminary findings from the Pre DSS survey
• Low levels of satisfaction with the decision-making processes across the
LBHC.
• There was some diversity across the components of the LBHC
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, males, new members and veterans to be more satisfied with
information and perceive higher levels of consensus, participation and
evidence-based decision-making.
• There was an overall sense that decisions were ineffective, presumably
because they were not based on information or evidence.
Conclusion from the Pre DSS survey findings
• New members are enthusiastic, but become more critical of decisionmaking over time and then eventually resolve this situation in some way –
either by withdrawing or seeking other sources of information).
• 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 region. As a result, they
may be less demanding of the decision-making processes.
Overall discussion
In summary, our findings have shown that:
There was considerable diversity in the way decision-making were
perceived, this, in turn, may require different approaches to health
planning across different groups.
The findings have also highlighted the need for a comprehensive
information framework and collaborative process to underpin planning for
healthy cities, thus enabling health coalitions to make effective decisions
that engage all stakeholders equitably.
The framework proposed in this paper would not only encourage 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 process.
Future Research
Unanswered questions:
• How the suggested framework and method are
actually applied in local communities
• The impact of the HDSS on decision-making and its
ability to facilitate collaborative-based health
planning debate
These important research questions shall be
addressed as we conduct the next phases in this
ongoing project.
HDSS Where to from here, challenges and
practicalities for Logan council?
Prototype development
stage – phase 1
Dec 2010
HDSS expanding usage to the
whole public – phase 3
Nov 2011
Phase 2
Dataset update (ABS) and
HDSS expanding usage to
the whole LBHC
Nov 2012
Nov 2014
National level / collaboration
with other bodies within
QLD and West Australia or
CRC-SI
“”Maps can show you where you are.
But a good map can also show you where you want to go,
and what needs to happen so that you can get there”
(http://www.mappingforchange.org.uk/, 2009)
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