This proposal is for a personalised decision support system for chronic disease management that will make predictions based on real-time data in order to empower individuals to participate in the self-management of their disease. The design will involve users at every stage to ensure that the system meets patient needs and raises clinical outcomes by preventing adverse episodes and improving lifestyle, monitoring and quality of life. Research will be conducted into the development of an innovative adaptive decision support system based on case-based reasoning combined with predictive computer modelling. The tool will offer bespoke advice for self-management by integrating personal health systems with broad and various sources of physiological, lifestyle, environmental and social data. The research will also examine the extent to which human behavioural factors and usability issues have previously hindered the wider adoption of personal guidance systems for chronic disease self-management. It will be developed and validated initially for people with diabetes on basal-bolus insulin therapy, but the underlying approach can be adapted to other chronic diseases. There will be a strong emphasis on safety, with glucose predictions, dose advice, alarms, limits and uncertainties communicated clearly to raise individual awareness of the risk of adverse events such as hypoglycaemia or hyperglycaemia. The outputs of this research will be validated in an ambulatory setting and a key aspect will be innovation management. All components will adhere to medical device standards in order to meet regulatory requirements and ensure interoperability, both with existing personal health systems and commercial products. The resulting architecture will improve interactions with healthcare professionals and provide a generic framework for providing adaptive mobile decision support, with innovation capacity to be applied to other applications, thereby increasing the impact of the project.

Polycystic Ovary Syndrome (PCOS) is the most prevalent, chronic endocrine-metabolic disorder of adolescents and young women (AYAs), affecting 5-10% of AYAs worldwide. It is the most frequent cause of anovulatory subfertility. There is no approved therapy for PCOS. Standard off-label treatment with oral contraceptives reverts neither the underlying pathophysiology nor the associated co-morbidities Pilot studies have generated new insights into the pathophysiology of PCOS, and have thus led to the development of a new approach wherein the PCOS phenotype is reverted without side effects. The novel medication is a fixed, low-dose combination of two insulin sensitisers [Pioglitazone (Pio), Metformin (Met)] and one mixed anti-androgen and anti-mineralocorticoid (Spironolactone (Spi)] within a single tablet: SPIOMET SPIOMET4HEALTH will test, in a multicentre Phase II trial, the additive effects of each SPIOMET component, on top of lifestyle measures in AYAs with PCOS. SPIOMET aims at normalising the ovulation rate and endocrine-metabolic status via the reduction of hepato-visceral fat excess, in an early phase of the disorder. This approach is expected to reduce the risk of morbidity (including subsequent anovulatory subfertility), to improve the quality of life, and to lower the economic burden on European healthcare systems. The consortium clusters the experts from key research groups working on PCOS in AYAs, across Europe. The design of SPIOMET4HEALTH foresees that the patients themselves will be engaged over the entire timespan of the project, and will also contribute to the ultimate study evaluation. The update and validation of PCOS-specific Patient Reported Outcome Measures (PROM) will provide the first large-scale evidence on the psychosocial benefits of the tested treatments. The collective evidence from SPIOMET4HEALTH, once completed with economic modelling, will lead to conclusions that inform sound decision-making about PCOS across European healthcare systems.

Despite many EU policy recommendations on People with Disabilities health and their rights to access health care services, approximately, people with a learning disability die, on average, 16 years younger than the general population, and are over 4 times more likely to die from causes that were amenable to good quality healthcareFor this reason, this project will focus on one MAIN OBJECTIVE: SUPPORTED RIGHTS AND ACCESS OF PEOPLE WITH INTELLECTUAL DISABILITIES (PWID) TO SECONDARY AND TERTIARY HEALTHCARE SERVICES. Results:1: More effective policies on PWID inclusion in HealthCare services 2: Lower barriers for PWID to access HealthCare services 3: More effective training of HealthCare Staff to interact with PWID 4: Stronger and better collaboration between HealthCare Staff and other professional supporters Target Groups:•Project Beneficiaries: oDoctors, Nurses, Administrative staff in secondary and tertiary HealthCare services (HealthCare Staff)oEducators, Psychologists, Social workers, etc. supporting PWID (Professional Supporters)•Final target group:oPWIDDeliverables:•Study “PWID rights and access to HealthCare services in Europe”•EQUAL TREATMENT: self-learning e-modules for HealthCare staff•EQUAL TREATMENT FACILITATOR PATHWAY: self-learning e-module for Professional SupportersImpactBeneficiaries: Healthcare Staff and Professional supporters •Enhanced quality of intervention and more inclusive environment in HealthCare services •Better knowledge of the rights of PWID Universities: •Improved training offer and enhanced employability of their students Final Target group: •Better Health, reinforced social inclusion, supported rights, better Quality of life of PWIDTransversal impact/impact on other stakeholders: •Better Quality of life of PWID families •Raised awareness on the topic of Rights, social inclusion, diversity management

CGI-Clinics aims at improving personalised medicine in oncology by optimizing genomic data interpretation (after sequencing and before advising on compatible targeted therapies). Interpretation is a bottleneck for the full deployment and broad accessibility of Next Generation Sequencing (NGS) in cancer management. The project tackles the 3 main hurdles in the interpretation of cancer mutations: it is not systematic, it deals with a majority of variants of unknown significance and it fails to empower patients. The interpretation of tumor genomic data relies on the work of experts reviewing scattered databases and resources, in a time-consuming process that may lead to suboptimal clinical decisions. CGI-clinics will systematize the interpretation process by integrating relevant public and private databases hospitals in a one-stop shop tool, with the possibility to organize virtual molecular tumor boards co-facilitated by reference hospitals. Project will have three phases: a setup (assess needs), validation (pilot with the 9 clinical partners) and replication (30 hospitals across EU). It will enable democratization of genomic data interpretation (independent of their size, resources and profiling technology) and provide health economics validation. Relying on a systematic automatic learning platform, GCI-Clinics will increase the share of interpretable variants in tumors (from the current 9-12% to at least 50%), and features that constitute biomarkers of drug response. The interpretation process is complex for most cancer patients, alienating them from knowledge of their illness. CGI-Clinics will build eduCGI, an app to help them understand the information gained through interpretation of their tumors, facilitating informed discussions with clinicians and sharing their data for research. Ultimately, the project is built to inform policy-makers on cancer management and empower patients.