Technologie en innovatie in Vlaanderen: Prioriteiten.

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Framework of Social Customer Relationship Management in E-Health Services

Healthcare organization is implementing Customer Relationship Management (CRM) as a strategy for managing interactions with patients involving technology to organize, automate, and coordinate business processes. Web-based CRM provides healthcare organization with the ability to broaden service beyond its usual practices in achieving a complex patient care goal, and this paper discusses and demonstrates how a new approach in CRM based on Web 2.0 or Social CRM helps healthcare organizations to improve their customer support, and at the same time avoiding possible conflicts, and promoting better healthcare to patients. A conceptual framework of the new approach will be proposed and highlighted. The framework includes some important features of Social CRM such as customer's empowerment, social interactivity between healthcare organization-patients, and patients-patients. The framework offers new perspective in building relationships between healthcare organizations and customers and among customers in e-health scenario. It is developed based on the latest development of CRM literatures and case studies analysis. In addition, customer service paradigm in social network's era, the important of online health education, and empowerment in healthcare organization will be taken into consideration.Comment: 15 pages. arXiv admin note: substantial text overlap with arXiv:1204.3689, arXiv:1203.3919, arXiv:1204.3685, arXiv:1203.4309, arXiv:1204.3691, arXiv:1203.392

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

B2B Infrastructures in the Process of Drug Discovery and Healthcare

In this paper we describe a demonstration of an innovative B2B infrastructure which can be used to support collaborations in the pharmaceutical industry to achieve the drug discovery goal. Based on experience gained in a wide range of collaborative projects in the areas of grid technology, semantics and data management we show future work and new topics in B2B infrastructures which arise when considering the use of patient records in the process of drug discovery and in healthcare applications

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Boosting Economic Growth Through Advanced Machine Vision

International audienceIn this chapter, we overview the potential of machine vision and related technologies in various application domains of critical importance for economic growth and prospect. Considered domains include healthcare, energy and environment, finance, and industrial innovation. Visibility technologies considered encompass augmented and virtual reality, 3D technologies, and media content authoring tools and technologies. We overview the main challenges facing the application domains and discuss the potential of machine vision technologies to address these challenges. In healthcare, rising cases for chronic diseases among patients and the urgent need for preventive healthcare is accelerating the deployment of telemedicine. Telemedicine as defined in the EU commission staff working paper on “Telemedicine for the benefit of patients, healthcare systems and society” (COM-SEC, 2009) is the delivery of healthcare services at a distance using information and communication technologies. There are two main groups of telemedicine applications: (1) applications linking a patient with a health professional; and (2) applications linking two health professionals (such as tele-second opinion, teleradiology). Machine vision technologies, coupled with reliable networking infrastructure, are key for accelerating the penetration of telemedicine applications. Several examples will be drawn illustrating the use of machine vision technologies in telemedicine. Sustainable energy and environment are key pillars for a sustainable economy. Technology is playing an increasing vital role in energy and environment including water resources management. This would foster greater control of the demand and supply side of energy and water. On the demand side, technologies including machine vision, could help indeveloping advanced visual metering technologies. On the supply side, machine vision technologies could help in exploring alternative sources for the generation of energy and water supply. In the finance domain, financial crises and the failure of banking systems are major challenges facing the coming decade. Recovery is still far from reach entailing a major economic slowdown. Machine vision technologies offer the potential for greater risk visibility, prediction of downturns and stress test of the soundness of the financial system. Examples are drawn from 3D/AR/VR applications in finance. Innovation could be seen as the process of deploying breakthrough outcome of research in industry. The innovation process could be conceived as a feedback loop starting from channelling the outcome of basic research into industrial production. Marketing strategies and novel approaches for customer relationship management draw a feedback loop that continuously update the feed of breakthrough research in industrial production. In this respect, machine vision technologies are key along this feedback process, particularly in the visualisation of the potential market and the potential route to market. CYBER II technology (Hasenfratz et al, 2003 and 2004) is described in section 6 as a machine vision technology that has a potential use in the various application domains considered in this chapter. CYBER II technology is based on multi-camera image acquisition, from different view points, of real moving bodies. Section 6 describes CYBER II technology and its potential application in the considered domains. The chapter concludes with a comparative analysis of the penetration of machine vision in various application domains and reflects on the horizon of machine vision in boosting economic growth

The rationale of e-health evaluation: The case of NHS Direct

An important area of research is that of the evaluation of e-health services. A holistic e-health evaluation framework should address the aspects that are hampering healthcare services from embracing the full potential of information and communication technologies towards successful e-health initiatives. Towards building a holistic evaluation framework for e-health services, this paper is intended to examine the rationale of e-health evaluation, as the paper argues that this aspect should be addressed first in the development of such a framework. NHS Direct which is one of the largest e-health services in the world has been chosen to discuss and validate a set of evaluation rationales and their applicability in practice