Workforce behaviour in healthcare systems

Michalis, Panayides 2023. Workforce behaviour in healthcare systems. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

This thesis investigates the behavioural dynamics that emerge at the interface of Emergency Departments (EDs) and the Emergency Medical Service (EMS). The focus is on the impact that time-targets may have on staff behaviour and patient well-being. This research is structured into two main parts: the first part is the development of a queueing theoretic representation of an ED and the second part is the development of a game theoretic model between two EDs and the EMS that distributes ambulances to them. This thesis uses a variety of mathematical and computational fields such as linear algebra, game theory, queueing theory, graph theory, optimisation, probability theory, agent-based simulation and reinforcement learning. The queueing model is developed using both a discrete event simulation and a Markov chain approach. The queueing network consists of two queueing nodes where there is some strategic managerial behaviour that relates to how two types of individuals are routed between the two nodes. The first node acts as a buffer for one type of individuals before moving to the second node, while the second node consists of a waiting room and a service centre. Both approaches are used to obtain performance measures of the queueing system and explicit formulas are derived for the mean waiting time, the mean blocking time and the proportion of individuals within a given target time. In addition, some numeric results are presented that compare the Markov chain and discrete event simulation approaches. Consequently, this thesis describes the development and application of a 3-player game theoretic model between two such queueing networks and a service that distributes individuals to them. In particular the game is then reduced to a 2-player normal-form game. The resultant model is used to explore dynamics between all players. A backwards induction technique is used to get the utilities of the normal-form game between the two queueing systems. The particular game is then applied to a healthcare scenario to capture the emergent behaviour between the EMS and two EDs. The results and outcomes that are produced ii by various instances of the game are then analysed and discussed. The learning algorithm replicator dynamics is used to explore the evolutionary behaviours that emerge in the game. In particular, the behaviour that naturally emerges from the game seems to be one that causes more blockage and includes less cooperation. Several ways to escape this learned inefficient behaviour are discussed. Finally, the thesis explores an extension of the queueing theoretic model that allows servers to choose their own service speed. This is implemented using an agent-based simulation approach. The agent-based model is then used in conjunction with a reinforcement learning algorithm to explore the effect that the servers’ behaviour has on the overall performance of the system.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Mathematics
Date of First Compliant Deposit:	8 June 2023
Last Modified:	08 Jun 2023 15:00
URI:	https://orca.cardiff.ac.uk/id/eprint/160238

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