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Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan.
BMJ Open. 2017 Dec 01; 7(11):e018628.BO

Abstract

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.

Authors+Show Affiliations

Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. Department of Information Management, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan.Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.Department of Medical Education and Research, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan. Department of Physical Therapy, Shu-Zen Junior College of Medicine and Management, Kaohsiung, Taiwan.Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

29196487

Citation

Juang, Wang-Chuan, et al. "Application of Time Series Analysis in Modelling and Forecasting Emergency Department Visits in a Medical Centre in Southern Taiwan." BMJ Open, vol. 7, no. 11, 2017, pp. e018628.
Juang WC, Huang SJ, Huang FD, et al. Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan. BMJ Open. 2017;7(11):e018628.
Juang, W. C., Huang, S. J., Huang, F. D., Cheng, P. W., & Wann, S. R. (2017). Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan. BMJ Open, 7(11), e018628. https://doi.org/10.1136/bmjopen-2017-018628
Juang WC, et al. Application of Time Series Analysis in Modelling and Forecasting Emergency Department Visits in a Medical Centre in Southern Taiwan. BMJ Open. 2017 Dec 1;7(11):e018628. PubMed PMID: 29196487.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan. AU - Juang,Wang-Chuan, AU - Huang,Sin-Jhih, AU - Huang,Fong-Dee, AU - Cheng,Pei-Wen, AU - Wann,Shue-Ren, Y1 - 2017/12/01/ PY - 2017/12/3/entrez PY - 2017/12/3/pubmed PY - 2018/7/22/medline KW - arima KW - emergency department overcrowding KW - prediction KW - time series analysis SP - e018628 EP - e018628 JF - BMJ open JO - BMJ Open VL - 7 IS - 11 N2 - 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. SN - 2044-6055 UR - https://www.unboundmedicine.com/medline/citation/29196487/Application_of_time_series_analysis_in_modelling_and_forecasting_emergency_department_visits_in_a_medical_centre_in_Southern_Taiwan_ L2 - http://bmjopen.bmj.com/cgi/pmidlookup?view=long&pmid=29196487 DB - PRIME DP - Unbound Medicine ER -