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Original Article



The CrowdHEALTH project and the Hollistic Health Records: Collective Wisdom Driving Public Health Policies

Dimosthenis Kyriazis, Serge Autexier, Iván Brondino, Michael Boniface, Lucas Donat, Vegard Engen, Rafael Fernandez, Ricardo Jimenez-Peris, Blanca Jordan,, Gregor Jurak, Athanasios Kiourtis, Thanos Kosmidis, Mitja Lustrek, Ilias Maglogiannis, John Mantas, Antonio Martinez, Argyro Mavrogiorgou, Andreas Menychtas, Lydia Montandon, Cosmin-Septimiu Nechifor, Sokratis Nifakos, Alexandra Papageorgiou, Marta Patino-Martinez, Manuel Perez, Vassilis Plagianakos, Dalibor Stanimirovic, Gregor Starc, Tanja Tomson, Francesco Torelli, Vicente Traver-Salcedo, George Vassilacopoulos, Andriana Magdalinou, Usman Wajid.

Abstract
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.

Key words: Holistic Health records, Health Analytics, Public Health Policy Making.


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