There are three main reasons for an immediate innovation action to apply big data technologies in Healthcare. Firstly, a Healthy nation is a Wealthy nation! An improvement in health leads to economic growth through long-term gains in human and physical capital, which ultimately raises productivity and per capita GDP. Secondly, Healthcare is one of the most expensive sectors, which accounts for 10% of the EU’s GDP continuously becoming more expensive. Thirdly, as healthcare is traditionally very conservative with adopting ICT, while big healthcare data is becoming available, the expected impact of applying big data technologies in Healthcare is enormous. BigMedilytics will transform Europe’s Healthcare sector by using state-of-the-art Big Data technologies to achieve breakthrough productivity in the sector by reducing cost, improving patient outcomes and delivering better access to healthcare facilities simultaneously, covering the entire Healthcare Continuum – from Prevention to Diagnosis, Treatment and Home Care throughout Europe. BigMedilytics produces: • A Big Data Healthcare Analytics Blueprint (defining platforms and components), which enables data integration and innovation spanning all the key players across the Healthcare Data Value Chains • Instantiations of the Blueprint which implement BigMedilytics concepts across 12 large-scale pilots accounting for an estimated 86% of deaths and 77% of the disease burden in Europe • The Best “Big Data technology and Healthcare policy” Practices related to big data technologies, new business models and European and national healthcare data policies and regulations. BigMedilytics will maximize the impact by using its Big Data Healthcare Analytics Blueprint and the Best Practices to scale-up the concepts demonstrated in the 12 pilots, to the whole Healthcare sector in Europe. It will use health records of more than 11 million patients across 8 countries and data from other sectors such as insurance and public sector.

Stroke is one of the most severe medical problems with far-reaching public health and socio-economic impact, gathering momentum in an ageing society. PRECISE4Q sets out to minimise the burden of stroke for the individual and for society. It will create multi-dimensional data-driven predictive simulation computer models enabling – for the first time – personalised stroke treatment, addressing patient’s needs in four stages: prevention, acute treatment, rehabilitation and reintegration. Heterogeneous data from multidisciplinary sources will be integrated: genomics, microbiomics, biochemical; imaging including mechanistic biophysiological models of brain perfusion/function; social, lifestyle, gender; economic and worklife, requiring substantial efforts for information extraction, semantic labelling and standardisation. Novel hybrid model architectures, structured prediction models, complex deep-learning and gradient boosting models will form the Digital Stroke Patient Platform including a Stroke Risk CDSS (Clinical Decision Support System), Treatment Outcomes CDSS, Rehab Programme, Socio-Economic Planning Tool and New QvidLab. The decision support will be tailored to the patient's current life stage thus enabling clinicians to optimise prevention and treatment strategies over time, and will include personalised coping strategies, support of well-being and reintegration into social life and work. The predictive capability and clinical precision will be validated with real clinical data generated by (i) prospective clinical studies and (ii) retrospective analyses of big data sets: health registries, cohort studies, health insurance data, electronic health records. PRECISE4Q will have a clinically measurable and sustainable impact leading to better understanding of risk, health and resilience factors. In contrast to current schematic therapy guidelines, it will support patients throughout their life-long journey by personalised strategies for their specific needs.