Remote Patient Monitoring in the Edge-Cloud Continuum

🩺 Health
📍 Athens, Greece

About

Status:

✅ Finalized

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Athens needs a faster, more reliable way to integrate heterogeneous medical IoT devices into a single remote patient-monitoring service that keeps working under unstable network conditions.

Description of the Challenge

Context

Remote patient-monitoring services in Athens need to operate reliably outside hospital settings, integrating multiple medical IoT devices into a single, usable system for patients and care teams.

Specific problem

Integrating heterogeneous medical IoT devices — such as blood-pressure, oxygen-saturation, temperature and weight monitors — for remote monitoring has been complex and time-intensive to install, which slows deployment of these services, particularly where network connectivity is unstable.

Current situation and gap

Remote-monitoring setups are typically built device by device rather than through a unified integration approach.

Preliminary diagnostics need to stay available even when network conditions are unstable, which most current setups are not designed for.

Expected Outcomes

Specific need

Athens needs a faster, more reliable way to integrate heterogeneous medical IoT devices into a single remote patient-monitoring service that keeps working under unstable network conditions.

Expected outcome

– An edge-cloud integration approach that unifies data from multiple medical IoT devices into one monitoring service.
– The approach should keep core diagnostic functions available even when network connectivity is degraded.
– A single, integrated view of a patient’s readings from multiple devices, with core diagnostics still available if connectivity drops.

Who would use the results

Remote-monitoring care teams and patients, to track health readings reliably outside hospital settings.

Expected impact

A unified integration approach could reduce the device-by-device integration burden for care providers and support more reliable remote patient monitoring.

Space for Solutions and Experimentation

Space. Remote patient-monitoring deployments in the Athens area.

Scale. Household / individual-patient scale, within the Athens area.

Data. Device-level health data (such as blood pressure, oxygen saturation, temperature and weight) can be generated during monitoring; no unified baseline dataset of this kind existed beforehand.

Constraints. Deployment involves personal health data and requires medical-data protection, privacy, patient consent and cybersecurity safeguards, interoperability across different device types, and dependable diagnostic functions when connectivity is unstable.

The Pilot

No data was found
No data was found

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