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Research Team: Dr Pantelis Gerogiou, Dr Luke Moore, Dr Pau Herrero, Dr Esmita Charani, Dr Timothy Rawson, Mr Bernard Hernández, Ms Juliet Allibone, Prof Chris Toumazou, and Prof Alison Holmes.

EPIC IMPOC is an NIHR i4i funded project which aims to develop intelligent clinical decision support system to help doctors prescribe the most appropriate antibiotics. EPIC IMPOC is a collaborative project between medics and other heathcare professionals from the  National Institute for Health Research Health Protection Research Unit (NIHR HPRU) and engineers from CBIT.

Healthcare professionals who diagnose and treat infections must often do so rapidly to prevent harm to their patients.  Their prescribing decisions can be assisted by providing them with access to treatment recommendations, based on the most likely organism causing the infection (antimicrobial guidelines) and data on local antimicrobial resistance patterns.  

These decision support systems are mostly rule-based, providing easy-to-access policies or guidelines. A new project started by the Faculty of Engineering and Medicine kickstart scheme, and now funded by the NIHR Invention for Innovation scheme has taken this further, developing anintelligent decision support system. This system is capable of considering a greater number of variables and a more complicated number of scenarios than other systems and by using a machine learning  and artificial intelligence algorithms the system is able to continually learn from its previous experiences.

The project team aim for the resulting tool to have:

1. The ability to display data from NHS servers on mobile and tablet devices at the patient bedside

2. A unique atificial intelligence/machine learning algorithm, developed in-house to provide advanced, intelligent decision support for clinicians for optimised antimicrobial choices

3. A population pharmacometric model to provide individualised antimicrobial dosing for individual patients

4. A patient engagement tool to facilitate shared decision making across a range of complex healthcare pathways

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