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Title: The synergy between system dynamics and the Coxian phase-type distribution : an application in healthcare modelling
Author: McQuillan, Janette
ISNI:       0000 0004 5371 6475
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2014
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Health systems of developed countries around the world are facing immense pressure. This is due to an ageing population and an increase in the prevalence of chronic disease. The purpose of this research is to develop the system dynamics Coxian phase-type (SDC-Ph) model which consists of inter-related components each one being a system dynamics model. This will serve as a framework for investigating the change in prevalence of coronary heart disease in Northern Ireland between 2007 and 2027 and the implications this will have on coronary heart disease (CHD) related inpatient admissions. The first component of the framework is the coronary heart disease prevalence model. It incorporates the changes anticipated in population size and age structure, risk factor prevalence, and primary and secondary interventions and the impact this will have on prevalence over the twenty year time period. The second component of the framework is the coronary heart disease hospital admissions model. It is a composite model which combines the system dynamics methodology with the Coxian phase-type distribution to represent the flow of patients with coronary heart disease through hospital and is used to determine the impact of the changing prevalence on the demand for acute care beds. The final component of the framework is the coronary heart disease cost model. It uses the results obtained from the fit of the Coxian phase-type distribution to group patients together according to their likely time of discharge from hospital thus allowing inferences to be made regarding the costs associated with the individual's phase of care. The SDC-Ph model allows testing of different healthcare interventions taking aspects of population dynamics such as the ageing population into account and forecasts future population incidents and associated costs. It allows healthcare providers to model the impact of interventions and thus assist in the decision making process.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available