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Abstract

The healthcare industry, and particularly the publicly funded healthcare industry, faces many challenges over the foreseeable future. These challenges come from a variety of sources such as trends in public opinion, advances in medical research and the changing healthcare demands of the patient population.

The publicly-funded healthcare industry has not kept pace with these changes, as evidenced by large waiting lists for many surgical procedures. If current standards of quality of care are to be maintained, and increased wherever possible, and healthcare budgets not to spiral upwards, then the only solution to the waiting list problem is to increase the cost-effectiveness of healthcare provision.

It will be hypothesised that the cost-effectiveness of healthcare delivery can be improved through the development of a computer-assisted patient scheduling system (CAPSS). This hypothesis will be supported by showing that providing healthcare managers with more complete and accurate information about the projected availability and demand for healthcare resources improves the ability to control the operational performance of the healthcare system. Moreover, that the ability to deliver this necessary information to the control system in a timely and efficient manner is only realistically attainable through the computerisation of the patient scheduling system, and hence the deployment of CAPSS.

The demonstration of the viability of a computerised model of patient scheduling is performed using the empirical domain of the Royal Brampton and Harefield NHS Trust (RBH). Using this data various models are developed, ranging from a mathematical model demonstrating the relationship between the degree of control over patient scheduling attainable by healthcare managers and the optimal level of cost-effectiveness thereby achievable, through to design models of computer simulation programs that may be used as the basis of a decision-support system for enhancing the processes of patient scheduling and the corresponding allocation of healthcare resources.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RZ Other systems of medicine
Departments:	School of Science & Technology > Computer Science School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses

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