Introduction: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. Aim: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. Methods: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. Results: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. Conclusions: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources.
Introduction: Individuals and healthcare providers need to trust that the EHRs are protected and that the confidentiality of their personal information is not at stake. Aim: Within CrowdHEALTH project, a security and privacy framework that ensures confidentiality, integrity, and availability of the data was developed. Methods: The CrowdHEALTH Security and Privacy framework includes Privacy Enhancing Technologies (PETs) in order to comply with the GDPR EU laws of data protection. CrowdHEALTH deploys OpenID Connect, an authentication protocol to provide flexibility, scalability, and lightweight user authentication as well as the attribute-base access control (ABAC) mechanism which supports creating efficient access control policies. Results: CrowdHEALTH integrates ABAC with OpenID Connect to build an effective and scalable base for end-users' authorization. CrowdHEALTH's security and privacy framework interacts with other CrowdHEALTH's components, for instance the Big Data Platform, that depends on user authentication and authorization. CrowdHEALTH users are able to access the CrowdHEALTH's database based on the result of an ABAC request. Moreover, due to the fact that the CrowdHEALTH system requires proofs during the interactions with data producers of low trust or low reputation level, the requirements for the Trust and Reputation Model have been identified. Conclusion: The CrowdHEALTH Integrated Holistic Security and Privacy framework meets the security criteria for an e-health cross-border system, due to the adoption of security mechanisms, such as user authentication, user authorization, access control, data anonymization, trust management and reputation modelling. The implemented framework remains to be tested to ensure its robustness and to evaluate its performance. The holistic security and privacy framework might be adapted during the project's life circle according to new legislations.
Abstract:The EU4ALL project (IST-FP6-034778) has developed a general framework to address the needs of accessible lifelong learning at Higher Education level consisting of several standards-based interoperable components integrated into an open web service architecture aimed at supporting adapted interaction to guarantee students' accessibility needs. Its flexibility has supported the project implementation at several sites with different settings and various learning management systems. Large-scale evaluations involving hundreds of users, considering diverse disability types, and key staff roles have allowed obtaining valuable lessons with respect to "how to adopt or enhance eLearning accessibility" at university. The project was evaluated at four higher education institutions, two of the largest in Europe and two mediumsized. In this paper, we focus on describing the implementation and main conclusions at the largest project evaluation site (UNED), which was involved in the project from the beginning, and thus, in the design process, and a medium-sized university that adopted the EU4ALL approach (UPV). This implies dealing with two well-known open source learning environments (i.e. dotLRN and Sakai), and considering a wide variety of stakeholders and requirements. Thus the results of this evaluation serve to illustrate the coverage of both the approach and developments.
Introduction: With With the proliferation of available ICT services, several sensors and health applications have become ubiquitous, while many applications have been developed to detect certain health conditions and early signs of disease. Currently, all these services operate independently, and the available data is heterogeneous with limited value gained from its exploitation. Aim: The Data Sources and Gateways component aims at providing an abstracted and unified API to support the data accumulation from various sources including healthcare organisations, biosensors, laboratories and mobile applications. Meanwhile it tackles connectivity and communication issues with such information sources. Methods: The CrowdHEALTH Data Sources and Gateways Service incorporates four main services:
Introduction: Dissemination benefits come from the outputs integration and implementation by the key audience, who will also determine the relevance and usability of the disseminated content. Aim: One of the CrowdHEALTH project's objectives is the transition from patient health records towards the Holistic Health Records (HHRs) and Social HHR. The CrowdHEALTH project aims at integrating high volumes of health-related data collected from various sources to support policy-making decisions. Methods: The European Federation for Medical Informatics (EFMI) supports the development of an effective Communication and Collaboration Plan identifying the messages, the tools and channels in disseminating the project and its outcomes to the target audience based on the McGuire approach. Results: The process for defining the dissemination strategy is a cyclic one as shown in the following figure involving review of each step periodically The next step was to define the four dimension dissemination approach based on McGuire attributes of persuasive communication. The objectives, target groups, key messages, the tools and channels where defined at this stage. Conclusion: The CrowdHEALTH project and its outcomes were disseminated with a variety of tools and channels such as scientific journals, conferences, exhibitions and social media communication.
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