UMBRELLA is a holistic approach to progress, reshape, and benchmark the overall stroke care pathway and set new and improved standards of care in terms of primary and secondary prevention, rapid access to treatments, early accurate diagnosis, stratification, management and real-time monitoring, therapeutic targets identification, and rehabilitation, recurrent stroke and related cardiovascular events. This innovative approach will transform healthcare systems by improving and harmonizing professionals' workflows in a more patient-centred, digitalized, and communicative manner. UMBRELLA aims to revolutionize stroke management by implementing a comprehensive approach that addresses gaps along the whole continuum of the stroke care pathway. The key paradigm in the project is the multicentric, synergistic "umbrella" strategy for local data collection, harmonization, and standardization along the entire pre-, in-, and post-hospitalization pathway. By establishing specific common data models (CMDs) implemented in each of the 7 top-tier European clinical centres, UMBRELLA will create a federated data platform (U-platform) where Real World Data (RWD)-based AI algorithms can be locally created and validated, to advance personalised diagnosis, risk prediction, and treatment decisions in the acute and post-acute phases of stroke. The algorithms will be then trained in a decentralized manner through a federated learning infrastructure (FL-platform), which preserve data security and privacy, avoiding data centralization or exchange across centres but fostering collective AI-models training. On the other hand, standardized stroke management protocols and procedures will be created and implemented across the participating centres, including the validated usage of advanced digital technologies as solutions to facilitate data collection, visualization, patient engagement, monitoring, outcomes integration, and decision-making across the whole stroke pathway.

Cardiorespiratory diseases and stroke are among the top four leading causes of death in the EU in prehospital settings. Urgent care services are in huge need of novel Point of care (POC) computing technologies that can specifically detect patient condition and enable Hospital information system (HIS) with real-time data for triaging patients for right care. Our goal in POC4TRIAGE is to develop robust and accurate POC technologies, from POC testing (devices) to POC systems (platform) that is capable of fast diagnosis and efficient transfer of data to HIS. We will develop and clinically validate four rapid (<10 min) easy-to-use, compact, cost- and energy-efficient POC devices with Edge AI computing models, to be used in ambulance & emergency room settings. POC4TRIAGE devices include a multimodal patch for real-time monitoring of cardiorespiratory data, novel sub-hairline non-invasive EEG based head caps for rapid stroke diagnosis, including detection of large vessel occlusion stroke, and a handheld, rapid immunodetector to diagnose stroke with clinical utility for various conditions. These devices integrate into a new Device Hospital Connectivity Platform (DHCP) that visualizes data, uses AI from multiple devices to triage and seamlessly integrates with hospital systems and clinical workflows. The POC devices and DHCP will be clinically validated. POC4TRIAGE brings together some of Europe's leading POC device developers, medical professionals and clinicians, patient representatives, ethics experts, data scientists, and health economists. POC4TRIAGE will shorten the time to treatment and improve clinical outcome. POC4TRIAGE has potential to revolutionize healthcare delivery, making it more accessible and efficient, traceable, and interpretable for patients and providers alike. As the POC device and computation market is growing fast, the new POC devices, real-time data analysis, and secure computing have potential for major economic impact.