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Research Theme

Translational Data Science

You are here: Home > Research Overview > Translational Data Science > Sub-theme 1: Healthcare observation and transformation

Sub-theme 1: Healthcare observation and transformation

Led by Professor Jim Davies and Professor Eva Morris

Our researchers are looking at how we can use patient data to drive improvements in care and outcomes across the NHS, while ensuring that it is handled appropriately. To tackle common conditions like cancer and cardiovascular disease, we need to work at scale and look at data across large populations.

A major part of our work is concerned with building platforms that can handle large amounts of data securely, balancing the need to protect patient confidentiality while still having enough information to make them useful.

The regional and national patient data we compile will provide the basis for robust epidemiological studies and help us to address the big challenges facing the NHS.  We will use them to generate evidence that will drive improvements in care, in different hospitals, from diagnosis to treatment to outcome. They are also vital in helping us to monitor social and geographical inequalities.

With a view to improving cancer outcomes, our researchers are working with the Thames Valley Cancer Alliance and the NIHR’s Health Informatics Collaborative cancer network to optimise pathways, focusing on patients who present with non-organ-specific symptoms or are diagnosed outside the referral process.

We are also leading, jointly with Imperial, a federated analytics initiative that would ‘unlock’ the NHS’s electronic patient records to support research. The initiative is developing algorithms to predict falls, inpatient hypoglycaemia and sepsis, as well as developing machine-learning techniques for patient feedback.

We are leading large national informatics collaborations dealing with colorectal cancer and hepatitis, and offering data mapping support for collaborations in cardiovascular and renal medicine, critical care, diabetes and children’s surgery.

Translational Data Science

  • Translational Data Science
  • Sub-theme 1: Healthcare observation and transformation
  • Sub-theme 2: Deep phenotyping
  • Sub-theme 3: Translational genomics
  • Sub-theme 4: Clinical trials and evaluation
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