Nowadays several devices and platforms provide health data (e.g. blood pressure, heart rate, sugar levels, etc). But data and respective services operate independently, and it becomes increasingly common to miss important events (e.g. early indications of spatiotemporal development of diseases). On the other hand, the multitude of data sources is an opportunity to make effective and targeted policies, prevent diseases and promote health in general. Additional factors are health determinants to be taken into account, such as the physical, social and economic environment, genetics, relationships with friends and family. However, health records (EHRs / PHRs) are still far from including aspects such as the environment, fit lifestyle, nutrition, mental and emotional health.
Capturing and linking this information with other data in EHRs would allow learning about outcomes of prevention strategies, health policies, etc. Records would become placeholders of all types of multi-information: data from multiple sources, incorporating multi-discipline knowledge, facilitating multi-stakeholder collaboration, capturing multi-morbidity cases.
CrowdHEALTH aims at delivering an integrated platform that provides decision support to public health authorities for policy creation / co-creation, through the exploitation of collective knowledge that emerges from multiple information sources. The latter will be realized through Holistic Health Records – HHRs that can include health, social and lifestyle data, data from medical devices, clinical data, diagnoses, medication, laboratory data, etc (see fig.1).
On top of big data management, CrowdHEALTH provides health analytics tools,(e.g. causal & risk stratification mechanisms, forecasting & simulation tools) towards the development of multi-modal targeted policies (see fig.2).
CrowdHEALTH will impact society providing policy makers with the means of processing large amount of healthcare information from a single entry point. The project’s platform will provide actionable insights on health-related implications of other policies (e.g. education or employment). It will facilitate the development of prevention strategies based on the evaluation and simulation of different scenarios. It will also provide means of assessing the impact of implementing strategies across specific patient groups and enable identifying successful KPIs.
