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).

The specific focus of iHELP is on early identification and mitigation of the risks associated with Pancreatic Cancer based on the application of advance AI-based learning and decision support techniques on the historic (primary) data of Cancer patients gathered from established data banks and cohorts. This analysis helps to (i) determine key risks associated with Pancreatic Cancer, (ii) develop predictive models for identified risks, and (iii) develop adaptive models for targeted prevention and intervention measures. Based on these developments, the project selects high-risk individuals that are invited to take part in the pilot activities or digital trials. The digital trials are carried out through user-centric mobile and wearable applications that apply proven usability principles to offer more awareness, more engaging experience for health monitoring, risk assessment and personalised decision support. Close collaboration between clinical and AI experts focus on drawing decision support against identified/predicted risks and providing personalised recommendations (e.g. lifestyle changes, behavioural nudges, screening test etc) to the participants in the digital trials. The iHELP (mobile and wearable) technology solutions help in validating iHELP solutions and raising health related awareness at individual level. The (secondary) data gathered through the mobile and wearable applications (concerning life style, behavioural, social interactions and response to targeted prevention and intervention measures) is integrated with primary data in the standardised HHR format – within a big data platform. Frugal AI-based learning techniques are developed to provide near real-time risk assessment based on the integrated and standardised HHR data. iHELP solutions are targeted at multiple stakeholders, including policymakers that will get decision support on the design of new screening programs and new guidelines for bringing improvements in clinical and lifestyle aspects.

The Euro-Asian Joint Master programme in Medical Technology and Healthcare Business (EMMaH) is a 2-year (120 ECTS) course, taught in English and run by the University of Lille (France), Hamburg University of Applied Sciences (Germany), the Polytechnic Institute of Porto (Portugal), and Taipei Medical University (Taiwan). Supported by a multicultural and multidisciplinary team of 46 staff, EMMaH covers different fields, from biomedical technologies and clinical research to healthcare business. The fast-changing healthcare diagnostics sector, boosted by innovation and demographic evolutions, is confronted with transformation in the healthcare landscapes. EMMaH’s main outcome is to reduce the lack of human resources in the research and technology-based sector of health science and biomedical engineering by increasing employability over a wide spectrum of positions and responsibilities. In four editions designed for 85 students, the consortium would allocate 60 EMJM high-level scholarships to excellent students worldwide to study one semester on each EU campus and complete a master thesis with any of the 4 partners. Graduates are awarded a joint diploma co-signed by the EU partners, the MSc from TMU and digital graduation certificates. EMMaH includes 13 associated partners (companies: Air Liquide, apoQlar, Bayer, IQVIA, Siemens; research institutes: INSERM, NHRI, ITRI; networks: RACS, CONIF; and HEIs: IFSF Brazil, HES-SO and UExeter), which contribute to strengthen the employability and supervise student projects and master theses. Moreover, they co-organize job fairs and offer industry and hospital internships. EMMaH partners carry out the EMJM project through two work packages: Project management and coordination; Student selection and supervision; Curriculum implementation; Promotion; Impact, sustainability and dissemination of results. Each partner leads tasks and coordinates activities in its field of expertise, all co-create and co-develop a set of 25 outputs.

Smoking is the largest avoidable cause of preventable morbidity worldwide. It causes most of the cases of lung cancer and chronic obstructive pulmonary disease (COPD) and contributes to the development of other lung diseases. The control of smoking is considered as a highly important intervention for the prevention of lung diseases. Tobacco consumption is highly influenced by socioeconomic factors. SmokeFreeBrain aims to address the effectiveness of a multi-level variety of interventions aiming at smoking cessation in high risk target groups within High Middle Income Countries (HMIC) such as unemployed young adults, COPD and asthma patients, as well as within the general population in Low Middle Income Countries (LMIC). The project addresses existing approaches aiming to prevent lung diseases caused by tobacco while at the same time it develops new treatments and analyzes their contextual adaptability to the local and global health care system. SmokeFreeBrain follows an interdisciplinary approach exploiting consortium’s expertise in various relevant fields in order to generate new knowledge. State of the art techniques in toxicology, pulmonary medicine, neuroscience and behavior will be utilized to evaluate the effectiveness of: (i) Public Service Announcement (PSA) against smoking, (ii) the use of electronic cigarettes with and without nicotine as a harm reduction approach and/or cessation aid, (iii) a specifically developed neurofeedback intervention protocol against smoking addiction, (iv) a specifically developed intervention protocol based on behavioral therapy, social media/mobile apps and short text messages (sms) and (v) pharmacologic interventions. The main objective of the project is to evaluate the interventions in terms of health economics, by studying their cost-effectiveness, and proposing a scalable plan and a clear pathway to embedding the proposed interventions into policy and practice both in LMIC as well as in HMIC.