Improving Efficiency and Equity of Ambulance Services through Advanced Demand Modelling

Demand for Ambulance Services in England has risen
dramatically over recent years, with growing pressure
anticipated for future years. The disparity between the
increasing demand and limited ambulance resources makes
the major challenge for maintaining a high-quality service. In
2017, NHS England undertook a significant national reform
called the Ambulance Response Programme (ARP), designed
to address efficiency and performance issues. It noted the
over-use of immediate dispatch decisions and the insufficient
allocation of resources to incidents. Key issues concerned:
the quality of care; its cost-effectiveness; and the equality of
provision across areas and population groups. Given such
situation, from 2017 to 2018, King's researchers have
worked with the London Ambulance Service (LAS) on an
ESRC-funded project - DASH, exploring how big data could
improve decision-making in ambulance response. The final
report suggested six new data initiatives, out of which, three
are strongly related to demand prediction.
In view of the growing pressures of NHS, and the necessity of
ambulance services to understand the needs of the
populations they serve, the proposed PhD project aims to
develop an advanced demand prediction model for
ambulance services taking LAS as a case study. The
research is to find the most correlated socioeconomic,
environmental, and spatiotemporal factors and to model
these factors as predictors of ambulance demand. The final
component of the PhD will develop the implications of the
model as Demand Management innovations, for future
testing.