Forecast emergency room visits – a major diagnostic categories based approach

¹ Industrial engineering dept. Université du Québec à Trois-Rivières, Canada

^⋆ Correspondence: abdeljelil.aroua@uqtr.ca

Received: 20 May 2014
Accepted: 20 May 2015

Abstract

This work is a case study intended to explore the capability of three forecasting techniques to predict emergency department (ED) visits based on Major Diagnostic Categories. It is a part of a larger work aimed to improve ED patients’ throughput time. The ED in this case is considered as a part of the health chain and the process of arrival and departure of patients are included. The prediction models presented in this work are initially established and validated from the historical 3-year emergency room visits at Sherbrook University Hospitals and uses the week as the period unit. Given that resources are consumed differently for each disease, a group of patients has been considered according to the major diagnostic categories (MDC). Three predictive models of the number of visits are considered and compared: linear regression model, SARIMA and multivariate SARIMA. The accuracy of the prediction models is evaluated by calculating the mean percentage error (MAPE) and the mean absolute error (MAE) between forecast and observed data. The medium term forecasting model for the number of admissions is determined according to the estimated admission ratio for each patient group, while the short term model is established according to a regression model based on age groups. SARIMAX offers the most accurate model with a MAPE ranging from 6% to 49% (group of a small number of visits). Twelve of the twenty-seven groups of patients account for nearly 90% of the total of emergency room visits and the weighted mean average percentage error (WMAPE) stands at 8%. The admission rates for each group of patients is based on Gauss’ distribution and is different from one group to another. For many MDCs, strong correlations can be demonstrated between the admission rates and the patient age groups by using a quadratic regression. The prediction models explored in this paper aims to help managers to plan more efficiently the emergency department resources. The models can also be used to plan resources of other hospital departments since they give information about the number of admitted patients for each MDC.