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Forecasting demand of emergency care.
Health Care Manag Sci. 2002 Nov; 5(4):297-305.HC

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.

Authors+Show Affiliations

Jones SA
School of Mathematics, Kingston University, Kingston-upon-Thames, Surrey, UK. s.jones@kingston.ac.uk
Joy MP
No affiliation info available
Pearson J
No affiliation info available

MeSH

Emergency Service, HospitalForecastingHealth Services Needs and DemandHumansInfluenza, HumanModels, StatisticalSeasonsUnited Kingdom

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

12437279

Citation

Jones, Simon Andrew, et al. "Forecasting Demand of Emergency Care." Health Care Management Science, vol. 5, no. 4, 2002, pp. 297-305.
Jones SA, Joy MP, Pearson J. Forecasting demand of emergency care. Health Care Manag Sci. 2002;5(4):297-305.
Jones, S. A., Joy, M. P., & Pearson, J. (2002). Forecasting demand of emergency care. Health Care Management Science, 5(4), 297-305.
Jones SA, Joy MP, Pearson J. Forecasting Demand of Emergency Care. Health Care Manag Sci. 2002;5(4):297-305. PubMed PMID: 12437279.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting demand of emergency care. AU - Jones,Simon Andrew, AU - Joy,Mark Patrick, AU - Pearson,Jon, PY - 2002/11/20/pubmed PY - 2002/12/17/medline PY - 2002/11/20/entrez SP - 297 EP - 305 JF - Health care management science JO - Health Care Manag Sci VL - 5 IS - 4 N2 - 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. SN - 1386-9620 UR - https://www.unboundmedicine.com/medline/citation/12437279/Forecasting_demand_of_emergency_care_ DB - PRIME DP - Unbound Medicine ER -