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Abstract:
It is no secret that the NHS is under immense pressure: waiting lists continue to grow, patients are waiting longer for care, and health outcomes are increasingly affected. At the same time, funding constraints leave little room for innovation and problem solving. Yet, the popularisation of AI outside of academia has created opportunities – small teams of Data Scientists are now commonly embedded within NHS hospitals, applying mathematical modelling and AI to healthcare challenges.
In this talk, we will share experiences of one such small team at Wrightington, Wigan & Leigh NHS Teaching Hospitals. We will describe a range of projects where mathematical modelling has been applied: developing a Digital Twin of a dermatology outpatient department, predicting patient deterioration, identifying patients at high risk of non-attendance to their appointment, using generative AI to aid clinical note taking, and using sentiment analysis to uncover patterns in patient mortality.
The emphasis on “small teams” is deliberate – collaboration with University and industry colleagues has driven some of the current work, filled in knowledge gaps, and helped ensure the highest standards of work. A primary strand of our efforts is improving access to high quality, research-ready, data through the adoption of synthetic data approaches, the OMOP (OHDSI) common data model, and the creation of a comprehensive Data Catalogue.
Bio:
Thomas Ingram was awarded a PhD in Molecular Biology from the University of Nottingham, in 2020, where he interrogated and worked to define the biochemical profiles of “healthy” ageing mitochondria in mammals. Subsequently, the COVID-19 pandemic drove a career shift toward statistics and data; he worked as Lead Data Scientist for the publicly funded COVID-19 RT-PCR testing facilities “Lighthouse Laboratories”, and is now the Principal Data Scientist at Wrightington, Wigan & Leigh NHS Teaching Hospitals. His primary areas of professional interest span the biological mechanisms of healthy ageing, the application of statistics and machine learning to practical problems in healthcare, AI safety, and improving access to healthcare data for research and collaboration.
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