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Remote Patient Monitoring: Early Warning Wearable Devices (EWWD) Project

Ewwd
The purpose of this project is to better understand what AI advances may be relevant today to persons with diabetes (PWDs), their clinicians, family, and caregivers for allowing the development of a predictive diabetes monitoring system using data from various measuring devices and other available data about the patient. Diabetes comprises over 12% of the world’s health expenditures, yet 1 in 2 persons remain undiagnosed and, as a result, untreated. The project is being delivered as part of the INTERREG FCE social innovation programme. Aside from creating new smart health solutions which will reduce the financial burden on state health systems by improving clinical efficiency and reducing clinical appointments whilst collecting more real-time data on patient conditions, the project also aims to:
These efforts will assist the medical community at large in providing better outcomes for everyone involved. Dydon is honored to be a part of this benevolent revolution. Here’s our impact:

The Challenge

“Enabling clinicians to devote more time to patient interaction and diabetes patients to receive optimal care in real-time”
Optimal care for persons with diabetes (PWDs) is often hampered by the absence of real-time, key health information necessary to make informed choices associated with intensive therapy and tight diabetes control. Although advances in technology offer unprecedented and inexpensive access to essential information for many individuals in many fields, its impact in the care of patients with diabetes seems rather limited. The challenges of real-time diabetes care information are compounded by the rapid expansion of medical knowledge.

The Solution

“Automation of routine processes and real-time key-factor information gathering and analysis”
Diabetes management, to a large degree, involves pattern recognition thus positioning it well for applications of AI. For example, key factors such as blood glucose, weight and blood pressure must be consistently measured and monitored to inform patient care.

By automating routine processes, clinicians can devote less time to data entry and more time to patient interaction. Patients may also gain an increased sense of control over their diabetes by having coaching tools and support at their disposal.

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