Cardiovascular diseases (CVD) present a significant public health challenge throughout Europe, being the leading cause of morbidity and a major contributor to mortality. Despite technological advancements and preventative strategies, the burden of CVD remains substantial, and the cardiac care pathways across Europe are inadequately mapped. The overarching goal of the EuroHeartPath consortium is to transform cardiovascular care pathways across the continuum of care. By adopting a pan-European scope and facilitating cross-country comparisons, EuroHeartPath aims to elucidate how care is organized and delivered, providing deeper insights and identifying best practices for integration with the support of stakeholders, including ESC, WONCA, EUPHA, and EPA. EuroHeartPath will simultaneously conduct 18 pathfinder studies to optimize the personalized early detection, diagnosis, monitoring, and treatment of heart failure, atrial fibrillation, ventricular fibrillation, and coronary disease, with a particular emphasis on prevention. These studies are categorized into four key areas: 1) novel artificial intelligence (AI) and machine learning applications, such as automated AI analysis of ECGs and Holters, and AI for predicting cardiomyopathies and cardiovascular events; 2) digital health integration, aimed at improving the diagnosis of aortic stenosis and enhancing secondary prevention measures; 3) point-of-care testing, including the use of point-of-care diagnostics in primary practice and ambulances to exclude heart failure; and 4) technology and robotics, encompassing robotic echocardiography and invasive diagnostics in obstructive epicardial coronary artery disease. EuroHeartPath will create a paradigm shift in the management and treatment of heart disease, offering substantial scientific, societal, and economic impacts across the entire patient health journey with a patient centric approach.

While randomized controlled trials (RCTs) remain the mainstay in drug development, approval, and reimbursement, the potential of real world data (RWD) to contribute to the understanding of drug effects is increasingly realized. Evidence, based on RWD – real world evidence (RWE) - can contribute significantly to the evidence to support decision making throughout all phases of (clinical) drug development, as well as improve efficiency in design and conduct of clinical trial programs. The aim of this project is to develop, implement and establish evidentiary standards and methods to address the data and evidentiary needs of regulatory authorities and HTA bodies towards a more efficient use of RWD for the development, registration and assessment of medicinal products in Europe (More-EUROPA).

Globally 1.Globally 1.71 billion people have musculoskeletal symptoms, the leading contributor to disability. Early disease stratification is important to ensure appropriate care (most suited healthcare provider and best treatment choice). Currently the patient journey to diagnosis and effective treatment is long and inefficient, resulting in persistent disease burden and economical loss. This is due to insufficiently understood relations disease causes and similarities in symptoms between diseases, insufficiently distinguishing tests, trial and error approach in initial treatment. SPIDeRR aims to disentangle the real-life complexity of early diagnosis of rheumatic diseases by considering the complete web of factors influencing patients’ symptoms. SPIDeRR’s approach will go well beyond the state-of-the-art in the following ways: - By identifying different disease groups, requiring different therapies, amongst patients with similar symptoms in contrast to the traditional approach aiming to only capture one disease early. - By integrating all relevant data dimensions from every healthcare level (primary and secondary care and patients seeking advice online). - By translating and applying machine learning techniques from the “omics” field to clinical patient data, which will result in new pipelines for translational data science SPIDERR will deliver three clinical models -a symptom checker for patients -a decision support tool for (primary) care providers providing guiding additional examination and referral decisions -a patient-patient similarity network to optimise diagnostic groups in rheumatology and support treatment decision To achieve this we additionally deliver solutions for data integration and shared analyses though GDPR compliant digital research environment and federated learning pipelines. Finally we will test the acceptability of the models through stakeholders studies and provide an implementation scene tailored to current healthcare in Europe.

ONCOVALUE will unlock the full potential of real world data (RWD) collected in European cancer hospitals and institutes to ease the decision-making of regulators on cost-effectiveness of novel cancer therapies. To achieve this, we build up data collection and processing capabilities of leading European cancer hospitals to create a high-quality clinical, quality of life, and adverse events data-sources. With the use powerful AI technologies, we will transform unstructured data originating from medical notes and medical images into structured data to enable analytics and real world evidence (RWE). This RWE will be directly available for clinicians for treatment management and for health regulatory and HTA bodies to adopt optimized data-driven methodologies for the effective assessment of medicinal products and digital health innovations. For that, we will provide an end-to-end infrastructure for RWD reporting in health regulatory and HTA decision-making and address the legal constraints in the cancer hospitals to ensure secure and legal access to RWD. Furthermore, ONCOVALUE will ensure the implementation of the developed guidelines and methodologies, by providing trainings for the collection and management of high-quality RWD in European cancer centres and for the analysis of this data by HTA and regulatory bodies. By opening the door to widespread regulatory and HTA integration of RWD, ONCOVALUE will lead to safer, more efficient, and affordable therapies, technologies, and digital solutions for (personalised) cancer care. As such, ONCOVALUE is positioned to contribute to increased cost-effectiveness and subsequent sustainability of cancer care. Systematic collection and evaluation of the patient reported outcomes will lead to improved well-being of the patients. Subsequently, on the long-term implementation of value-based cancer care at European cancer hospitals will aid in reducing the growing burden of cancer treatment in the EU and worldwide.