The software, known as DECODE, helps to identify which patients are most likely to benefit from a full dementia assessment

£73,000 funding to drive forward dementia diagnosis technology

A dementia diagnosis software system developed at the University of Exeter has received three grants totalling £73,000 to continue developing the technology to improve identification of dementia earlier.

The software, known as DECODE, helps to identify which patients are most likely to benefit from a full dementia assessment, improving accuracy and identification of the condition. A trial to assess its implementation into the NHS is due to take place later this year.  

The grants come from the Engineering and Physical Sciences Research Council Impact Acceleration Account (EPSRC IAA) and the University of Exeter Innovation, Impact and Business Commercialisation Fund. The grants were won by Alice Garrood, Research Fellow at the University of Exeter Medical School.

Alice said: “The grants have been essential in helping to move the DECODE project forward and get the necessary expertise to complete the next stage of the trial. We’re really excited to see the results and continue developing DECODE. It has huge potential to improve patient care, clinician experiences and offer economic savings.”

Professor David Llewellyn has led the development of DECODE with Janice Ranson. Professor Llewellyn is also the Exeter Institute for Data Science and Artificial Intelligence Clinical Theme Lead.

Professor Llewellyn of the University of Exeter Medical School said: “These grants are crucial to allow us to take DECODE to the next level and move towards clinical impact. Securing these grants is a fantastic achievement for Alice and the DECODE team and will make an enormous difference.”

Dr Laura Hill, Consultant Psychiatrist from Devon Partnership NHS Trust and partner on the EPSRC IAA, said “The DECODE technology is extremely promising and has the potential to transform dementia assessment. It is a simple, time-efficient technology which supports clinical decision making and we are keen to implement it as soon as possible.”

The funding will also support the team’s work with York Health Economics Consortium to develop an early cost-effectiveness model of DECODE’s implementation into the NHS.

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Date: 3 March 2020