CrowdHEALTH aims at the provision of a data-driven platform that enables the creation and evaluation of public health policies. A key challenge in the creation of health policies is to ensure that several aspects are considered in a complete way. To this end, CrowdHEALTH introduces a new paradigm of Holistic Health Records (HHRs) that include all health determinants (e.g. social and lifestyle data such as nutrition or physical activities, biosignals obtained from home healthcare systems or wearables, clinical data, medications, etc.). These data are obtained from different sources and data stores, for which CrowdHEALTH proposes mechanisms that allow the data flow towards the new data structures of HHRs. Additionally, the CrowdHEALTH architecture that is presented in this document, proposes frameworks for dealing with data inconsistencies in order to ensure data quality, data heterogeneity towards enhanced interoperability, as well as data aggregation to realize the HHRs vision.
Furthermore, HHRs are not considered as "isolated" entities in the overall CrowdHEALTH infrastructure. The added-value emerges from their correlation and combination. In this context, HHRs are transformed into HHRs clusters capturing the clinical, social and human context of population segments and as a result collective knowledge for different factors. This collective knowledge is thereafter utilized by a set of health analytics tools and mechanisms (i.e. clinical pathway mining, risk identification and evaluation, forecasting and causal analysis, and situational awareness) in order to provide actionable information to the policy makers. CrowdHEALTH introduces a PDT that incorporates the aforementioned health analytics mechanisms and enables policy makers to pose questions and perform on-demand analytics on top of an innovative big data platform. A unique policy modelling approach is also introduced, driving KPI-based policies specification and thus allowing structured policies to be created and thereafter evaluated.