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Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan.
Juang, Wang-Chuan; Huang, Sin-Jhih; Huang, Fong-Dee; Cheng, Pei-Wen; Wann, Shue-Ren.
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  • Juang WC; Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
  • Huang SJ; Department of Information Management, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan.
  • Huang FD; Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
  • Cheng PW; Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
  • Wann SR; Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
BMJ Open ; 7(11): e018628, 2017 Dec 01.
Article en En | MEDLINE | ID: mdl-29196487
OBJECTIVE: Emergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits. METHODS: We retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses. RESULTS: A series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visitt=7111.161+(at+0.37462 at-1). CONCLUSION: The ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos Estadísticos / Servicio de Urgencia en Hospital / Predicción Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: BMJ Open Año: 2017 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos Estadísticos / Servicio de Urgencia en Hospital / Predicción Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: Asia Idioma: En Revista: BMJ Open Año: 2017 Tipo del documento: Article País de afiliación: Taiwán Pais de publicación: Reino Unido