title: A systematic review of models for forecasting the number of emergency department visits.
abstract: The ability to predict patient visits to emergency departments (ED) is crucial for designing strategies aimed at avoiding overcrowding. A good working knowledge of the mathematical models used to predict patient volume and of their results is therefore essential. Articles retrieved by a Medline search were reviewed for studies designed to predict patient attendance at ED or walk-in clinics. Nine studies were identified. Most of the models used to predict patient volume were either linear regression models including calendar variables or time series models. These models explained 31-75% of patient-volume variability. Although the day of the week had the strongest effect, this variable explained only part of the variability. Other causes of this variability are to be defined. However, the performance of the models was good, with errors ranging from 4.2% to 14.4%. Adding meteorological data failed to improve model performance. The mathematical methods developed to predict ED visits have a low rate of error, but the prediction of daily patient visits should be used carefully and therefore does not allow day-to-day adjustments of staff. ED directors or managers should be aware of the model limitations. These models should certainly be used on a larger scale to assess future needs.
authors.affiliation: Service des Urgences, Hôpital Bichat, 46 rue Henri-Huchard, Paris, France. mathias.wargon@bch.aphp.fr
authors.forename: M
authors.initials: M
authors.lastname: Wargon
authors.forename: B
authors.initials: B
authors.lastname: Guidet
authors.forename: T D
authors.initials: TD
authors.lastname: Hoang
authors.forename: G
authors.initials: G
authors.lastname: Hejblum
mesh_terms: D004636:Emergency Service, Hospital / Q000706:statistics & numerical data* / Q000639:trends; D005544:Forecasting; D006801:Humans; D015233:Models, Statistical; D010342:Patient Acceptance of Health Care / Q000706:statistics & numerical data*; D012044:Regression Analysis