CrowdHEALTH is focused on the development of dynamic modelling of obesity chronic disease based on the individual’s progress over time and evolution of BMI and associated conditions using a dashboard based on PM4H.
Today’s rich digital information environment is characterized by a multitude of data sources. There are extremely large amounts of medical data. But currently collected data are heterogeneous, spread across different health care providers and systems that operate independently. Due to this fact it is quite common that important events related to health are missed.
CrowdHEALTH is an international research project partially funded by the Horizon 2020 Programme of the European Commission that intends to integrate high volumes health-related heterogeneous data from multiple sources with the aim of supporting policy-making decisions.
The project is delivering a secure ICT platform to collect and aggregate high volumes of health data from multiple information sources in Europe. CrowdHEALTH also proposes the evolution of patient health records (PHR) towards Holistic Health Records (HHRs) enriched to become “Social HHRs” to capture the clinical, social and human factors. The CrowdHEALTH system has been deployed in five large-scale pilots that took place in Germany, Greece, Slovenia, Spain, Sweden and the United Kingdom and engaged more than 200,000 users.
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 modelling 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 centres, social networks, public environments, living labs, diseases monitoring).