Forecasting daily attendances at an emergency department to aid resource planning

Health Services & Outcomes Research, National Healthcare Group, Commonwealth Lane, Singapore.
BMC Emergency Medicine 01/2009; 9(1):1. DOI: 10.1186/1471-227X-9-1
Source: PubMed


Accurate forecasting of emergency department (ED) attendances can be a valuable tool for micro and macro level planning.
Data for analysis was the counts of daily patient attendances at the ED of an acute care regional general hospital from July 2005 to Mar 2008. Patients were stratified into three acuity categories; i.e. P1, P2 and P3, with P1 being the most acute and P3 being the least acute. The autoregressive integrated moving average (ARIMA) method was separately applied to each of the three acuity categories and total patient attendances. Independent variables included in the model were public holiday (yes or no), ambient air quality measured by pollution standard index (PSI), daily ambient average temperature and daily relative humidity. The seasonal components of weekly and yearly periodicities in the time series of daily attendances were also studied. Univariate analysis by t-tests and multivariate time series analysis were carried out in SPSS version 15.
By time series analyses, P1 attendances did not show any weekly or yearly periodicity and was only predicted by ambient air quality of PSI > 50. P2 and total attendances showed weekly periodicities, and were also significantly predicted by public holiday. P3 attendances were significantly correlated with day of the week, month of the year, public holiday, and ambient air quality of PSI > 50.After applying the developed models to validate the forecast, the MAPE of prediction by the models were 16.8%, 6.7%, 8.6% and 4.8% for P1, P2, P3 and total attendances, respectively. The models were able to account for most of the significant autocorrelations present in the data.
Time series analysis has been shown to provide a useful, readily available tool for predicting emergency department workload that can be used to plan staff roster and resource planning.

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    • "[13] ont utilisé deux méthodes statistiques : i) un lissage exponentiel et ii ) la méthode de Box- Jenkins pour prévoir le nombre d'admission chaque mois pour la période 2000 à 2005 aux urgences de l'hôpital régional de Victoria en Australie. [14] ont utilisé les séries chronologiques pour prévoir les fréquentations quotidiennes des services d'urgences. Les auteurs ont conclu que l'analyse des séries chronologiques peut fournir un outil utile et facile pour prédire la charge de travail des services d'urgences qui peut être utilisé dans la planification des ressources. "

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    • "Additionally, Sun et al. (2009) evaluated the use of autoregressive integrated moving average models, adjusted to incorporate various environmental variables, to forecast counts of daily patient attendances in the emergency department of an acute care regional general hospital. In addition to univariate time series approaches to forecasting emergency department patient volumes, multivariate time series models have also been utilized and have been shown to reliably forecast emergency department patient census. "
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    BMC Medical Research Methodology 05/2013; 13(1):67. DOI:10.1186/1471-2288-13-67 · 2.27 Impact Factor
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    Computational and Mathematical Methods in Medicine 12/2011; 2011(6):395690. DOI:10.1155/2011/395690 · 0.77 Impact Factor
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