Globalising health informatics

Globalising Health Informatics: The Role of GIScience

AuthorsHamish Robertson

Nick NicholasA/Professor Andrew Georgiou

A/Prof Julie JohnsonDr Joanne Travaglia

Contents

• Background

• The globalisation of health information and informatics

• Geotechnology

• Geographic Information Science

• Australian example

• African example

• Visualising health information

• Conclusion

Background

• Focus on multidimensional nature of reality and data

• Space (3D), time and scale

• Dynamic health informatics environments and spatial science developments

• Using spatial science methods for developmental science i.e. when information varies in detail and quality

• Focus on population ageing as a global issue

• Emphasis on visual methods for exploration and engagement with complex scenarios e.g. dementias and other chronic diseases

The Globalisation of Health Informatics

• Health systems development and expansion are closely linked with economic developmental, urbanisation and social change (WHO, 2001)

• Health professionals highly mobile – clinical and non-clinical personnel (ICT staff)

• Developing countries still losing health personnel to richer countries –ageing and disability a significant driver but also some return patterns e.g. Thailand (an out-source for HMO’s) and Rwanda – emerging regional centre for medical expertise

• Health technologies and applications a diffusion model of systemic change and uptake – buy tech and often adapt methods/practices

• Health information central to bureaucratic systems – public, private and NFP

• Global strategies for risk management, quality control and (some) systemic interoperabilities e.g. meta-data, semantic ontologies etc

Source: WHO, 2001

Tobler’s First Law

"Everything is related to everything else, but near things are more related than distant things.”

Tobler W., (1970) "A computer movie simulating urban

growth in the Detroit region” Economic Geography, 46(2): 234-240.

Geotechnology is one of the three "mega technologies" for the 21st century and promises to

forever change how we conceptualize, utilize and visualize spatial relationships in scientific research and commercial applications (U.S. Department of Labor)

Modelling involves analysis of spatial relationships and patterns

Prescriptive Modelling

Why So What What if…

Global Positioning System (location and navigation)

Remote Sensing(measure and classify)

GPS/GIS/RS

The Spatial Triad

Mappinginvolves precise placement

of physical features and inventories

(Discrete/Graphic)

Descriptive Mapping

(Nanotechnology) Geotechnology (Biotechnology)

Source: Joseph Berry, 2010

Map Analysis …provides “tools” for investigating spatial patterns and relationships

Geographic Information Systems (map and analyze)

is

Where What

(Berry)

Geographic Information Science

• Goodchild 1992 identifies a transition from geographic information systems (hardware/ software) to giscience (emergent scientific domain)

• A branch of information science –yes but not entirely or exclusively

• Intersectional scientific domain – information + space + spatial theories/concepts/methods

• Developmental field of scientific endeavourcharacterised by rapid change and expansion

• Many issues for health informatics already included in giscience – similar pathways, different timeframes -> see next slide

‘Early’ and Enduring GIScience Research PrioritiesSource: UCGIS, 1996!

• Spatial Data Acquisition and Integration

• Distributed Computing

• Extensions to Geographic Representation

• Cognition of Geographic Information

• Interoperability of Geographic Information

• Scale

• Spatial Analysis in a GIS Environment

• The Future of the Spatial Information Infrastructure

• Uncertainty in Spatial Data and GIS-based Analyses

• GIS and Society

Australian Example

• Dementia and sub-type data in Australia is still estimate only – no population-level studies

• Many sub-groups where data even less detailed e.g. immigrant groups, Aboriginal people etc

• Current evidence indicates sub-national variations in prevalence and incidence

• Hugely uneven population distribution and differential population ageing e.g. state and territory patterns

• Spatial model useful even in the context of relatively high resources and quality information

• Developmental knowledge base must acknowledge and integrate spatial variations – improved science e.gwhy here and not there? Implications?

Australian Dementia Estimate 2027

African Example• Limited quality information at various scales –

continental, national and sub-national• Locus of increasing change and diversity – e.g.

urbanisation, economic, social, demographic• Variability across space – MDGs, HIV/Aids, emerging

diseases etc• Focus for emergent scientific knowledge i.e. rapid

growth in volume, quality and conclusions – 10/66 project and local initiatives

• Ageing and continued growth often occurring concurrently – not a linear pattern

• Significant zones of conflict, vulnerability and limited resources to address ageing issues

Africa – Alzheimer’s Estimate 2010

Visualising Health Information• Big data in healthcare is as much about visualisation as

analysis –> visual = accessible• Sharing health information can’t always depend on high data

literacy – visual summaries help inform and educate• Communication can’t just be about abstract risks and ratios –

visual supports central to understanding• Spatially enabled data is highly visual and shareable• Maps (map technologies) link health information to the

environment – with and without borders/boundaries• Globalising health informatics will be ever more graphic/visual

in input/output formats;– e.g. where are most Ebola cases currently? Where do we have cool

store facilities for vaccines? Where is the closest trained provider? What safety issues do we face? Who has a computer or internet connection for data tracking? Where is data stored/archived? How to get into the field? Etc…

Conclusion• Health information increasing in importance as issues

and systems develop, change and expand – varied emphases e.g. acute clinical versus international public health, emerging health systems, WHO universal access agenda etc

• Spatial dimension adds to flexibility and science of health informatics in high and low information environments – adaptability for change

• Spatial science has already addressed meta-data issues, data interchange and interoperability concerns

• Visualisation of (1) scenarios and (2) changes in data over time and location = improved knowledge

• Globalising health informatics needs to be a spatially enabled scientific domain to link people data with clinical and place data