Forecasting demand of emergency care

Simon Andrew Jones; Mark Patrick Joy; Jon Pearson

Forecasting demand of emergency care

Simon Andrew Jones, Mark Patrick Joy, Jon Pearson

Research output: Contribution to journal › Article › peer-review

Abstract

This paper describes a model that can forecast the daily number of occupied beds due to emergency admissions in an acute hospital. Out of sample forecasts 32 day days in advance. have an RMS error of 3% of the mean number of beds used for emergency admissions. We find that the number of occupied beds due to emergency admissions is related to both air temperature and PHLS data on influenza like illnesses. We find that a period of high volatility, indicated by GARCH errors, will result in an increase in waiting times in the A&E Department. Furthermore. volatility gives more warning of waiting times in A&E than total bed occupancy.

Original language	English
Pages (from-to)	297-305
Journal	Health Care Management Science
Volume	5
Issue number	4
Publication status	Published - Nov 2002
Externally published	Yes

Keywords

Allied health professions and studies

Access to Document

http://www.ncbi.nlm.nih.gov/pubmed/12437279

Cite this

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title = "Forecasting demand of emergency care",

abstract = "This paper describes a model that can forecast the daily number of occupied beds due to emergency admissions in an acute hospital. Out of sample forecasts 32 day days in advance. have an RMS error of 3\% of the mean number of beds used for emergency admissions. We find that the number of occupied beds due to emergency admissions is related to both air temperature and PHLS data on influenza like illnesses. We find that a period of high volatility, indicated by GARCH errors, will result in an increase in waiting times in the A\&E Department. Furthermore. volatility gives more warning of waiting times in A\&E than total bed occupancy.",

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AU - Pearson, Jon

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AB - This paper describes a model that can forecast the daily number of occupied beds due to emergency admissions in an acute hospital. Out of sample forecasts 32 day days in advance. have an RMS error of 3% of the mean number of beds used for emergency admissions. We find that the number of occupied beds due to emergency admissions is related to both air temperature and PHLS data on influenza like illnesses. We find that a period of high volatility, indicated by GARCH errors, will result in an increase in waiting times in the A&E Department. Furthermore. volatility gives more warning of waiting times in A&E than total bed occupancy.

KW - Allied health professions and studies

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VL - 5

SP - 297

EP - 305

JO - Health Care Management Science

JF - Health Care Management Science

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ER -

Forecasting demand of emergency care

Abstract

Keywords

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