Michael King, Senior Director, Product & Strategy, Technology Solutions at IQVIA talks about the future of quality and regulatory in the MedTech industry.
Increasing numbers of regulations and standards are driving complexity in the MedTech industry. Within that complexity is the continuous evolution and divergence of global regulations, which, alongside the rapid adoption of technologies such as artificial intelligence (AI), including Generative AI (GenAI), present a challenging and interesting path forward for quality and regulatory teams.
In an ideal world, MedTech quality and regulatory standards would be harmonised globally, but that is not the case at present and is unlikely to occur any time soon when the drivers of politics and economics are considered. Therefore, the industry must find a way of successfully operating within a complex global landscape and look to ‘control the controllables’ that are truly within their influence.
Evolving Regulations
The regulatory world is always in motion and can often step outside of traditional boundaries. This is due to the identification of new therapeutic
areas and the advancement of product solutions that often combine a range of product types, risk classes and steps across the medical device and pharmaceutical arenas. The complexity is further compounded by the need to meet global data and privacy requirements.
One of the biggest drivers seen over the last year is the advancement of regulation that targets the use of AI and other advanced technologies in MedTech. These inputs must be considered when products are being developed for both local and global markets. Consequently, regulatory experts within a company now have an important ‘seat at the table’ when it comes to both hardware and software design, defining the boundaries around regulation and relating those back to commercial imperatives and the ability to predictably define market access dependencies and timelines.
The era of connected devices also presents an opportunity to leverage intelligent automation through AI and GenAI. These technologies can significantly enhance and bring new capabilities to quality and regulatory operations by supporting the augmented user. They support activities such as the drafting of core documentation such
"One of the biggest drivers seen over the last year is the advancement of regulation that targets the use of AI...
as regulatory submissions, audit responses or adverse event reports and the identification of signals to proactively assess product quality, production and safety trends. The tracking of product performance and production activities in real time, with AI supporting the augmented user, can greatly improve process and production controls and post market surveillance (PMS) activities to ensure enhanced compliance and optimised performance.
An augmented user can use AI to combine and synthesise data from multiple sources into unified evidence packages for human review. GenAI can further streamline the process by generating high-quality regulatory documents such as submissions, reports and labeling content and, by reviewing precedent information within a company’s quality and regulatory ecosystem, it can highlight areas in the written document that need enhanced scrutiny. This not only benefits companies by saving time but also allows regulators to readily assess product functionality, performance data and real-world outcomes, saving them valuable resources.
Enhanced Safety and Quality
Patient safety remains the top priority, but MedTech operates under the concept of acceptable risk. Destructively testing every medical device before it is released to the public is clearly impractical as no product would be available for commercialisation. However, the use of GenAI can significantly improve patient outcomes by supporting both the generation of product risk documentation and the identification of product failure modes and associated risk mitigation plans.
Additionally, through analysing aggregated device performance and patient data, GenAI can build predictive models to forecast potential safety risks and device failures before they occur. Advanced technology can flag trends occurring at a rate outside of the threshold defined in the product documentation. Generative models can continuously scan published medical literature, clinical studies, social media and call center audio files to detect potential adverse events and product quality issues for medical devices in
real-time. These models accelerate post-market safety intervention and potentially reduce the impact of any needed regulatory field action.
In Closing
As products become more intricate, managing go-to-market activities in a cost-effective manner presents a growing challenge. While automation and AI are poised to play a significant role, MedTech organisations recognise that AI has limitations for some quality and regulatory activities due to constraints like data, legal concerns regarding privacy of proprietary information, validation of the use of such AI driven tools and a company’s cost in using and maintaining such systems.
The excitement surrounding GenAI is undeniable, and prioritising feasible and practical applications over a onesize-fits-all approach is crucial. Success depends upon leading with a customer centric approach – (i) start with the quality and regulatory customer problem statement and/ or opportunity; (ii) identify what processes and decision points are mandated by global regulations and standards; (iii) pinpoint what data sets and data structures apply to these quality and regulatory processes, and (iv) begin solutioning in a way that best suits the organisation.
Effective outcomes stem from enhancing human-technology interfaces and utilising AI responsibly to augment quality and regulatory professional expertise. Strategic technology deployment, supported by robust processes, is the key to unlocking the full potential of AI in this domain. Ultimately, the use of AI to enhance human to human interactions improves the provision of safe and effective product solutions in all global markets.
As products become more intricate, managing go-to-market activities in a cost-effective manner presents a growing challenge.
