Oxford Technology Showcase
Dr Cui was invited to talk at the Oxford University Innovation, Oxford AHSN & NIHR Oxford Biomedical Research Centre Technology Showcase on 6 July 2016. Her presentation 'Working towards a personalised surgical pathway' is available to view on the Oxford University Innovation website.
BM BS (Hons), BMedSci, MRCS
I am a clinical researcher, with a urology background, working as a staff member within the Nuffield Department of Surgical Sciences, University of Oxford.
I am collaborating with our industry partner, McLaren Applied Technologies, to research how to improve the patient preparation, and flow, through the surgical pathway in order to improve patient experience and outcomes from surgery. We are developing a system of remote monitoring and personalised patient feedback that can be integrated into, and enhance the care of surgical patients.
On average, a person will have four operations in their lifetime. The pathway from initial contact through to the postoperative recovery period is complex and can be confusing. Many factors along the pathway will affect patient experience and outcomes. Most of the research to date has been focused on how to improve the 'hands on' care for patients by doctors in hospital. My research investigates ways of using innovative technology to involve patients in their entire surgical pathway in order to provide better care. We have used wearable, remote activity monitoring technology, that is widely available commercially, to add value to the care of surgical patients in the NHS by assessing their fitness levels before and after an operation. Our findings will help doctors be better informed of a patient's health status and provide a means for patients to optimise their health before an operation.
I am also working with the Oxford Stone Group on improving the efficacy of lithotripsy for renal tract stones using computed tomography analysis of stone images. We are combining extra information that can be gained from imaging stones with other clinical variables that can help us better predict which patient’s stones are likely to be successfully treated with lithotripsy.
The association of pre-operative home accelerometry with cardiopulmonary exercise variables.
Cui HW. et al, (2018), Anaesthesia, 73, 738 - 745
The Preoperative Assessment and Optimization of Patients Undergoing Major Urological Surgery.
Cui HW. et al, (2017), Curr Urol Rep, 18
CT Texture Analysis of Ex Vivo Renal Stones Predicts Ease of Fragmentation with Shockwave Lithotripsy.
Cui HW. et al, (2017), J Endourol, 31, 694 - 700
Which stones will fail shockwave lithotripsy treatment? Analysis of patient and stone factors in a predictive model