Home ITACA : Collective wisdom driving public health policies


Grant Agreement Nº 727560 (H2020)

Today’s rich digital information environment is characterized by the multitude of data sources providing information that has not yet reached its full potential in eHealth. CrowdHEALTH will introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants. HHRs will be transformed into Social HHRs communities capturing the clinical, social and human context of the population segmentation and as a result the collective knowledge for different factors depending on the communities formulation criteria (e.g. demographics, diseases, lifestyle choices, nutrition, etc).
CrowdHEALTH will deliver a secure integrated ICT platform that seamlessly integrates big data technologies across the complete data path, providing of Data as a Service (DaaS) to the health ecosystem stakeholders. CrowdHEALTH will develop policy modeling techniques to facilitate the inclusion of Key Performance Indicators (KPIs) in policies and the correlation of these KPIs both with all health determinants captured in HHRs and with information from other domains towards a “health in all policies” approach.
Creation and co-creation (cross-domain) of policies will be feasible through a rich toolkit, which will be provided on top of the DaaS, incorporating mechanisms for causal and risk analysis, as well as for compilation of predictions. Through the toolkit, multi-modal targeted policies addressing various time scales (long- / short- term), locations (area, regional, national, international), populations, and evolving risks will be realized.
CrowdHEALTH will facilitate policies evaluation (on complete policy and per-KPI levels) and optimization through adaptive and incremental visualizations of simulations and outcomes of evidence based analysis of prevention strategies. CrowdHEALTH will collect data and will be validated through 5 pilots addressing different environments (care centers, social networks, public environments, living labs, diseases monitoring).

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727560.