From Clinical Information Systems toward HealthGrid
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This document summarizes the transition from clinical information systems to health grids and the future of health research infrastructure. It discusses trends like rising populations in Asia, increasing resource scarcity, and the need for multidisciplinary and open collaboration. Health grids are presented as enabling virtual collaborations across institutions. Key areas like medical imaging, computational models, and genomic medicine are highlighted. Adoption challenges and requirements like reliable, usable infrastructure are also summarized.
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From Clinical Information Systems toward HealthGrid
- 1. From Clinical Information Systems toward HealthGrid Yannick Legré, maatG President of the International HealthGrid Association (yannick.legre@healthgrid.org) HINZ 2010 Conference – November 3 rd 2010 – Wellington (New Zealand) www.healthgrid.org
- 3. A glance to the future…
- 13. Once upon a time… HealthGrid
- 19. Multiple Components Endocardium (LV) Mitral valve Epicardium (LV) Aortic Valve, Root Right Ventricle Left Atrium Right Atrium Aorta Pulmonary trunk Sim-e-Child 2 – Computational Models of the Human Body – the Virtual Physiological Human (3/3) http://sim-e-child.org
- 21. ACGT Screenshot of the Newsletter Winter 2009 (page 1 and page 2) 3 – Grid enabled Pharmaceutical R&D: PharmaGrids (2/2) http://eu-acgt.org
- 22. 4 – Grids for Epidemiological Studies & Public Health Surveillance (1/2) http://g-info.healthgrid.org g-INFO
- 23. Sentinelle / Ginseng Improve and facilitate systematic screening, such as cancer screening Easy and secure sharing of information between all concerned partners Allow anonymized access to data for epidemiological surveys Provide support to research and facilitate feedback to improve clinical practices Can be easily customized to fit the requirements of a city, a region, a country… Sanitary Surveillance System 4 – Grids for Epidemiological Studies & Public Health Surveillance (2/2) http://www.e-sentinelle.org
- 24. Innovative Medicine Initiative 5 – Genomic Medicine Grid (1/2) http://www.imi.europa.eu
- 26. Life Sciences and Biomedical Research: adoption and requirements
- 29. Toward a roadmap Reference distribution of grid services Courtesy of the SHARE project – www.eu-share.org http://roadmap.healthgrid.org phase 1 phase 2 Sustainable computing grid Reference implementation of grid services Sustainable data grid Agreed medical informatics & grid standards Sustainable knowledge grid Agreed open source medical ontologies Generalized use of knowledge grids
- 30. HealthGrid Roadmap: Challenges & complexity Courtesy of the SHARE project – www.eu-share.org http://roadmap.healthgrid.org
- 31. HealthGrid Roadmap: Another look Workflow tools http://roadmap.healthgrid.org Courtesy of the SHARE project – www.eu-share.org Visionaries Early Adopters Early Majority Late Majority 2007 2009 2011 2014 2016 Infrastructure Interoperability Models Time Complexity Visionaries Early Adopters Early Majority Late Majority 2007 2009 2011 2014 2016 Visionaries Early Adopters Early Majority Late Majority 2007 2009 2011 2014 2016 Infrastructure Interoperability Quality of Service Quality of Service On Demand Access On Demand Access User Friendliness Usability Improved Distributed Data Management Improved Distributed Data Management Distributed Data Models Data Integration Tools and Standards Knowledge Management Tools and standards Domain Specific Knowledge Management and Ontologies Domain Specific Knowledge Management and Ontologies Time Complexity Collaboration
- 35. From Research Infrastructures towards sustainable Infrastructures: the EGI example
- 43. HealthGrid 2011 June 29 th – July 2 nd 2011 Bristol, UK http://bristol2011.healthgrid.org (soon available)
Notas do Editor
- Collect all clinically relevant data for a patient to obtain full set of info of patient’s health Understand individual health ICT can contribute the following: collect and store data in a structured manner visualise representations of data build tools for making use of the data
- 1 – MEDICAL IMAGING AND MEDICAL IMAGE PROCESSING 2 – COMPUTATIONAL MODELS OF THE HUMAN BODY ( Therapy Planning and Computer-Assisted Intervention – Atlases – Numerical Simulations of the Human Body – … ) 3 – GRID ENABLED PHARMACEUTICAL R&D: PHARMAGRIDS 4 – GRIDS FOR EPIDEMIOLOGICAL STUDIES 5 – GENOMIC MEDICINE GRID
- - I will explain the road map we have devised in stages. A useful first stage is to consider our first attempt at a set of major milestones which lead to the major goal of knowledge healthgrids in widespread use. - We divide the milestones into two kinds -- technological (in purple), and -- deployment (in green). - We also distinguish those within relatively easy reach, in Phase 1, and those that we consider somewhat more remote, in Phase 2.
- - We also have this model from the world of innovation studies , which distinguishes between -- ‘visionaries’ – the pioneers in research and application; -- ‘early adopters’ (also known as ‘fast followers’) – who recognise the potential for rapid benefits and take up the technology quickly; -- ‘early majority’ (sometimes called ‘mature adopters’) – these take relatively little risk in adopting the technology; and -- ‘late majority’, those who finally adopt a technology because there is virtually no other option.
- - We also have this model from the world of innovation studies , which distinguishes between -- ‘visionaries’ – the pioneers in research and application; -- ‘early adopters’ (also known as ‘fast followers’) – who recognise the potential for rapid benefits and take up the technology quickly; -- ‘early majority’ (sometimes called ‘mature adopters’) – these take relatively little risk in adopting the technology; and -- ‘late majority’, those who finally adopt a technology because there is virtually no other option. - Even for early adopters, infrastructure interoperability and distributed data management are already necessary. - On demand access, ‘user friendliness’ and quality of service are at the first point of inflection, before rapid expansion. - With sophisticated AI tools in the later stages, a second inflection occurs and the technologies become routinely accepted.