23,286 research outputs found
The European Institute for Innovation through Health Data
The European Institute for Innovation through Health Data (i~HD, www.i-hd.eu) has been formed as one of the key sustainable entities arising from the Electronic Health Records for Clinical Research (IMI-JU-115189) and SemanticHealthNet (FP7-288408) projects, in collaboration with several other European projects and initiatives supported by the European Commission. i~HD is a European not-for-profit body, registered in Belgium through Royal Assent. i~HD has been established to tackle areas of challenge in the successful scaling up of innovations that critically rely on high-quality and interoperable health data. It will specifically address obstacles and opportunities to using health data by collating, developing, and promoting best practices in information governance and in semantic interoperability. It will help to sustain and propagate the results of health information and communication technology (ICT) research that enables better use of health data, assessing and optimizing their novel value wherever possible. i~HD has been formed after wide consultation and engagement of many stakeholders to develop methods, solutions, and services that can help to maximize the value obtained by all stakeholders from health data. It will support innovations in health maintenance, health care delivery, and knowledge discovery while ensuring compliance with all legal prerequisites, especially regarding the insurance of patient's privacy protection. It is bringing multiple stakeholder groups together so as to ensure that future solutions serve their collective needs and can be readily adopted affordably and at scale
Preliminary Recommendations for Shareable Metadata Best Practices
A primary goal of the IMLS Digital Collections and Content project has been to encourage sharing of metadata and participation in aggregating projects. Benefits realized through these activities include increased visibility of the collection, an environment of complimentary
resources, and enhanced discoverability of individual resources. In order for these benefits to be achieved, metadata from each repository must be interoperable with existing metadata in the aggregation. Although metadata may be quality metadata in the local context, the records must
have certain characteristics in order to be relevant in the aggregated environment.IMLS National Leadership Grant LG-02-02-0281unpublishednot peer reviewe
Building a Disciplinary, World-Wide Data Infrastructure
Sharing scientific data, with the objective of making it fully discoverable,
accessible, assessable, intelligible, usable, and interoperable, requires work
at the disciplinary level to define in particular how the data should be
formatted and described. Each discipline has its own organization and history
as a starting point, and this paper explores the way a range of disciplines,
namely materials science, crystallography, astronomy, earth sciences,
humanities and linguistics get organized at the international level to tackle
this question. In each case, the disciplinary culture with respect to data
sharing, science drivers, organization and lessons learnt are briefly
described, as well as the elements of the specific data infrastructure which
are or could be shared with others. Commonalities and differences are assessed.
Common key elements for success are identified: data sharing should be science
driven; defining the disciplinary part of the interdisciplinary standards is
mandatory but challenging; sharing of applications should accompany data
sharing. Incentives such as journal and funding agency requirements are also
similar. For all, it also appears that social aspects are more challenging than
technological ones. Governance is more diverse, and linked to the discipline
organization. CODATA, the RDA and the WDS can facilitate the establishment of
disciplinary interoperability frameworks. Being problem-driven is also a key
factor of success for building bridges to enable interdisciplinary research.Comment: Proceedings of the session "Building a disciplinary, world-wide data
infrastructure" of SciDataCon 2016, held in Denver, CO, USA, 12-14 September
2016, to be published in ICSU CODATA Data Science Journal in 201
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Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.
At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities
Towards an interoperable healthcare information infrastructure - working from the bottom up
Historically, the healthcare system has not made effective use of information technology. On the face of things, it would seem to provide a natural and richly varied domain in which to target benefit from IT solutions. But history shows that it is one of the most difficult domains in which to bring them to fruition. This paper provides an overview of the changing context and information requirements of healthcare that help to explain these characteristics.First and foremost, the disciplines and professions that healthcare encompasses have immense complexity and diversity to deal with, in structuring knowledge about what medicine and healthcare are, how they function, and what differentiates good practice and good performance. The need to maintain macro-economic stability of the health service, faced with this and many other uncertainties, means that management bottom lines predominate over choices and decisions that have to be made within everyday individual patient services. Individual practice and care, the bedrock of healthcare, is, for this and other reasons, more and more subject to professional and managerial control and regulation.One characteristic of organisations shown to be good at making effective use of IT is their capacity to devolve decisions within the organisation to where they can be best made, for the purpose of meeting their customers' needs. IT should, in this context, contribute as an enabler and not as an enforcer of good information services. The information infrastructure must work effectively, both top down and bottom up, to accommodate these countervailing pressures. This issue is explored in the context of infrastructure to support electronic health records.Because of the diverse and changing requirements of the huge healthcare sector, and the need to sustain health records over many decades, standardised systems must concentrate on doing the easier things well and as simply as possible, while accommodating immense diversity of requirements and practice. The manner in which the healthcare information infrastructure can be formulated and implemented to meet useful practical goals is explored, in the context of two case studies of research in CHIME at UCL and their user communities.Healthcare has severe problems both as a provider of information and as a purchaser of information systems. This has an impact on both its customer and its supplier relationships. Healthcare needs to become a better purchaser, more aware and realistic about what technology can and cannot do and where research is needed. Industry needs a greater awareness of the complexity of the healthcare domain, and the subtle ways in which information is part of the basic contract between healthcare professionals and patients, and the trust and understanding that must exist between them. It is an ideal domain for deeper collaboration between academic institutions and industry
Interoperable Systems: an introduction
This short chapter introduces interoperable systems and attempts to distinguish the principal
research strands in this area. It is not intended as a review. Significant review material is
integrated with each of the succeeding chapters. It is rather intended to whet the appetite for
what follows and to provide some initial conceptual orientation.
This book concerns the architecture, modelling and management of interoperable computing
systems. Our collective research agenda addresses all aspects of interoperable systems
development, including the business and industry requirements and environments for
distributed information services
MOSAIC roadmap for mobile collaborative work related to health and wellbeing.
The objective of the MOSAIC project is to accelerate innovation in Mobile Worker Support Environments. For that purpose MOSAIC develops visions and illustrative scenarios for future collaborative workspaces involving mobile and location-aware working. Analysis of the scenarios is input to the process of road mapping with the purpose of developing strategies for R&D leading to deployment of innovative mobile work technologies and applications across different domains. One of the application domains where MOSAIC is active is health and wellbeing. This paper builds on another paper submitted to this same conference, which presents and discusses health care and wellbeing specific scenarios. The aim is to present an early form of a roadmap for validation
1st INCF Workshop on Sustainability of Neuroscience Databases
The goal of the workshop was to discuss issues related to the sustainability of neuroscience databases, identify problems and propose solutions, and formulate recommendations to the INCF. The report summarizes the discussions of invited participants from the neuroinformatics community as well as from other disciplines where sustainability issues have already been approached. The recommendations for the INCF involve rating, ranking, and supporting database sustainability
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