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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/13964

Title: Modelling waiting lists and waiting times for cardiac surgery operations
Authors: Greaves, Gareth
Issue Date: 2009
Publisher: © Gareth Greaves
Abstract: This study details the creation of two Simulat1on models for a cardiac surgery specialty in a Midlands hospital The models were designed to help the specialty meet waiting time targets set out by the Government in their NHS Plan. The first model is a spreadsheet data Simulation that gives a general prediction of patients waiting for surgery by time band for up to a year in the future based on previous data. The study uses the qualitative analysis of Interviews and documents to generate the second model The first part of this model is a qualitative causal loop diagram of the cardiac surgery system A quantitative 'Stock & Flow' model is drawn from this qualitative model which gives detailed predict1ons of waiting lists and times and other system variables for the cardiac surgery specialty The system dynamics model is validated it can estimate the maximum number of new outpatient attendances the system can support whilst keeping inpatient waiting times below three months for various configurations of theatre time and Cardiac lntensive Care Unit (CICU) beds The study concludes that CICU beds are a bigger constraint on inpatient waiting times in the cardiac surgery specialty at the hospital than theatre time. Measures to improve waiting times and shorten lists should therefore concentrate on improving patient flow through the CICU, for example more beds in the unit would enable more patients to be treated The model can also demonstrate the use of the theory of constraints in managing waiting lists, which is the method used by the NHS Modernisation Agency in their guidance on wait1ng list management.
Description: A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.
URI: https://dspace.lboro.ac.uk/2134/13964
Appears in Collections:PhD Theses (Information Science)

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