Prediction of demand for emergency care in an acute hospital

Jones, Simon Andrew (2005) Prediction of demand for emergency care in an acute hospital. (PhD thesis), Kingston University.

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Abstract

This thesis describes some models that attempt to forecast the number of occupied beds due to emergency admissions each day in an acute general hospital. Hospital bed managers have two conflicting demands: they must not only ensure that at all times they have sufficient empty beds to cope with possible emergency admissions but they must fill as many empty beds as possible with people on the waiting list. This model is important as it could help balance these two conflicting demands. The research is based on data from a district general and a postgraduate teaching hospital in South East London. Several tests indicate that emergency bed occupancy may have a nonlinear underlying data generating process. Therefore, both linear models and nonlinear models have been fitted to the data. At horizons up to 14 days, it was found that there was no statistically significant difference in the errors from the linear and nonlinear models. However at the 35 day forecast horizon the linear model gives the best forecast and tests indicate errors from this model are within 4% of mean occupancy. It is noted that a Markov Switching model gave very good forecasts of up to 4 days into the future. A search of the literature found no previous research that tested emergency bed occupancy for nonlinearities. The thesis ends with a gravity model to predict the change in number of Accident and Emergency (A&E) attendances following the relocation of an A&E Department in South East London.

Item Type: Thesis (PhD)
Physical Location: This item is held in stock at Kingston University Library.
Research Area: Allied health professions and studies
Applied mathematics
Faculty, School or Research Centre: Faculty of Computing, Information Systems and Mathematics (until 2011) > Digital Imaging Research Centre (DIRC)
Depositing User: Automatic Import Agent
Date Deposited: 09 Sep 2011 15:07
Last Modified: 30 May 2014 14:32
URI: http://eprints.kingston.ac.uk/id/eprint/20739

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