A new tool that helps target clinical trials to areas of unmet clinical need has sparked interest in both the National Health Service (NHS) and the commercial clinical research world. Stephen Lock, Head of Business Intelligence for the National Institute for Health Research (NIHR) Clinical Research Network, reveals why.

For over a decade the NIHR Clinical Research Network has been recording the clinical research activity in England and tracking its growth and expansion. Innovative approaches to how we use this data are now presenting new opportunities to improve research delivery and to help maintain the UK’s position as a key global destination for clinical research.

To understand why, we first need to understand the role of the NIHR Clinical Research Network. The easiest way is to think of it as the clinical research arm of the NHS. This unique government-funded research infrastructure is embedded throughout the entire NHS, reaching into both primary and secondary care. It provides the people, resources and support needed to carry out clinical research in the NHS with the ultimate aim of developing better treatments for patients.

Last financial year our organisation helped recruit over 725,000 participants into 4901 clinical research studies – which makes us the biggest recruiter of research participants in the world. We’ve been recording this type of research activity since 2006 so, as you can imagine, we are what you might call a “data-rich” organisation.

As with most organisations, our original purpose for capturing data was performance monitoring and management of resources. The organisation comprises 15 Local Clinical Research Networks which cover the whole of England. We also organise our work through 30 medical specialty areas including everything from ageing, cancer and diabetes, to respiratory, stroke and surgery – and many more inbetween. Comparing research activity in these different regions and categories certainly helps us to direct our resources to where they were most needed, look at what we have achieved, and look at where there might be room for improvement.

More recently we’ve found that the data we collect has proved an important asset in our public-facing campaigns which aim to help raise awareness of the importance of clinical research. For example, for the last seven years we have published NHS research activity league tables which enable members of the public to see exactly how much research is being conducted in their local NHS trust, be that acute, mental health or ambulance trusts.

As our Business Intelligence capabilities have matured we’ve found that the data that we collect is becoming ever more valuable. This data needs to be managed, maintained and interpreted by a wide range of stakeholders to drive research delivery and continuous improvement in the clinical research environment spanning the NHS, academia and the life-sciences industry. In 2013 we launched our Open Data Platform (ODP) which allows NIHR employees and other specific stakeholders to explore up-to-date study and recruitment data on their PC or mobile device. It’s a self-service business intelligence tool that increases visibility and improves the visualisation of data from separate information systems into simplified dashboards, or apps as we prefer to call them.

The different apps allow users to interact with high level or granular information to suit their information, analytical and reporting needs. This is useful for gaining insight and predicting what might happen in the future. But in 2018 we took the next logical step and we’re now looking at how our data can help our customers and partner organisations make decisions about what should happen in the future.

The real “Aha” moment came when a colleague produced a heat map of NHS research activity broken down by Clinical Commissioning Group (CCG) areas (these are clinically-led statutory NHS bodies responsible for the planning and commissioning of health care services for their local area). We’d already learned that looking at our data through different lenses gave us different insights, but the granularity of this view of our data resembled the granularity of disease prevalence heat maps and other similar data provided by the Office for National Statistics and Public Health England. This immediately begged the question: What would we learn if we directly compared disease prevalence to research activity in a specific specialty or condition?

What we discovered was a game-changer.

Several conversations ensued with the clinical community, life science industry and other partner organisations. The potential benefits were clear and development of a Research Targeting Tool was prioritised. Within six months we had a working prototype added to our suite of apps on our Open Data Platform.

Judith Holliday, Head of Research and Innovation at Mid Yorkshire Hospitals NHS Trust, is a big fan of the Research Targeting Tool. I first discussed the concept with her about six months before the tool was launched. She says: 

“Stephen initially came to me with an idea and the advantages of having access to the Research Targeting Tool, as he described it, were immediately obvious to me.

“Our annual process for identifying the Trust’s research priorities for the year ahead can be quite complex. We have to take into consideration a number of practical things such as ongoing research activity and the availability of research skills and experience. This helps us to establish where the capacity exists. But also we have to ask: Where is the clinical need? Which patients might benefit most from having opportunities to take part in clinical studies and gaining access to new treatments?

“My trust covers three sites: Pinderfields, Dewsbury and Pontefract. All these areas were previously home to collieries, and therefore have a large ex-mining population. Consequently, we currently have a high incidence of respiratory disease, for example COPD. Yet historically we have had very low levels of respiratory research – just one small commercially-funded study in the last 18 months. So I had a strong sense that there was capacity there, as well as an unmet clinical need.

“Once it was launched, the Research Targeting Tool enabled me be to back-up my intuition with hard evidence. I was able to visually demonstrate high incidence of respiratory conditions versus low levels of research in our region. It was the perfect conversation starter – a spark to ignite an interest and engage the respiratory clinical team in a discussion about bringing new research opportunities to our patients.

“From that starting point we are now supporting the respiratory clinical team to learn more about research and we are working with our local NIHR Clinical Research Network team to identify relevant clinical studies that we hope to be able to offer to our respiratory patients in the coming months.”

“The data is also invaluable in helping us to attract those studies. District General Hospitals often find it hard to compete with the bigger research centres yet, as the data demonstrates, there is a lot of untapped potential here in West Yorkshire. Having this type of information available so easily will help us to bring clinical studies to those patients that need them.”

Life science companies are also beginning to have similar conversations with us. During the autumn of 2018 we demonstrated the tool at a number of conferences and meetings and after each session my colleagues and I were inundated with questions from companies like Johnson & Johnson and Covance.

The app has the ability to compare up to four different maps at once. The example we normally use, and one that really illustrates the potential of the tool, is a map of diabetes research activity over the last three years alongside a map of diabetes disease prevalence. Immediately we can see that much of the research activity is focussed around large academic teaching centres centres such as Cambridge. However, the diabetes prevalence is actually quite low around Cambridge when compared to an area such as Bradford.

So why Bradford? Well, add in a data set from the Office of National Statistics showing demographic indicators such as ethnicity and there is your answer; Bradford has a higher than average population of people from South Asian origin who are genetically more prone to diabetes. But what if the focus of your study is diabetes prevention? Then perhaps you’d find a heat map showing hot spots of childhood obesity at school year six more useful? It’s all there, in fact there are currently 32 datasets available, and we are working on adding more.

Feedback so far tells us that the Research Targeting Tool has the potential to really help our partners in academia and the life science industry to place their studies intelligently. As a new addition to the NIHR Study Support Service, it certainly has become a conversation changer. We now have the ability to advise researchers how to plan their studies in areas where the patients exist and where the study has the best chance of successfully recruiting the required number of participants within planned timescales, while simultaneously meeting the clinical needs of the nation.