EWWD Project

Early Warning Wearable Devices

HexTransforma Healthcare is leading this social and economic development project which will:

  • Introduce a remote monitoring wearable device to enable healthcare professionals to monitor patients outside of a hospital environment and improve patient outcomes
  • Provide opportunities for young people in both the UK and France to improve their employability and social mobility through engagement with medical technology innovation and by working alongside the project delivery team

The EWWD (Early Warning Wearable Devices) project is being delivered as an Innovation programme with Interreg France (Channel) England and is co-financed by the European Regional Development Fund.

The Interreg France (Channel) England programme aims to fund a number of high-quality cooperation projects between France and the UK. Find out more on their website

HexTransforma’s EWWD project will run for 34 months from July 2020 till May 2023.

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Programme Summary

EWWD (Early Warning Wearable Devices) is a machine learning, AI driven platform, with a remote monitoring system, to support patients with Musculoskeletal Disorders and Types I and II Diabetes. The platform combines AI and gait analysis to deliver cutting-edge patient care. EWWD presents the opportunity to remotely triage patients prior to any surgical intervention as well as monitoring them post-operatively, for any complications or infections.

The EWWD project also encompasses two subprojects: EWWD Academy and EWWD MedTech Challenge, which will run simultaneously as the EWWD device is being deployed. The two subprojects directly contribute to the employability and social development goals.

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Project Aims

  • To create new smart health solutions which will relieve the burden on state health systems by improving clinical efficiency and saving money, clinical resources and patient lives.
  • To address the decline of prominent industries across the FCE area which will only be further exacerbated due to Covid-19. Prior to the pandemic, there were already high levels of youth unemployment of 27.61% in the France FCE area alone (EuroStat 2018).
  • To encourage investment in the Medical Technology sector which currently is growing steadily at around 5.5% per annum.
  • To increase social mobility and employability by offering young people from disadvantaged backgrounds the opportunity to develop skills in this growing smart technology environment.
  • To develop employment aspiration in young people by involvement in the Schools MedTech project and creating opportunities in the rapidly developing Medical Technology sector.

Funding

The project will be co-funded by the Interreg France (Channel) England programme with a European Regional Development Fund contribution of €7.4M.

FCE

Early Warning Wearable Device (EWWD)

EWWD (Early Warning Wearable Devices) is a machine learning, AI driven platform, with a remote monitoring system, to support patients with Musculoskeletal Disorders and Types I and II Diabetes. The platform combines AI and gait analysis to deliver cutting-edge patient care. The remote monitoring solution will improve the accuracy of treatment plans, providing better care while allowing the patients to remain active outside of the hospital.

The project delivers a capability to enable a clinician via the SaaS AI desktop, in real time to remotely monitor an unlimited number of patients using wearable devices and communicate with them directly to schedule appointments based on necessity. The 2020 Covid-19 pandemic demonstrated the need for remote monitoring solutions to allow clinicians to monitor patient symptoms within an outpatient context. The ability to securely monitor patients’ symptoms remotely creates an interoperable healthcare ecosystem.

The innovation reduces pressure on clinical resources, reduces costs, has a positive environmental impact through the reduction of travels and car journeys to attend clinical appointments (approx. 48M per year) and, finally, delivers significantly improved patient pathways and outcomes.

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