title: Research protocol: EB-GIS4HEALTH UK - foundation evidence base and ontology-based framework of modular, reusable models for UK/NHS health and healthcare GIS applications.
abstract: EB-GIS4HEALTH UK aims at building a UK-oriented foundation evidence base and modular conceptual models for GIS applications and programmes in health and healthcare to improve the currently poor GIS state of affairs within the NHS; help the NHS understand and harness the importance of spatial information in the health sector in order to better respond to national health plans, priorities, and requirements; and also foster the much-needed NHS-academia GIS collaboration. The project will focus on diabetes and dental care, which together account for about 11% of the annual NHS budget, and are thus important topics where GIS can help optimising resource utilisation and outcomes. Virtual e-focus groups will ensure all UK/NHS health GIS stakeholders are represented. The models will be built using Protege ontology editor http://protege.stanford.edu/ based on the best evidence pooled in the project's evidence base (from critical literature reviews and e-focus groups). We will disseminate our evidence base, GIS models, and documentation through the project's Web server. The models will be human-readable in different ways to inform NHS GIS implementers, and it will be possible to also use them to generate the necessary template databases (and even to develop "intelligent" health GIS solutions using software agents) for running the modelled applications. Our products and experience in this project will be transferable to address other national health topics based on the same principles. Our ultimate goal is to provide the NHS with practical, vendor-neutral, modular workflow models, and ready-to-use, evidence-based frameworks for developing successful GIS business plans and implementing GIS to address various health issues. NHS organisations adopting such frameworks will achieve a common understanding of spatial data and processes, which will enable them to efficiently and effectively share, compare, and integrate their data silos and results for more informed planning and better outcomes.
authors.affiliation: School for Health, University of Bath, Claverton Down, Bath BA2 7AY, UK. M.N.K.Boulos@bath.ac.uk.
authors.forename: Maged N Kamel
authors.initials: MN
authors.lastname: Boulos