Myfyriwr ymchwil, Yr Ysgol Mathemateg
- M/1.27, 21-23 Ffordd Senghennydd, Cathays, Caerdydd, CF24 4AG
Mae'r cynnwys hwn ar gael yn Saesneg yn unig.
Education: BSc in Mathematics, Cardiff University (2017)
Healthcare modelling, data analytics, queuing theory
Utilising machine learning to understand cost variability in the NHS
The health service is facing unprecedented pressure from limited cash growth, increasing demand from an ageing population linked with higher incidences of chronic conditions and a reduction in local community services.
Historically, costing has utilised Health Resource Groups (HRG’s), a currency that seeks to cluster procedures into groups consuming similar resources. As these tend to reflect procedures carried out on patients rather than conditions presented by the patient the focus of the costing community has been on elective procedures which only amount to 35% of (non mental health) admitted patients.
Cwm Taf has sought to widen the use of HRG’s by trying to understand other factors that influence variation in costs, in particular the effect of co-morbidities. The cost of treating patients has been shown to be affected by the hospital site, the type of co-morbidity, treatment ward and the age of the patient. Cwm Taf annually produces over 90,000 coded episodes and has access to a further 19,000,000 coded episodes from other Trusts and Health Boards in England and Wales. The advantage of using this type of analysis is that all this information is routinely gathered and quickly available.
Through the development of a collaborative partnership between the NHS and academia, this piece of work aims to create a mathematical model that best reflects factors affecting the patient pathway.
Cwm Taf HB