In contemporary healthcare, nurses need to be users and generators of evidence to underpin their practice. As users of evidence they need to be able to locate evidence and critically appraise it. As generators of evidence nurses need to be able to research their own practice and translate those findings into practice. The overall aim of this project is to raise the profile of research in nursing by increasing both research capacity and capability and to enhance cooperation between higher education institution and hospitals. This has the ultimate aim to provide care to patients and families consistent with the best available evidence thereby improving patient outcomes (CMFT 2009). This project addresses two complex issues: the lack of research knowledge among RNs and poor research alliance between hospitals and HEIs. The project consortium consists of hospitals and HEIs that will address these issues jointly. The profile of research in nursing will be raised by increasing both research capacity and capability. Nursing research knowledge alliance will be encouraged through 5 identified objectives:1. to build infrastructure with research leadership that will support a research active environment (analysing nursing work environment and enablers/barriers in facilitating nurses to do research, establishing nursing research groups/units; identifying research mentors within the hospitals and linked researchers at HEI); 2. to improve access to research training and support opportunities (developing online learning programme on nursing research);3. to identify research priorities in practice and link in with HEI to address them jointly4. to encourage intra-disciplinary, multi-disciplinary & inter-agency research collaborations (research group establishment; journal clubs);5. to raise the profile of research conducted by nurses to internal and external organisations (encouraging nurses to submit abstracts to conferences, journals and improve research writing skills.

ClinicalModelling, a project gathering 9 partners from 4 countries, aims to design, test and scale up a telementoring and video modelling approach to strengthen surgeons’, nurses’ and students’ competences in surgeries and other technical procedures. In detail, the project has as objectives: SO1 To endow surgeons and nurses with equipment, guidance and competences to successfully use smart glasses to capture, edit and analyse video images to support learning processes in the health sector using video modelling and video self-modelling approachesSO2 To capacitate teachers, trainers and tutors to use and support students in the use of smart glasses to capture, edit and analyse video images to support learning processes in the health sector using video modelling and video self-modelling approaches during classes and simulationSO3 To empower and strengthen students’ social-emotional competences promoting their lifelong learning propensity SO4 To design and test new lessons and case studies for nursing master’s and nursing specialization coursesSO5 To launch the foundations to scaling up the innovative teaching approach to medical residences and nursing internshipsThe project has as target groups organizations, professionals, researchers and policymakers from the health, educational and ICT sectors Multiple events will be promoted to engage these target groups in the creation, implementation and validation of the following deliverables:D2.2 Manual on using smart classes in clinical practiceD2.3 Training toolkit for the capacitation of surgeons and scrub nurses on using smart glassesD3.1 Manual on video-modelling for health education: a new approach to bring practice into the classroomD3.2 Training toolkit for teachers, tutors and trainers on video-self modelling and video-modellingD4.1 Common library of medical and nursing casesD4.2 Catalogue of lessons for nursing master’s degreesD4.3 Catalogue of lessons for the continuous VET offers post-master’s degree

IMPROVE will use Patient Generated Health Data (PGHD) gathered via m-health and e-health technologies to gain improved insights into the real-life behavior of, and challenges faced by, patients of all ages with complex, chronic diseases and comorbidities. Already today, a wealth of patient and citizen information is available, but fragmented, and therefore not coming to its full utility and value. These personal data will complement and improve existing approaches for Patient-Centered Outcome Measures beyond those currently available in state-of-the-art platforms. The IMPROVE platform that the consortium will build will enable the smart use of patient input and patient generated evidence to 1) advance the role of patient preference and patient experience in the context of treatment selection, 2) improve medical device design based on patient preferences and experiences, and 3) facilitate faster market entry of patient-centric and cost-effective advanced integrated care solutions. Improved clinical adoption of Value Based Health Care, and enhanced return on research and innovation investments will be demonstrated in different care settings across the EU, for 10 use cases in at least 5 different disease areas (e.g., ophthalmology, oncology, cardiovascular disease, chronic inflammation, and neurology). The use cases will be conducted using a large variety of implementation strategies, building on a design thinking approach, to optimally test the innovative framework of data gathering and translation into controlled change and action. In addition, a significant contribution from implementation science is planned to reach out to all stakeholders that are relevant for this initiative and maximise the impact to IMPROVE healthcare provision.

Europe is facing unprecedented challenges, such as the health, migration, economic, climate, energy, and political crises, leading to a sharp increase in emergency public spending and relaxation of due diligence checks. This has resulted in a rise in corruption and fraudulent activities, which have significant negative impacts on the European economy, society, environment, and democracy. Despite emerging technology’s potential to become a powerful tool in the fight against corruption and fraud, the public sector has been slow to adopt digitalization, resulting in data NOT being shared, harmonized, or properly analysed, making evidence-based decision-making almost impossible. Governments are slowly adopting new approaches to ensure a more data-driven, transparent, and accountable public governance, but several fundamental data-related issues remain unresolved. With a team of 9 excellent research institutions and universities, 12 technology, business, and standards, developing companies, 7 public end users, and 3 domain-relevant, industry-exposed NGOs, CEDAR will: (1) Identify, collect, fuse, harmonise, and protect complex data sources to generate and share 10+ high-quality, high-value datasets relevant for a more transparent and accountable public governance in Europe. (2) Develop interoperable and secure connectors and APIs to utilise and enrich 6+ Common European Data Spaces. (3) Develop innovative and scalable technologies for effective big data management and Machine Learning (ML) operations. (4) Deliver robust big data analytics and ML to facilitate human-centric and evidence-based decision-making in public administration. (4) Validate the new datasets and technologies (TRL5) in the context of fighting corruption, thus aligning with the EU strategic priorities: digitalisation, economy, democracy. (5) Actively promote results across Europe to ensure their adoption and longevity, and to generate positive, direct, tangible, and immediate impacts.