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The CrowdHEALTH project and the hollistic health records: collective wisdom driving public health policies

The CrowdHEALTH project and the hollistic health records: collective wisdom driving public health policies
The CrowdHEALTH project and the hollistic health records: collective wisdom driving public health policies
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
1986-5988
369-373
Boniface, Michael
f30bfd7d-20ed-451b-b405-34e3e22fdfba
Boniface, Michael
f30bfd7d-20ed-451b-b405-34e3e22fdfba

Boniface, Michael (2019) The CrowdHEALTH project and the hollistic health records: collective wisdom driving public health policies. Acta Informatica Medica, 27 (5), 369-373. (doi:10.5455/aim.2019.27.369-373). (In Press)

Record type: Article

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

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Accepted/In Press date: 30 December 2019

Identifiers

Local EPrints ID: 442197
URI: http://eprints.soton.ac.uk/id/eprint/442197
ISSN: 1986-5988
PURE UUID: 4cd10e1f-49e0-468c-80e0-d8474d038c19

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Date deposited: 08 Jul 2020 16:31
Last modified: 28 Jul 2020 16:47

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Author: Michael Boniface

University divisions

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