Abstract
The principal goal of this chapter is to elucidate the future requirements of healthgrids if they are to become the infrastructure of choice for biomedical research and healthcare. These require ments take many forms, technical, organizational and economic, with initiatives required in the domains of ethical and legal regulation. Thus, particular objectives of the chapter are to explore and analyse each of these domains to a sufficient depth to be able to make sense of the overall picture.
TopIntroduction
Grid technology, one of the key technologies for the ‘European Research Area’, offers rapid computation, large scale data storage and flexible collaboration by harnessing together the power of large numbers of computers, from end-users’ desktops to powerful workstations and clusters of more powerful machines. The grid was devised for use in scientific fields, such as particle physics and bioinformatics, in which large volumes of data, or very rapid processing, or both, are necessary. The impact of this concept has already reached beyond eScience, to eBusiness, eGovernment and eHealth. However, a major challenge is to take the technology out of the laboratory to the citizen. The term ‘healthgrid’ is used to describe the application of this technology to biomedical and healthcare informatics. This domain of application presents some difficult challenges.
The SHARE project (http://eu-share.org/about-share/deliverables-and-documents.html) includes an extensive analysis of several case studies exploring their technical requirements, full discussion of the ethical, legal, social and economic issues which may impede early deployment, and concludes with an attempt to reconcile the tensions between technological developments and regulatory frameworks.
Grid technology (Foster et al, 2004), one of the key technologies for the ‘European Research Area’, offers rapid computation, large scale data storage and flexible collaboration by harnessing together the power of a large number of commodity computers or clusters of other basic machines. The grid was devised for use in scientific fields, such as particle physics and bioinformatics, in which large volumes of data, or very rapid processing, or both, are necessary. The impact of this concept is expected to reach far beyond eScience, to eBusiness, eGovernment and eHealth. However, a major challenge is to take the technology out of the laboratory to the citizen.
The concept of grids for health was born in Europe in 2002 and has been carried forward through the HealthGrid initiative. This European collaboration has edited a White Paper (Breton et al, 2005) setting out the concept and benefits of emerging grid technologies in different applications in healthcare.
The White Paper defines the concept of a healthgrid as follows:
HealthGrids are grid infrastructures comprising applications, services or middleware components that deal with the specific problems arising in the processing of biomedical data. Resources in healthgrids are databases, computing power, medical expertise and even medical devices.
The EU-funded SHARE project has identified some important challenges towards wide deployment and adoption of healthgrids in Europe. The project has devised a strategy to address the issues identified in the Action Plan for European e-Health and has devised roadmaps for technological developments, legal and ethical initiatives and socio-economic investments needed for successful uptake of healthgrids in the next ten years.
The SHARE roadmaps express certain measurable goals and objectives for the HealthGrid community, provide an analysis of the technical gaps to be bridged in order to achieve a number of staged technical objectives, explore the ethical, legal, social and economic (ELSE) conditions of such developments, analysing the extent to which technology and its environment will need to be reconciled, and articulate a strategy for the concurrent achievement of these goals and objectives subject to realistic contextual conditions.
These roadmaps have been developed from three major inputs:
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An analysis of user requirements in a carefully triangulated set of domains through current projects and scripted use-cases;
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A technical road map which sets out the key objectives for a viable ‘knowledge healthgrid’ to be achieved in a span of 10-15 years;
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A conceptual map of ELSE conditions, constraints and requirements which must be addressed before a knowledge healthgrid can be deployed in a real healthcare setting.
Key Terms in this Chapter
XML Schema: A definition of the structure of an XML document. A schema contains a set of rules that dictate how an XML document must look like in order to be an instance of this schema. The relationship between a schema and an XML document implementing it can be compared with a class definition and an instance in object-oriented programming.
XML: An annotation technology used to describe structured data within a document using mark-ups and tags, similar to HTML. The main difference between the two is that the elements in XML can be given a definition depending on their usage which may be semantic rather than presentational. XML is a text format and can be read easily either by humans or machines.
Data: Any and all complex data entities from observations, experiments, simulations, models, and higher order assemblies, along with the associated documentation needed to describe and interpret them
Web Service: A software system designed to allow inter-computer interaction over a network to perform a task. Other computers interact with a web service, in a manner prescribed by its interface, using messages which are enclosed in a SOAP envelope and are often conveyed by HTTP. Software applications can use web services to exchange data over a network.
Ontology: The systematic description of a given phenomenon, which often includes a controlled vocabulary and relationships, captures nuances in meaning and enables knowledge sharing and reuse. Typically, ontology defines data entities, data attributes, relations and possible functions and operations.
Data Mining: Automatically searching large volumes of data for patterns or associations.
SOAP: A protocol for exchanging XML messages over a network. It defines the structure of the XML messages (the SOAP envelope), and a framework that defines how these messages should be processed by software.
Workflow: A set of components and relations between them, used to define a complex process from simple building blocks. Relations may be in the form of data links which allow the output of one component to be used as the input of another, or control links which state some conditions on the execution of a component.
The Article 29 Data Protection Working Party: A working party established by article 29 of directive 95/46/EC. It is the independent EU advisory body on data protection and privacy. Its tasks are laid down in article 30 of directive 95/46/EC and in article 14 of directive 97/66/EC.
Metadata: May be regarded as a subset of data, and are data about data. Metadata summarise data content, context, structure, inter-relationships, and provenance (information on history and origins). They add relevance and purpose to data, and enable the identification of similar data in different data collections.
Grid: A fully distributed, dynamically reconfigurable, scalable and autonomous infrastructure to provide location independent, pervasive, reliable, secure and efficient access to a coordinated set of services encapsulating and virtualising resources
Authentication: Verifying and confirming the identity of a grid user.
Roadmapping: An extended look at the future of a chosen field of inquiry, leading to an outline or map of how and by what means to achieve certain goals.
Processing: Obtaining, recording or holding the data, or carrying out any operation on the data, including organising, adapting or altering it. Retrieval, consultation or use of the data, disclosure of the data, and alignment, combination, blocking, erasure or destruction of the data are all legally classed as processing.
Data Processor: Any person who processes data on behalf of a data controller.
Authorisation: Restricting access to resources based on what a user has been granted access to.
Informed Consent: A legal term referring to a situation where a person can be said to have given their consent based upon an appreciation and understanding of the facts and implications of an action.
Middleware: A software stack composed of security, resource management, data access, accounting, and other services required for applications, users, and resource providers to operate effectively in a grid environment.
Virtual Organisation: A group of grid users with similar interests and requirements working collaboratively and/or sharing resources regardless of geographical location.
Data Subject: An individual who is the subject of personal data.
Data Controller: The person or organisation responsible for the manner in which any personal data is processed.
Data Model: A model that describes in an abstract way how data is represented in an information system. A data model can be a part of ontology, which is a description of how data is represented in an entire domain