The MHRA has published it’s data strategy, defining the regulator’s vision for how data, digital technology, and Real-World Evidence (RWE) can be used to deliver their vision of ensuring the UK is a global leader in healthcare innovation.

Recognising the opportunities of the UK health data landscape, alongside the enormous challenges of fragmented digital technologies across the NHS, the MHRA Data Strategy sets out five core themes. 

Key in these are the recognition that frameworks need to be in place to let innovators use Real-Word Evidence (RWE) to underpin regulatory processes and that all parties need to understand how they can safely exploit AI to improve processes from product development through to post market surveillance.  

Theme 1: Support data-driven innovation, early access, and interdisciplinary data science to underpin our regulatory framework

The MHRA, like other global regulators, recognises that industry needs to understand how RWE can be used to support regulatory processes  if innovative products are to reach patients. Potential issues with randomised clinical trials including sample sizes, representative patient groups and duration are often more prevalent with novel, digital products.  Theme 1 attempts to provide clarity on regulation to enable innovators to develop and apply new approaches to collecting RWD and generating quality evidence. 

The data strategy identifies opportunities for RWD to impact regulation throughout the project lifestyle, in processes from defining disease epidemiology and informing trial design to post-authorisation safety and effectiveness studies.

The MHRA plans to do the following to deliver Theme 1:

• Producing guidance documents on RWE, engaging with stakeholders and international regulators to improve global harmonisation of goals and standards.
• Develop a RWE Scientific Dialogue Programme to ensure regulatory expectations are clearly communicated to innovators.
• Pilot a Data, Methodology and Endpoints Qualification process to support innovative strategies throughout the product lifecycle. 
• Understand and describe patterns of underrepresentation in clinical research. This is to ensure processes are inclusive, improving equity of access.
• Deliver clear routes for RWD to support early access to medicines and devices whilst upholding safety through proactive post-market surveillance. 

Theme 2: Enable effective, timely, and proportionate regulatory decision-making through Real-World Evidence

The data strategy identifies some key issues in using RWD to support regulatory decisions, including harmonised standards, representative data and duration of data collection. These issues are compounded by challenges in the NHS arising from the fragmentation of technologies across regions and between care settings. 

A key tool in this will be the MHRA’s research service, the Clinical Practice Research Datalink (CPRD), which makes available anonymised data from 65 million patients from GP practices across the UK.

The MHRA plans to do the following to deliver Theme 2:

• Evaluate the role of common data models and federated analytics to generate RWE in the UK data ecosystem. This aims to allow the MHRA to address regulatory questions in a timely manner with a robust understanding of the underlying science.
• Scope opportunities to expand and improve data linkage across care settings and data sources to generate actionable evidence for medicines and medical devices.
• Deliver necessary secondary legislation and associated guidance around medical device identification to support traceability across the device lifecycle.
• Map, evaluate, and strategically engage with major device and outcome registries, identifying opportunities to improve safety activities by leveraging RWD.

Theme 3: Develop, extend, and integrate our capabilities in data and digital technologies

The MHRA intends to invest in its people to ensure it has the relevant expertise to address data requirements. 

The MHRA plans to do the following to deliver Theme 3:

• Establish a Cross-Agency Data Science network to share expertise, encourage collaboration and foster innovation.
• Identify opportunities for digital and data-focused graduate placements. 
• Conduct a Data Maturity Assessment programme to evaluate internal data assets.
• Implement a data architecture to gain a comprehensive view of medical products and achieve ‘collect once, use many times’ best practices for data management.

Theme 4: Establish, embed, and expand synergistic partnerships across the data ecosystem

The MHRA stresses the value of partnerships with academic institutions and medical organisations, both in UK and internationally, to achieve the strategy’s aims. This network will provide evidence and methodologies to assist regulatory decision making, supporting innovative product development and early market access.

The MHRA plans to do the following to deliver Theme 4:

• Use Centres of Excellence in Regulatory Science and Innovation to deliver progress in data science. 
• Support international harmonisation of terminology, nomenclature, and data standards by working collaboratively with partners such as ICH and IMDRF.
• Establish partnerships with academic institutions to develop novel analytical methodologies.
• Collaborate with partners across the UK to ensure collection and integration of decision-ready regulatory medical product data.

Theme 5: Safely and responsibly harness the potential of artificial intelligence and advanced analytics throughout the product lifecycle

The MHRA recognises the huge potential for AI and ML to assist with making data-driven decisions. 

The data strategy highlights that a cautious approach is needed when employing these technologies in the Healthcare sector. For example, uncertainty and reproducibility of outcomes must be considered and quantified. Grappling with this, the MHRA intends to focus on the quality of the data used to train algorithms, and ensure clear accountability where these tools are used. 

The MHRA plans to do the following to deliver Theme 5:

• Establish a task force to explore the potential of Generative AI and LLMs to improve regulatory processes. 
• Evaluate the use of natural language processing to improve pharmacovigilance. 
• Investigate the role of advanced analytics including causal inference and AI/ML, for the analysis of RWD and generation of RWE to reduce ambiguity in benefit-risk evaluation.
• Evaluate the potential of novel methodologies to support adverse event signal detection. 

If you have any questions about the regulation of medical products or market access, please contact our Healthcare team. This article was written by Rory Trust and Zoe Williams.