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Forecasting Daily Volume and Acuity of Patients in the Emergency Department.
Comput Math Methods Med. 2016; 2016:3863268.CM

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+Show Affiliations

Department of Industrial and Transportation Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.Department of Industrial and Transportation Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.Department of Industrial and Transportation Engineering, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.Endocrine Division, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.Emergency Department, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.Endocrine Division, Hospital de Clínicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27725842

Citation

Calegari, Rafael, et al. "Forecasting Daily Volume and Acuity of Patients in the Emergency Department." Computational and Mathematical Methods in Medicine, vol. 2016, 2016, p. 3863268.
Calegari R, Fogliatto FS, Lucini FR, et al. Forecasting Daily Volume and Acuity of Patients in the Emergency Department. Comput Math Methods Med. 2016;2016:3863268.
Calegari, R., Fogliatto, F. S., Lucini, F. R., Neyeloff, J., Kuchenbecker, R. S., & Schaan, B. D. (2016). Forecasting Daily Volume and Acuity of Patients in the Emergency Department. Computational and Mathematical Methods in Medicine, 2016, 3863268.
Calegari R, et al. Forecasting Daily Volume and Acuity of Patients in the Emergency Department. Comput Math Methods Med. 2016;2016:3863268. PubMed PMID: 27725842.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Forecasting Daily Volume and Acuity of Patients in the Emergency Department. AU - Calegari,Rafael, AU - Fogliatto,Flavio S, AU - Lucini,Filipe R, AU - Neyeloff,Jeruza, AU - Kuchenbecker,Ricardo S, AU - Schaan,Beatriz D, Y1 - 2016/09/20/ PY - 2016/05/31/received PY - 2016/08/18/revised PY - 2016/08/21/accepted PY - 2016/10/12/entrez PY - 2016/10/12/pubmed PY - 2017/3/8/medline SP - 3863268 EP - 3863268 JF - Computational and mathematical methods in medicine JO - Comput Math Methods Med VL - 2016 N2 - 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. SN - 1748-6718 UR - https://www.unboundmedicine.com/medline/citation/27725842/Forecasting_Daily_Volume_and_Acuity_of_Patients_in_the_Emergency_Department_ L2 - https://dx.doi.org/10.1155/2016/3863268 DB - PRIME DP - Unbound Medicine ER -