Researchers at UCL and King’s College London are training an artificial intelligence (AI) on a set of NHS data for 57 million people in England
Foresight is a generative AI model that learns to predict what happens next based on previous medical events and NHS data. It works similarly to ChatGPT, which predicts the next word in a sentence based on what it’s seen previously from data across the internet.
The researchers are training Foresight on routinely collected, de-identified NHS data, like hospital admissions and rates of COVID-19 vaccination, to predict potential health outcomes for patient groups across England. This could be events such as hospitalisation, heart attacks, or a new diagnosis.
Predicting future medical events could enable targeted intervention and support a preventative healthcare approach.
Analysing NHS data from 57 million people in England
The pilot study uses data from the NHS England Secure Data Environment (SDE), a secure data and research analysis platform, providing controlled access to de-identified NHS data from 57 million people in England.
The study includes data covering England’s entire population to allow the model to predict health outcomes across all demographics and for rare conditions.
Lead researcher Dr Chris Tomlinson (UCL Institute of Health Informatics) said: “AI models are only as good as the data on which they’re trained. So, if we want a model that can benefit all patients with all conditions, then the AI needs to see that during training.
“Using national-scale data allows us to represent the kaleidoscopic diversity of England’s population, particularly for minority groups and rare diseases, which are often excluded from research.”
Identifying high-risk patient groups and improving care
The researchers believe the model’s predictive power could pinpoint high-risk patient groups, opening up a window of opportunity to intervene to improve and save lives. The diverse NHS data could potentially highlight and address healthcare inequalities and analyse healthcare risks and outcomes at population level, offering critical support to the NHS.
Looking towards the future, the researchers would like to train the model further on deeper NHS data sources and explore how to expand the scope of the model responsibly.
Simon Ellershaw, a PhD researcher at UCL Institute of Health Informatics, said: “Combining the computing resources needed for AI with NHS data has always been challenging, but thanks to the support of our partners we’ve been able to safely and securely apply state-of-the-art AI methods to NHS data at unprecedented scale.”
Lead researcher Professor Richard Dobson, based at UCL Institute of Health Informatics as well as King’s College London, who is also Deputy Director of the NIHR Maudsley Biomedical Research Centre, said: “This pilot is building on previous research that demonstrated Foresight’s ability to predict health trajectories from data from two NHS trusts. Using it in a national setting is very exciting as it will potentially demonstrate more powerful predictions that can inform services nationally and locally.
“Currently the data in this pilot is broad but shallow, and ultimately we’d like to harness the expertise and AI platforms behind Foresight by including richer sources of information like clinicians’ notes, or results of investigations such as blood tests and scans if they become available.”
Dr Vin Diwakar, National Director of Transformation at NHS England, said: “AI has the potential to transform the way we prevent and treat disease if trained on large datasets and safely tested. The NHS Secure Data Environment has been fundamental to this pioneering research, shaping a future where earlier treatments and interventions are targeted to those who will benefit, preventing future ill health. This will boost our ability to move quickly towards personalised, preventative care.”
