Philip Richard Andrew James Worrall, UoA 11, ECS
Philip Worrall studied Economics at the University of Manchester as part of a broad degree encompassing the social sciences, mathematics, business and law. Whilst at Manchester he developed a keen interest in the use of statistical techniques to support business planning and strategy in large organisations. In 2008 he obtained a full scholarship from Lancaster University for his MSc in Operational Research, which he passed with distinction and won the SAS prize for his project “Forecasting the future needs of patients with long-term conditions in Derbyshire, East Midlands”. Philip moved to London in 2009 to start his PhD at the University of Westminster in the area of demand forecasting for long-term care in collaboration with the NHS London Procurement Programme.
Advancements in the field of medicine and increased access to high quality healthcare are two factors often cited to explain the rise in life expectancy across the globe. Yet, as populations begin to age and fundamental demographic shifts take hold, significant doubt has been cast over the ability of existing systems of social support, pensions and healthcare to continue to remain economically viable as the proportion of the population working to support those in retirement begins to shrink. One healthcare service that is particularly at risk – due to it being provided to the very section of society most likely to expand over the next 20 years and the observed decrease in the ability of family-support networks to provide assistance – is long-term care (LTC). LTC comprises of the health and social support services for those with chronic illness, physical or mental disability to help them both obtain and maintain an optimal level of functioning. The complex needs of patients combined with high resource requirements and long duration of care results in countries spending between 1% and 3% of their gross domestic product (GDP) on LTC at present. As the link between ageing and demand for LTC is not yet fully understood, there is a large amount of uncertainty regarding the future demand and cost of LTC. The objective of the PhD project is to develop quantitative models using state of the art algorithms. This will generate both reliable and robust results from the extremely limited data characteristic of LTC and increase our understanding of the evolution of LTC. This will help support optimal decision making surrounding the allocation of resources for both current and future generations of patients.