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Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models.
BMC Health Serv Res. 2017 07 10; 17(1):469.BH

Abstract

BACKGROUND

Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration.

METHODS

We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead.

RESULTS

The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better.

CONCLUSIONS

Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step.

Authors+Show Affiliations

Business School, Sichuan University, No. 29, Wangjiang Road, Chengdu, Sichuan, 610064, China.Business School, Sichuan University, No. 29, Wangjiang Road, Chengdu, Sichuan, 610064, China.Business School, Sichuan University, No. 29, Wangjiang Road, Chengdu, Sichuan, 610064, China. zhangxl3032@163.com.Outpatient Department, West China Hospital, Sichuan University, Chengdu, Sichuan, 610064, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28693579

Citation

Luo, Li, et al. "Hospital Daily Outpatient Visits Forecasting Using a Combinatorial Model Based On ARIMA and SES Models." BMC Health Services Research, vol. 17, no. 1, 2017, p. 469.
Luo L, Luo L, Zhang X, et al. Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Serv Res. 2017;17(1):469.
Luo, L., Luo, L., Zhang, X., & He, X. (2017). Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. BMC Health Services Research, 17(1), 469. https://doi.org/10.1186/s12913-017-2407-9
Luo L, et al. Hospital Daily Outpatient Visits Forecasting Using a Combinatorial Model Based On ARIMA and SES Models. BMC Health Serv Res. 2017 07 10;17(1):469. PubMed PMID: 28693579.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Hospital daily outpatient visits forecasting using a combinatorial model based on ARIMA and SES models. AU - Luo,Li, AU - Luo,Le, AU - Zhang,Xinli, AU - He,Xiaoli, Y1 - 2017/07/10/ PY - 2016/11/04/received PY - 2017/06/22/accepted PY - 2017/7/12/entrez PY - 2017/7/12/pubmed PY - 2018/3/3/medline KW - ARIMA KW - Combinatorial forecasting model KW - Daily outpatient visits KW - SES SP - 469 EP - 469 JF - BMC health services research JO - BMC Health Serv Res VL - 17 IS - 1 N2 - BACKGROUND: Accurate forecasting of hospital outpatient visits is beneficial for the reasonable planning and allocation of healthcare resource to meet the medical demands. In terms of the multiple attributes of daily outpatient visits, such as randomness, cyclicity and trend, time series methods, ARIMA, can be a good choice for outpatient visits forecasting. On the other hand, the hospital outpatient visits are also affected by the doctors' scheduling and the effects are not pure random. Thinking about the impure specialty, this paper presents a new forecasting model that takes cyclicity and the day of the week effect into consideration. METHODS: We formulate a seasonal ARIMA (SARIMA) model on a daily time series and then a single exponential smoothing (SES) model on the day of the week time series, and finally establish a combinatorial model by modifying them. The models are applied to 1 year of daily visits data of urban outpatients in two internal medicine departments of a large hospital in Chengdu, for forecasting the daily outpatient visits about 1 week ahead. RESULTS: The proposed model is applied to forecast the cross-sectional data for 7 consecutive days of daily outpatient visits over an 8-weeks period based on 43 weeks of observation data during 1 year. The results show that the two single traditional models and the combinatorial model are simplicity of implementation and low computational intensiveness, whilst being appropriate for short-term forecast horizons. Furthermore, the combinatorial model can capture the comprehensive features of the time series data better. CONCLUSIONS: Combinatorial model can achieve better prediction performance than the single model, with lower residuals variance and small mean of residual errors which needs to be optimized deeply on the next research step. SN - 1472-6963 UR - https://www.unboundmedicine.com/medline/citation/28693579/Hospital_daily_outpatient_visits_forecasting_using_a_combinatorial_model_based_on_ARIMA_and_SES_models_ L2 - https://bmchealthservres.biomedcentral.com/articles/10.1186/s12913-017-2407-9 DB - PRIME DP - Unbound Medicine ER -