Time series analysis can provide accurate predictions of emergency department volume, length of stay, and acuity.
Prospective stochastic time series modeling.
A university teaching hospital.
All patients seen during two sequential years had time of arrival, discharge, and acuity recorded in a computer database.
Time series variables were formed for patients arriving per hour, length of stay, and acuity.
Prediction models were developed from the year 1 data and included five types: raw observations, moving averages, mean values with moving averages, seasonal indicators with moving averages, and autoregressive integrated moving averages.
Forecasts from each model were compared with observations from the first 25 weeks of year 2.
Mots-clés Pascal : Système santé, Service hospitalier, Urgence, Gestion hospitalière, Modèle prévision, Série temporelle, Homme, Utilisation, Temps séjour, Indice gravité, Etats Unis, Amérique du Nord, Amérique
Mots-clés Pascal anglais : Health system, Hospital ward, Emergency, Hospital management, Forecast model, Time series, Human, Use, Residence time, Severity score, United States, North America, America
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 94-0232561
Code Inist : 002B30A01B. Création : 199406.