Modelling the cost-effectiveness of strategies to treat end-stage heart failure using discrete event simulation

University of Technology Sydney. Faculty of Business. The cost of providing healthcare is increasing due to an ageing population and new technologies, hence the assessments of value for money are becoming more important. Health Technology Assessment (HTA) is an approach to estimate the cost-effectiveness of treatment strategies to assist in decision-making. However, resource constraints are not usually explicitly considered in HTA. For example, if a patient requires a new drug, it is assumed that that resource is available immediately, without delay to the patient. Queues and waiting lists are commonplace in health care; for instance, patients in an emergency department waiting room or the waiting list for elective surgery. Not incorporating queuing theory into HTA is likely to be an issue if the consequences of delayed treatment significantly affect a patient’s morbidity and mortality. A case-study in end-stage heart failure is utilised to explore the restrictions faced by patients as they enter the heart transplant (HTx) waiting list due to the shortage of donor organs. Unique to organ donation is the matching process, whereby patients are matched to a donor heart based on blood type and weight rather than a simple first-come first-served basis. Additionally, artificial implantable devices, such as a left ventricular assist device, can buy patients more time on the waiting list or allow patients to become eligible for a HTx when used as a bridge to candidacy. This thesis explicitly considers a resource constrained HTA by applying queuing theory using discrete event simulation (DES). A dynamic simulation modelling method, DES models queues representing the competition between patients for resources. This study used real world data from an Australian transplanting hospital to inform the modelling. The results of a DES model with and without queuing are compared with a traditional cohort Markov model to explore the impact of the modelling methods on decision-making.