To develop methods to determine the causes of variations in clinical staff performance and mitigate their impact on healthcare safety and efficiency
A significant obstacle to high quality performance of healthcare services (the NHS in the UK) is the presence of large variations in team performance, which can lead to inefficient usage of hospital resources and even harm to patients. Different clinical teams perform differently due to a number of factors such as clinical knowledge and skills, teamwork, resources, environment, etc. Cost savings of £5bn per annum could be achieved in the NHS if most hospitals could match the performance of best performing teams.
Automated analysis of performance of clinical teams is necessary in healthcare, and a solution to this would provide a step change in performance monitoring, enabling clinicians to reduce error, improve process outcomes and staff productivity. These are key goals in the current times when healthcare needs are expanding, becoming more complex, and resources are increasingly limited.
This is an exciting multidisciplinary project with supervisory expertise in healthcare processes, reliability engineering and computer vision. The project will develop methods for probabilistic modelling of causes and consequences of variations in clinical staff performance. Clinical process simulations will be used to evaluate reliability and efficiency of outcomes when a number of different variations happen. Knowing when an undesirable variation has occurred will help to predict its influence on outcomes, to search for the best recovery action and respond to the situation in real-time. The method will be potentially applied to the field of aseptic techniques to minimise the risk of infection, and processes carried out in an operating theatre.
Starting October 2020, we require an enthusiastic graduate with a 1st class degree in engineering, science, maths or a relevant discipline, preferably at Masters level, or an equivalent overseas degree (in exceptional circumstances a 2:1 degree can be considered).
This studentship is open until filled. Early application is strongly encouraged.