title: Forecasting Daily Volume and Acuity of Patients in the Emergency Department.
abstract: This study aimed at analyzing the performance of four forecasting models in predicting the demand for medical care in terms of daily visits in an emergency department (ED) that handles high complexity cases, testing the influence of climatic and calendrical factors on demand behavior. We tested different mathematical models to forecast ED daily visits at Hospital de Clínicas de Porto Alegre (HCPA), which is a tertiary care teaching hospital located in Southern Brazil. Model accuracy was evaluated using mean absolute percentage error (MAPE), considering forecasting horizons of 1, 7, 14, 21, and 30 days. The demand time series was stratified according to patient classification using the Manchester Triage System's (MTS) criteria. Models tested were the simple seasonal exponential smoothing (SS), seasonal multiplicative Holt-Winters (SMHW), seasonal autoregressive integrated moving average (SARIMA), and multivariate autoregressive integrated moving average (MSARIMA). Performance of models varied according to patient classification, such that SS was the best choice when all types of patients were jointly considered, and SARIMA was the most accurate for modeling demands of very urgent (VU) and urgent (U) patients. The MSARIMA models taking into account climatic factors did not improve the performance of the SARIMA models, independent of patient classification.
authors.affiliation: Department of Industrial and Transportation Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
authors.forename: Rafael
authors.identifier: 0000-0002-5042-0930
authors.initials: R
authors.lastname: Calegari
authors.affiliation: Department of Industrial and Transportation Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
authors.forename: Flavio S
authors.initials: FS
authors.lastname: Fogliatto
authors.affiliation: Department of Industrial and Transportation Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
authors.forename: Filipe R
authors.identifier: 0000-0002-6090-6846
authors.initials: FR
authors.lastname: Lucini
authors.affiliation: Endocrine Division, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
authors.forename: Jeruza
authors.initials: J
authors.lastname: Neyeloff
authors.affiliation: Emergency Department, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
authors.forename: Ricardo S
authors.initials: RS
authors.lastname: Kuchenbecker
authors.affiliation: Endocrine Division, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.
authors.forename: Beatriz D
authors.initials: BD
authors.lastname: Schaan
mesh_terms: D001938:Brazil; D002980:Climate; D004635:Emergency Medicine / Q000706:statistics & numerical data*; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005544:Forecasting; D006295:Health Resources / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000379:methods; D006751:Hospital Information Systems; D006784:Hospitals, Teaching; D006801:Humans; D008962:Models, Theoretical; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care; D012044:Regression Analysis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012621:Seasons; D013696:Temperature; D062606:Tertiary Care Centers; D013997:Time Factors; D014193:Trauma Centers; D014947:Wounds and Injuries / Q000628:therapy*