Whilst early warning scores help to identify patients who are deteriorating in the resource-limited ward setting, there is a need for a system that can identify patients at risk of complications so that clinical contact and investigations can be prioritised prospectively.
This burden falls disproportionately on the elderly: consequent socioeconomic costs and ageing demographics make improving post-operative outcomes a significant public health consideration. Stratification of patients for post-operative intensive care has received much attention, but many perioperative complications occur later.
My project will use de-identified perioperative episodes (CUH has >20,000 historical high-risk patient episodes) to build dynamic prediction models for perioperative complications by day.
I will establish models to better describe and predict from typical patient profiles classes and attempt to better understand the impact of complications on length of stay. These will be used to create better dynamic risk predictions to guide resource allocation and inform shared decision making.
The key to digital transformation is implementation. The key to implementation is broad buy-in. The key to buy-in is education and understanding.
I am a research-active anaesthetics and intensive care consultant at Cambridge University Hospitals NHS Foundation Trust.
Before medicine, I obtained a PhD in experimental physics, gaining expertise in computer modelling, mathematics and analysis of complex data.
My research has focused on digital biomarkers and applications of statistical and machine learning to ICU and perioperative routinely collected data.
I am the chair of the European Society of Intensive Care Data Science section which I helped to set up and am a Founding Fellow of the Institute of Clinical Informatics.