1. The vision of a HealthGrid Sofie NØRAGER European Commission DG INFSO, eHealth unit Sofie NØRAGER European Commission DG INFSO, eHealth unit

2. Outline of the presentation  Introduction to the HealthGrid  Some examples of Grid Imaging projects

3. eH E A L T H - R&D ACTIVITIES to from DG REGIO, RELEX, EMP, ENT… supports Electromagnetic Fields DG SANCO supports: Public Health Policy Others FUTURE KNOWLEDGE Technology for Health ” FUTURE 10 years (2003-2014) “ KNOWLEDGE Technology for Health ” “m to M” PAST 10 years (1991-2002) “ INFORMATION Technology for HealthCare ” “M to m” FP2FP3FP4FP5 Networks for HealthCare Professionals Computers for Doctors User Needs Approach Societal Demands 1989-1990 Budget 20M € Budget 100M € Budget 140M € Budget 200M € Projects 30 Projects 63 Projects 158 Projects 125 Results Feasibility Study Results AIM Community Results 1 st batch of Products Results EU Health Telematics Industry 2003-2006 2007-20102011-2014 1991-19941995-1998 1999-2002 @ 1. E-Molecule: Bioinformatics 2. E-cellule/tissue: neuroinformatics 3. E-Individual: Medical Informatics R ESEARCH Regional - National Plans DG INFSO supports: eHealth 2002 + 2005, eHealth Ministerial Conference 2005, TEN/TELECOM, etc DG RTD eEUROPE - eHEAlth TELECOMPOLICIES Others

4. 2.Bioinformatics/Genomics (BI) molecular informatics Electronic Health Record (EHR) Health Informatics/Telematics Standards Clinical Decision Support Intelligent Agent Systems for Health Telemedicine Safety and Security etc. Biosignal Analysis and Pattern Recognition Systems Neuro Algorithms Neurocell Technology Human Computer Interfaces Machine Learning Knowledge Discovery etc. Structural Genomics Functional Genomics Proteomics Biochip Technologies Advanced Data Clustering and Analysis Techniques Computational Biology Ethics etc. Medical Sciences Behavioral Sciences Social Sciences Biological Sciences example: Advancing into the molecular causes of diseases: Molecular Medicine example: Exploiting human genetics variation for individualized health care: Individualized Health Care example: Informatics in support of the next generation of brain research: Molecular Neuroscience example: Gene expression imaging in the brain or integration of genomic and neuroscience databases: Neurogenomics example: Modeling of structure and function of molecules, cells, systems in disease: Health Knowledge Science DG INFSO.B.1/JCH/CVD/ 14.XII.2001/ “rings” v19/10/2001 1.Medical Informatics (MI) citizen, patient and population informatics CB D 3.Neuroinformatics (NI) cell to organ informatics A IT Synergy between Medical Informatics, Bioinformatics, and Neuroinformatics: Knowledge Empowering Individual Health Care & Well-Being

5. “European” Research Area » now is the time to bring our endeavours together and to build a research and innovation equivalent of the "common market" for goods and services. That structure is called the European Research Area and is regrouping all Community supports for the better coordination of research activities and the convergence of research and innovation policies, at national and EU levels. «

6. Health + Grid = HealthGrid Health: All levels of data & information, from molecule to population needed to ensure better prevention, diagnosis and treatment. Grid: An environment, created through the sharing of resources, in which heterogeneous and dispersed data as well as applications can be accessed by different partners according to their authorisation, without loss of information.

7. Draft Ideas, September 2002 INDIVIDUALISED HEALTHCARE MOLECULAR MEDECINE Databases Association Modelling Computation HealthGRID Computational recommandation Public Health Patient Tissue, organ Cell Molecule Patient related data Public Health Patient Tissue, organ Cell Molecule

8. In 5 Years ! HealthGrid Conferences & web site www.healthgrid.org BrainGrid Hospital Grid Imaging Grid Environment, Astronomy,Physics Infrastructures, Technology Epidemiology Grid Pharma Grid BioGrid

9. In 10 Years ? HealthGrid Conferences & web side www.healthgrid.org BrainGrid Hospital Grid Imaging Grid Epidemiology Grid Pharma Grid BioGrid Infrastructures, Technology Environment, Astronomy,Physics

