Authors:
Argyro Mavrogiorgou
1
;
Athanasios Kiourtis
1
;
Ilias Maglogiannis
1
;
Dimosthenis Kyriazis
1
;
Antonio De Nigro
2
;
Vicent Blanes-Selva
3
;
Juan M. García-Gómez
3
;
Andreas Menychtas
4
;
Maroje Sorić
5
;
Gregor Jurak
5
;
Mitja Luštrek
6
;
Anton Gradišek
6
;
Thanos Kosmidis
7
;
Sokratis Nifakos
8
;
Konstantinos Perakis
9
;
Dimitrios Miltiadou
9
and
Parisis Gallos
10
Affiliations:
1
Department of Digital Systems, University of Piraeus, Piraeus, Greece
;
2
Engineering Ingegneria Informatica SpA - R&D Laboratory, Roma, Italy
;
3
Instituto de Tecnologías de la Información y Comunicaciones - BDSLab, Universitat Politècnica de València, Valencia, Spain
;
4
BioAssist S.A., Athens, Greece
;
5
University of Ljubljana, Ljubljana, Slovenia
;
6
Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia
;
7
Care Across Ltd, London, U.K.
;
8
Department Health Informatics Center, Karolinska Institutet, Stockholm, Sweden
;
9
Singular Logic EU Projects Department, Kifisia, Greece
;
10
European Federation for Medical informatics, Lausanne, Switzerland
Keyword(s):
CrowdHEALTH, e-Health Platform, Health Records, Health Policies, Health Analytics, Big Data.
Abstract:
In today’s interconnected world, more health data is available than ever before, resulting into a rich digital information environment that is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. CrowdHEALTH introduces a new paradigm of Health Records, the Holistic Health Records (HHRs), which offer the ability to include all this existing health data. To achieve that, CrowdHEALTH seamlessly integrates big data technologies across the complete data path, providing its results to the health ecosystem stakeholders, as well as to policy makers towards a “health in all policies” approach. This paper describes the CrowdHEALTH architecture, summarizing all the mechanisms and tools that have been developed and integrated in the context of CrowdHEALTH. The latter, along with the experimentation with several use cases that provide diverse data from different sources, have provided useful insights towards the successful an
d wide adaptation of the CrowdHEALTH platform in the healthcare domain.
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