Develop policies that ensure optimal
allocation of scarce healthcare resources.

The project would develop novel applications of AI/ML methods, decision
making, modelling and Simulation methods in the healthcare management
problems such as hospital admission pattern analysis, bed resource
requirements forecasting, allocation and management. It would provide students
an excellent opportunity to understand how AI/ ML methods can be used for
developing solutions to real life problems and also assessing effectiveness of
such AI/ML tools.
Healthcare resource planners need to develop policies that ensure optimal
allocation of scarce healthcare resources. This goal can be achieved by
analysing admission patterns to forecast daily resource requirements to ensure
optimum allocation and management of available resources. If resources are
limited, admission should be scheduled according to the resource availability.
Such resource availability or demand can change with time. We here model
admissions and patient flow through the care system as a discrete time Markov
chain. In order to have a more realistic representation, a non-homogeneous
model is developed which incorporates time-dependent covariates, namely a
patient’s present age and the present calendar year. However, more
sophisticated models are required to better manage changes in admission
patterns and resource requirements. As our previous work, we have already
developed many such sophisticated models for better modelling admission
patterns and resource requirements [1-4]. In this final year project (FYP), we wi
extend our work to develop novel approaches to effectively solve the problem
using AI/ML based methods.