Forecast emergency room visits – a major diagnostic categories based approach

Abstract

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.

Forecasting Daily Arrivals and Peak Occupancy in a Combined Emergency Department

“…by department, acuity, diagnosis or method of arrival. An example of meaningful strati cation of ED visits was done by Aroua et al (24). They strati ed patients according to 25 classes of major diagnostic categories and achieved low MAPEs of 7-9% for cardiovascular, respiratory, and gastrointestinal diagnoses, whereas MAPE was as high as 68 % for mental and substance abuse disorders.…”

Unplanned blood use within 24 hours of emergency department presentation: A cohort study in an ageing population

“…This highlights the importance of understanding the impost unplanned events place on demand for clinical resources. The complexity of processes, diverse array of presenting conditions and fluctuations in demand mean that scheduling resources for the ED is complex, with overcrowded departments and long waiting times being common 3 . While demographic and clinical drivers of blood use in ICUs and some surgical specialities are well studied, observational and epidemiological studies of blood use following ED presentation are lacking 4,5 …”

“…The complexity of processes, diverse array of presenting conditions and fluctuations in demand mean that scheduling resources for the ED is complex, with overcrowded departments and long waiting times being common. 3 While demographic and clinical drivers of blood use in ICUs and some surgical specialities are well studied, observational and epidemiological studies of blood use following ED presentation are lacking. 4,5 Previous studies have reported on clinical and demographic drivers of blood use in Australia, [6][7][8] UK, 9,10 Spain, 11 USA, 7 Denmark, 7 Germany 12,13 and Switzerland.…”