Andrew Borland, Head of Virtual Engineering Center 4.0 at the University of Liverpool, who currently runs a program for SME manufacturers in Cheshire and Warrington, explains the opportunities offered by Industry 4.0.
Over the past 18 months, we have witnessed massive global collaboration in life sciences and healthcare. As in so many other sectors, the pandemic has also catalyzed structural changes on an incredible scale. The adoption of processes and technologies has accelerated by necessity.
Effective vaccines that can take decades to develop have been unveiled in less than a year. A plethora of in vitro diagnostics (IVD) have also been designed, ratified and manufactured at record speed. At the same time, digital health tools to enable contact tracing have been launched on a large scale around the world.
Human ingenuity and resilience were rightly celebrated in the implementation of this innovation, but the supporting role played by emerging technologies must also be recognized.
Far from simply boosting the response to COVID-19, industrial digital technologies (IDT) have been essential to enable healthcare delivery, research and product development more broadly.
Clinical trials have shifted to remote models in many cases. Patients received care via telemedicine and remote monitoring with a fluidity that many would have thought impossible before the pandemic. Medical device manufacturers have accelerated the adoption of smart factory and production line technology to move from concept to market faster.
Data is underpinning almost every facet of global healthcare mobilization to meet the challenges of the past 18 months.
Backed by Industry 4.0 technology, our collective ability to capture, analyze and interpret huge datasets has enabled an answer that would have been nearly impossible just a decade ago.
The data has been described as the new oil in the machine, but it is also a raw material. When extracted, refined, and implemented correctly, its potential for medical technology is enormous. From personalized medicine and treatment planning to drug development and the design, prototyping and validation of medical devices and procedures.
Medtech is a highly regulated industry where quality, safety and efficiency are non-negotiable requirements. Understanding the importance of data science and data engineering is implicit for diagnostic developers and digital health companies. But makers of more tangible medical technology products also know they’re data-driven; receive it downstream from supply chains and produce it in their factories.
The challenges common to many of these companies, especially SMEs, are knowing what data is useful and how it can be combined with artificial intelligence, human knowledge and digital engineering tools.
What do I need to know? When should I know? How precisely do I need to know it?
These are three of the three key questions that almost all medical technology companies looking to formulate a data strategy should begin with. For manufacturers in particular, data is often captured in silos across different areas of production. Without the in-house skills to determine which of these data points is the most valuable, the sheer volume of data each can be daunting,
A lack of internal expertise, or high-level systems architecture, is often compounded by the interoperability of individual products.
Very few manufacturers have systems sophisticated enough to self-update in real time throughout the operation, based on the analysis of individual data entries.
The result may be to default to analog processes (pen and paper) to track overall production and make changes throughout the day. Not only is this inefficient, but it also breaks the digital ledger. This is where the major risk lies, especially for small and medium-sized companies in medical technology supply chains.
Many run the risk of being excluded from tenders by large companies or healthcare organizations, if they are not able to demonstrate a solid level of data mastery and transparent digital recording of production.
For medical technology to continue to evolve, it must learn from other sectors that have found the right recipe for collaboration between industry and regulators. Aerospace is a prime example. It is also highly regulated, with safety and quality considerations above all. Its companies and regulators easily share data in real time and the R&D process is thus streamlined.
Patients (or service users), industry, regulators, clinicians and payers need to find a common approach to data that everyone is happy with. Businesses will naturally want to protect intellectual property; patients should be reassured that the data will be anonymized; while regulators will demand unhindered access.
There will be trial and error to find this balance. But medtech innovation will ultimately be accelerated by better data capture, analysis, interpretation and, above all, sharing.