Medical Technology UK took place in Coventry, England this year at the world-famous Coventry Building Society Arena. The event attracts the best minds in medical device engineering. In this special “hot tech” section, we highlight exciting research, people, and technology shaping the future of the industry. We caught up with Steven Bagshaw, Head of Business Strategy, CPI.
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Q&A With Steven Bagshaw, Head of Business Strategy at CPI
Bagshaw spoke on challenges in commercializing next-generation wearables at MedTech UK, focusing on the convergence of MedTech and consumer electronics. This Q&A has been lightly edited for clarity.
Q: What barriers are you seeing as the line between wearables and MedTech blurs?
Most wearables on the market are ‘HealthTech’ – i.e. for indication only and not producing medically valid data. MedTech requires any output from wearables to be clinically validated against the gold standard. A good example of this is the use of photoplethysmography (PPG) to derive blood pressure. There are now devices on the market that claim to measure blood pressure, but until these are validated against clinically accepted methods, they will not be accepted by governing bodies, such as the British and Irish Hypertension Society.
Getting consumers to accept the cost/functionality difference between ‘HealthTech’ and ‘MedTech’ will always be more expensive due to the additional documentation/standards to be in compliance with. Also, testing, traceability, and/or manufacturer ISO 13485 accreditation.
There seems to be more of a focus on wearables for the mass market, rather than dedicated MedTech R&D to provide the tools needed in non-invasive preventative medicine.
The MedTech R&D community generally has very poor interaction levels with end-users, whether they are patients, clinicians, or a mixture. This does not allow for high-quality user-centered design to be carried out, whereas for the mass market and ‘HealthTech’ the designers have easy access to large sample groups, particularly in the sports/exercise industries.
There are many areas of wearable that are almost impossible for smaller companies to compete in (here and here) so they not only have to find opportunities outside of these but also face the challenge of having to try to develop MedTech based on a much smaller amount of previous research.
Q: What low-hanging fruit exists in the wearable and MedTech space?
A few things here:
- Wearables to improve compliance with the taking of prescription medication, which in turn, reduces relapses and complications.
- Wearables to support mental health – everything is geared to physical health. For many, physical health monitors lead to a deterioration in mental health because they often feel like they’ve failed when they don’t ‘keep up’ with their goals, and so give up completely.
- Wearables to support the aging population e.g. enhanced haptic feedback devices for those with reduced feeling, memory support devices.
- Patient safety devices – could include checklists, automated recording of things, etc. to particularly assist those in vulnerable groups and their communication with their clinical teams.
Q: What should OEM engineers and R&D departments be investing in right now?
We should understand the psychology of wearable tech users, covering everything from ‘enthusiastic fitness monitor wearer’ to ‘patient with chronic illness in constant pain’. This also fits with the mental health wearable development.
- Methods/technology for early detection of infectious diseases
- More focus on technology to support the prevention of disease using non-invasive methods – could either be for the general populace, or for people classed as high risk for something due to family history, genetic predictors etc.
- Development of AI for automated interpretation of e.g. lateral flow tests, immunofluorescent staining tests, OCT images of the eye (it’s already been shown that AI is more accurate than about 95% of clinicians for this – anything that has well-defined features within an image is a candidate. Not so useful for things like lesion/tumour detection where features are often indistinct or are very variable).