Patient pathways are complex, involving many steps and the use of resources that are often limited, e.g., surgical teams, or required by many different pathways, e.g., imaging and diagnostics. Prioritising one patient pathway’s access to a resource may have adverse effects for other pathways that also need that resource.

Determining effective prioritisation strategies for patients requires modelling of the complexity of patient pathways, their use of resources, and the effect of different prioritisation strategies. It also requires the consideration of appropriate metrics, in order to define a “good” prioritisation strategy. One of our particular interests is in accumulating priority queues where both acuity and waiting time contribute to a patient’s priority.

Feasible accumulation rates can be chosen to satisfy specified performance objectives for each class, where the objectives are in terms of percentage of patients seen within a prespecified time (metrics common to several health systems, including those in Australia, Canada, and New Zealand). However, such objectives may incentivise bad behaviour, particularly, the “dropping” of patients who have breached the threshold. There is, therefore, also the question of what are the “right” metrics for patient pathways.

This project will develop a portfolio of possible metrics for health care provision and within each metric, develop analytical methods for modelling the system performance and optimising patient flows. The aim of this work is to provide recommendations on prioritisation of patients, both tactical (how to route) and policy-based (how to measure) for patient pathways.

This project requires a strong mathematical background, with knowledge of probability and stochastic processes.

Total value
The three-year scholarship covers PhD tuition fees plus a non-taxed living allowance of NZ$27,300 per annum. The starting date can be anytime from 1 November 2017.

How to apply
For further information and to apply, please contact:
Associate Professor Ilze Ziedins
Department of Statistics
University of Auckland
New Zealand