A Research and Innovation Project
INsu-LINE: Promoting integrated and data-driven care for chronic diseases
INsu-LINE is an Italian research project aimed at improving diabetes management through digital technologies and data science. The project, lasting 13 months, is funded by the second Cascade Call under Spoke 3 “Digitally-enabled secondary and tertiary prevention” of the DARE “Digital Lifelong Prevention” program (Project code MUR: PNC00000002 – CUP: B53C22006470001), an initiative coordinated by the Tor Vergata University of Rome, in turn funded by the Ministry of University and Research as part of the National Complementary Plan to the PNRR.
INsu-LINE is coordinated by Prof. Guido Iaccarino of the Department of Clinical Medicine and Surgery at the Federico II University of Naples.
The DARE Project
The DARE project is funded by the Ministry of University and Research (MUR) as part of the National Complementary Plan to the PNRR, with the objective of enhancing the potential of data to improve health promotion and prevention across the entire lifespan. It is an initiative coordinated by the Tor Vergata University of Rome that aims to harness data to improve health promotion and digital and community-based prevention throughout life. DARE achieves this objective by strengthening tools, knowledge and processes for the effective use of data to define, monitor and predict health trajectories. INsuLINE is an offshoot of the DARE project, more specifically of Spoke 3.
TRANSFORMING INTEGRATED CARE
THROUGH DATA AND AI
Building a Data-Driven Ecosystem for Integrated and Preventive Care
INsu-LINE is a research and innovation initiative aiming to enable integrated and preventive care through interoperable data infrastructures, advanced analytics, and AI-driven decision support tools. The project supports healthcare systems in identifying risk early, coordinating care pathways, and improving long-term outcomes.
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Interoperable Health Data Infrastructure
Development of secure, interoperable data environments enabling the integration of health and care information across settings, supporting continuity of care and advanced analytics.
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AI-Driven Risk Stratification and Predictive Analytics
Application of artificial intelligence models to identify high-risk populations, support clinical decision-making, and enhance early prevention strategies.
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Integrated and Preventive Care Pathways
Design and implementation of coordinated care pathways that promote prevention, multidisciplinary collaboration, and long-term health system sustainability.