Andrew Borland, Head of Commercialisation for the Virtual Engineering Centre explains the opportunities available through industry 4.0.
Over the past 18 months, we have witnessed mass global collaboration within life science and healthcare. As in so many other sectors, the pandemic has also catalysed structural change on an incredible scale. The adoption of processes and technologies has accelerated out of necessity.
Effective vaccines which can take decades to develop were unveiled in less than a year. A plethora of in vitro diagnostics (IVDs) were designed, ratified, and manufactured at record speed too. Running alongside this, digital health tools to enable contact tracing were launched at scale globally.
Human ingenuity and resilience have been rightfully celebrated in delivering this innovation, but the supporting role emergent technologies have played should also be acknowledged.
Far from just turbocharging the response to COVID-19, industrial digital technologies (IDT) have been pivotal to enabling healthcare delivery, research, and product development more generally.
Clinical trials shifted to remote-first models in many cases. Patients received care via telemedicine and remote monitoring with a seamlessness many would have thought impossible pre-pandemic. Medical device manufacturers accelerated the adoption of smart factory and production line technology to go from concept to market faster.
Underpinning almost every facet of the global healthcare mobilisation to meet the challenges of the last 18 months is data.
Supported by industry 4.0 technology, our collective ability to capture, analyse and interpret huge datasets has enabled a response that would have been nigh on impossible just a decade ago.
Data has been described as the new oil in the machine, but it is also a raw material. When extracted, refined, and put to work properly, its potential for MedTech is huge; from personalised medicine and treatment planning to drug development and the design, prototyping and validation of medical devices and intervention.
Medtech is a highly regulated sector where quality, safety and efficacy are non-negotiable requirements. Understanding the importance of data science and data engineering is implicit for diagnostics developers and digital health businesses. But manufacturers of more tangible MedTech products also know they are data-driven; receiving it downstream from supply chains and producing it within their factories.
Common challenges for many of these businesses, particularly SMEs, are knowing what data is useful, and how it can be paired with artificial intelligence, human insight and digital engineering tools.
What do I need to know? When do I need to know it? How accurately do I need to know this?
Those are three of three key questions almost any Medtech business looking to formulate a data strategy should start with. For manufacturers, in particular, data is often captured in silos across individual areas of production. Without the in-house skills to take a helicopter view of which of those data points is the most valuable, the sheer volume of data from each can be daunting,
A lack of in-house expertise, or top-level systems architecture, is often further compounded by the interoperability of individual products.
Very few manufacturers have systems that are sophisticated enough to update autonomously in real-time across the entire operation, based on analysing individual data inputs.
The result can be to default to analogue processes (pen and paper) to keep track of overall production and to make within-day changes. Not only is this inefficient, it also breaks the digital ledger. Therein lies the major risk, especially for smaller and mid-sized businesses in MedTech supply chains.
Many run the risk of being ruled out of tenders by larger corporates, or health payers if they are not able to demonstrate a solid level of data literacy and a transparent digital record of production.
For MedTech to continue to evolve, it must learn from other sectors which have found the right recipe for collaboration between industry and regulators. Aerospace is a prime example. It too is highly regulated, with safety and quality foremost considerations. Its businesses and regulators share real-time data readily and the R&D process is streamlined as a result.
Patients (or service users), industry, regulators, clinicians, and payers, must find a common approach to data that satisfies everyone. Businesses will naturally want IP protected; patients must be reassured that data will be anonymised; while regulators will demand unfettered access.
There will be some trial and error in finding this balance. But MedTech innovation will be ultimately accelerated by better capture, analysis, interpretation and, crucially, sharing of data.