10. Starting Point Trial for the introduction of the Grid approach in the biotechnology industry BioGrid Task: operate a Grid for biomolecular simulations Biology & Medical Apps: Medical imaging Computational Grid for “Virtual Arteries” Surgical support tool Grid-Enabled Medical Simulation Services European federated mammogram database on a GRID infrastructure

11. 1st Conference HealthGrid 2003, Lyon January 2003  Knowledge and information discovery across distributed/federated databases  Computational grids  Integration  Virtual organisations  Privacy & security  Healthcare reluctance to introduce new IT

12. www.healthgrid.org  Under development - depends on your input  Information about Grid technology - also for starters  Link to projects (EU, US, ASIA etc.)  Information about conferences  Central contact point - who, what, how  Developed by the HealthGrid Association

13. HealthGrid 2004 January 29th - 30th 2004, Clermont-Ferrand, France http://clermont2004.healthgrid.org The aims of this conference are to reinforce and promote awareness of the possibilities and advantages linked to the deployment of GRID technologies in health. In this context "Health" does not involve only clinical practice but covers the whole range of information from molecular level (genetic and proteomic information) through cells and tissues, to the individual and finally the population level (social healthcare).

14. Medical Imaging Grid’s  MAMMOGRID (Mammogram database)  eDIAMOND (Mammogram database)  GEMSS (Medical Imaging Reconstruction)  MEDIGRID (Medical Images for 3D modelling)  NDMA (National Digital Mammography Archive)  etc etc.

15. Simulation /Imaging Software Grid Software /solutions Bio-numeric modelling Medical Expertise Legal Aspects Project Duration: 30 months, Commencement: 1.9.2002 http://www.gemss.de GEMSS: GRID-enabled Medical Simulation Services

16. GEMSS - main goals Main GEMSS Goals:  Secure and lawful Grid provision of medical simulation services,  Build 6 Grid-enabled medical prototype applications,  Build suitable middleware on top of common standards,  Install and evaluate a GEMSS test-bed,  Anticipate privacy, security and other legal concerns related to providing medical services over the Internet.

17. Necessary Assumption: No special purpose network infrastructure Appropriate User Interfaces & Applications Workflow Workflow Enactor Negotiation Business Processes Secure Transfer, Web Services Security, Logging Negotiated Service Provision GEMSS - Technical Goals & Challenges

18. GEMSS - outlook Status of Work:  GEMSS has finalised its design phase: client-server arch. based on web services (OGSA-compliant).  Outlook:prototype system – Feb. 2004 final GEMSS system – Aug. 2004  Contribution to Standardisation: GEMSS is assessing its involvement in GGF, IETF or W3C. Final Strategy has yet to be decided.

19. MammoGrid - European federated mammogram database implemented on a GRID infrastructure Main goals:  To provide a demonstrator for use in epidemiological studies, quality control and validation of computer aided detection algorithms.  Development of a fully functional Grid for these purposes, which will be implemented in hospitals.  Development of some CADe techniques - use of the SMF (Standard Mammogram Form) http://lotus5.vitamib.com/hnb/mammogrid/mammogrid.nsf/Web/Frame?openform

20. University Database Healthcare Institute Hospital Italy Hospital UK Shared meta-data Analysis-specific data Knowledge is stored alongside data Active (meta-)objects manage various versions of data and algorithms Small network bandwidth required Clinician’s Workstations Query Result Local Query Local Analysis Local Analysis Local Analysis Local Analysis Massively distributed data AND distributed analyses GRID Local Query Local Query Local Query MammoGrid - Federated System Solution

21. MammoGrid Why a Grid infrastructure ?  Large federated databases  large data (size of images)  needs enough data for statistics  Ontologies and metadata  Heterogeneous image formation parameters and features  Needs “standard” for comparison  Clinical information  Geographically distributed data  Effective data mining of a rapidly growing database  Computing expensive simulations  Allow for complex queries involving executables

22. MammoGrid Challenges  Data resides in hospitals  Firewall protected  Legal restrictions on access to data  Clinicians, researchers, developers, Govt, …  Secure file transfer - Patient privacy & security  Combining several databases  Medical image analysis clients are not Grid experts!  Services must be system-resident, invisible, generic.

23. Impact of Grid technology in Health  Define collaborations. There is a need for both technology developers and users in order to move from “research to applications”.  Define resources to be shared and access rights - address privacy and security issues.  Awareness of other Grid’s - no reinventing the wheel, interoperability  Creation of a HealthGrid community will ensure impact on standards, enable showcases etc.

24. Thank you for your attention !