If you are a wearable health tech developer dealing with fragmented patient data — this project developed an integration of IoT and edge devices that enables interoperable, patient-centred services. This allows devices to communicate directly with emergency medical units across 6 countries.
AI and IoT System for Emergency Medical Coordination and Remote Patient Monitoring
Imagine a digital safety net that connects ambulances, hospitals, and patients instantly during a crisis. It uses smart wristbands and AI to tell doctors exactly what is happening to a patient before they even arrive at the ER. This helps cities handle sudden surges of patients without the system crashing.
What needed solving
Healthcare systems struggle with unplanned patient surges and fragmented data during crises. This leads to service disruptions and poor coordination between emergency units and primary care.
What was built
A list of tools and healthcare services for urgent care providers, including AI algorithms, smart handheld devices, and telemedicine platforms.
Who needs this
Who can put this to work
If you are a telemedicine platform provider dealing with inefficient surge management during crises — this project developed AI-powered integrated healthcare systems. These tools allow for proactive interventions and better resource optimization during unplanned patient surges.
If you are an EMS operator dealing with poor coordination during cross-border emergencies — this project developed smart handheld devices for emergency personnel. These tools improve the continuity of care and reduce service disruptions during crises.
Quick answers
What is the cost or pricing model for these tools?
Based on available project data, specific pricing or cost models for the resulting tools are not provided; the project is funded by a EUR 5,345,250 EU contribution.
Can this be scaled to an industrial level?
The project is designed for scale, validating three different patient care scenarios across 6 countries to ensure the tools work in diverse national contexts.
Who owns the IP and how is licensing handled?
Based on available project data, specific IP and licensing agreements are not detailed, though the consortium includes 4 SMEs and 6 industry partners to support technical development.
How does this integrate with existing health records?
The system is designed to align with the European Health Data Space (EHDS) to ensure interoperability and data integration.
What is the timeline for deployment?
The project runs from 2024-01-01 to 2026-12-31, with pilot validation occurring across the 36-month period.
Who built it
The consortium is heavily weighted toward commercial application, with a 50% industry ratio consisting of 6 industry partners, including 4 SMEs. This balance between 5 universities and 6 industry players suggests a strong focus on moving research into usable products. The geographic spread across 10 countries, including the UK and Israel, provides a broad market validation base for the AI and IoT tools.
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Contact us to explore licensing opportunities for the AI-enabled emergency tools.