Ricerc@Sapienza

Since 2022, the research and project activity has been focused on the opportunities for digitalization of health data and healthcare systems, which has led to several national and international grant funding. Indeed, especially after the experience of the pandemic emergency, the application of innovative methodologies designed to manage heterogeneous health data subject to digital transformation resulted essential, for the management of both chronic and acute  complex diseases. This process started in 2020, with the project proposal funded in the POS 2014-2020 program, "E-DAI," which involves the development of a digital ecosystem for the integrated analysis of health data related to high-impact diseases for an innovative model of care and research. An interdisciplinary virtual network was implemented on an extended geographic backbone of digital infrastructure to achieve technological innovation, translational clinical research and cross-disciplinary knowledge transfer goals. The implementation of an interoperable platform and digital technologies can allow to improve early diagnosis, monitoring and targeted treatment of diseases with high impact on the national health system (NHS). In 2022, with project proposals funded by the National Recovery and Resilience Plan (NRRP) - Partenariato Esteso (PE 6) - and by the National Complementary Plan (PNC) of the NRRP, the promotion of research, clinical and enterprises activities have been built vertically to achieve technological innovation objectives. With HEAL-ITALIA (PE 6), the goal is to provide novel, cost-effective, evidence-based predictive and noninvasive diagnostic pathways for faster, earlier, more accurate, and accessible prediction, diagnosis, and treatment of monogenic (rare), polygenic (cardiovascular and metabolic), and neoplastic diseases, as well as to identify innovative and effective therapeutic approaches. The project will enable the application of precision medicine approaches, particularly on integrated and precision diagnostics by developing risk-based stratification algorithms and provide open access scientific evidence for health policies. Finally, D3 4 HEALTH (PNC) will promote the development of innovative predictive, diagnostic, and therapeutic models, making use of the most advanced digital technologies, represented by omics, imaging biomarkers, and wearable devices and sensors by developing state-of-the-art Artificial Intelligence algorithms and Network Analysis methods. The advancement of scientific research will inevitably impact clinical practice optimizing patient care, specifically through the development of a Digital Twin and a Biological Twin of reference diseases. Indeed, Digital Twin, defined as clusters of patients homogeneous with respect to a given outcome in each disease (metastatic colon cancer, liver and bile duct cancer, central nervous system cancer, diabetes type I and multiple sclerosis), will represent scalable starting bases to adopt simulation strategies for the prediction of outcomes based on yet to be discovered parameters, and the recognition of complex patterns in the process. These solutions will improve the quality of life of the reference communities by reducing the need to get access to the hospital facility.