Application of autoregressive integrated moving average model in prediction of outpatient and emergency visits in a district-level public hospital_West China Medical Journal

Authors：

QIU Xuehan ¹ , PENG Di ² ,  YANG Cui ¹

1. Operation Management Department, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Operation Management Department, the First People’s Hospital of Shuangliu District, Chengdu, Sichuan 610041, P. R. China;

Corresponding?author：

YANG Cui, Email: yangcui@wchscu.cn

Keywords：

Outpatient and emergency visits; autoregressive integrated moving average model; prediction

DOI：

10.7507/1002-0179.202311076

Video：

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Objective To accurately predict the outpatient and emergency visits of a district-level public hospital based on autoregressive integrated moving average (ARIMA) model, providing important basis for hospital budget planning and operational decisions. Methods The monthly outpatient and emergency visits of a public hospital in Shuangliu District, Chengdu City from January 2012 to November 2023 were collected, and R 4.3.1 software was used to establish an ARIMA model based on the data from January 2012 to December 2022. The outpatient and emergency visits from January to November 2023 were predicted and validated. Results Except for January and March 2023, every monthly number of predicted outpatient and emergency visits for 2023 matched the actual one relatively well. The average absolute percentage error for January to November 2023 was 8.504%. The actual total number of outpatient and emergency visits from January to November 2023 was 1441960, and the predicted value was 1417130 with a relative error of –1.722%. Conclusions ARIMA model can predict the outpatient and emergency visits of district-level hospitals relatively well. However, factors such as the high incidence of COVID-19 may affect the accuracy of short-term prediction.

Citation： QIU Xuehan, PENG Di, YANG Cui. Application of autoregressive integrated moving average model in prediction of outpatient and emergency visits in a district-level public hospital. West China Medical Journal, 2023, 38(12): 1807-1811. doi: 10.7507/1002-0179.202311076 Copy

1.	宮芳芳, 劉勝男. 新時期縣(區)級醫院高質量發展的實踐與思考. 現代醫院管理, 2023, 21(2): 55-57.
2.	劉磊. 醫院門診量季節變化與趨勢分析. 中國病案, 2022, 23(9): 52-55.
3.	程莉. 縣級公立醫院預算管理工作中存在問題及對策. 質量與市場, 2023(11): 82-84.
4.	Sumitra ID, Basri I. Forecasting the number of outpatient patient visits using the ARIMA, SES and holt-winters meth-ods at XYZ Community Health Center. IOP Conf Ser: Mater Sci Eng, 2020(879): 012060.
5.	Lin Y, Wan C, Li S. Prediction of women and Children’s hospital outpatient numbers based on the autoregressive integrated moving average model. Heliyon, 2023, 9(4): e14845.
6.	唐路, 宋萍, 謝冰玨, 等. 基于 R 語言的 ARIMA 乘積季節模型對重慶某兒童醫院門診量的預測分析. 醫學信息, 2021, 34(11): 19-22.
7.	李磊, 孫瑞華, 王蜜源. Arima-Elman 耦合模型在醫院門診量預測中的應用及評價. 中國病案, 2020, 21(11): 68-72.
8.	邱林. 應用 ARIMA 模型預測某三級甲等醫院門診量. 中國醫院統計, 2018, 25(1): 77-79.
9.	潘歡弘, 朱蒙曼, 劉曉青. ARIMA 乘積季節模型在江西省手足口病發病預測中的應用. 現代預防醫學, 2018, 45(1): 1-4, 7.
10.	牛巖, 郭超. 基于支持向量機的富平縣大氣蒸發量數據預測研究. 農業與技術, 2022, 42(14): 63-66.
11.	楊振昊, 張俊波, 楊晨星, 等. 基于 SARIMA 模型的我國水產品消費價格指數預測. 海洋湖沼通報, 2021, 43(2): 131-138.
12.	Quartey-Papafio TK, Liu S, Javed SA. Forecasting cocoa production of six major producers through ARIMA and grey models. Grey Syst, 2021, 11(3): 434-462.
13.	楊圓, 鄒霞, 凌莉. 應用傳播動力學模型模擬我國人口流動情況對耐多藥結核病發病率的影響. 熱帶醫學雜志, 2022, 22(7): 1001-1005.
14.	謝國艷. 縣級公立醫院全面預算管理問題研究——以 TZ 縣人民醫院為例. 老字號品牌營銷, 2023(10): 64-66.
15.	李依恬, 喬富宇. ARIMA 模型在北京市西城區手足口病月發病率預測中的應用. 職業與健康, 2018, 34(7): 925-928.
16.	王晨, 郭倩, 周羅晶. ARIMA 模型與指數平滑法預測門診量效果比較. 預防醫學, 2018, 30(11): 1152-1155.
17.	邵靖靖. 中醫院門急診量 ARIMA 分析與對策研究. 中醫藥管理雜志, 2021, 29(18): 47-49.
18.	姜迪, 楊洋, 孔曉穎, 等. 時間序列分析方法在醫院管理中的應用. 預防醫學論壇, 2022, 28(2): 158-161.
19.	戴靈, 劉印, 康孟利, 等. 新冠疫情對兒科門急診的影響及對策研究. 醫院管理論壇, 2022, 39(2): 22-25.
20.	虞佩, 周帥, 成杰, 等. 面對激增的新型冠狀病毒感染患者瑞金醫院門急診工作布局調整方案. 內科理論與實踐, 2023, 18(1): 38-39.
21.	任佳樂, 郭璇, 許珊珊, 等. 2018—2021 年新疆某三級甲等醫院急診量變化趨勢分析. 中國醫院統計, 2023, 30(5): 391-395.

1. 宮芳芳, 劉勝男. 新時期縣(區)級醫院高質量發展的實踐與思考. 現代醫院管理, 2023, 21(2): 55-57.
2. 劉磊. 醫院門診量季節變化與趨勢分析. 中國病案, 2022, 23(9): 52-55.
3. 程莉. 縣級公立醫院預算管理工作中存在問題及對策. 質量與市場, 2023(11): 82-84.
4. Sumitra ID, Basri I. Forecasting the number of outpatient patient visits using the ARIMA, SES and holt-winters meth-ods at XYZ Community Health Center. IOP Conf Ser: Mater Sci Eng, 2020(879): 012060.
5. Lin Y, Wan C, Li S. Prediction of women and Children’s hospital outpatient numbers based on the autoregressive integrated moving average model. Heliyon, 2023, 9(4): e14845.
6. 唐路, 宋萍, 謝冰玨, 等. 基于 R 語言的 ARIMA 乘積季節模型對重慶某兒童醫院門診量的預測分析. 醫學信息, 2021, 34(11): 19-22.
7. 李磊, 孫瑞華, 王蜜源. Arima-Elman 耦合模型在醫院門診量預測中的應用及評價. 中國病案, 2020, 21(11): 68-72.
8. 邱林. 應用 ARIMA 模型預測某三級甲等醫院門診量. 中國醫院統計, 2018, 25(1): 77-79.
9. 潘歡弘, 朱蒙曼, 劉曉青. ARIMA 乘積季節模型在江西省手足口病發病預測中的應用. 現代預防醫學, 2018, 45(1): 1-4, 7.
10. 牛巖, 郭超. 基于支持向量機的富平縣大氣蒸發量數據預測研究. 農業與技術, 2022, 42(14): 63-66.
11. 楊振昊, 張俊波, 楊晨星, 等. 基于 SARIMA 模型的我國水產品消費價格指數預測. 海洋湖沼通報, 2021, 43(2): 131-138.
12. Quartey-Papafio TK, Liu S, Javed SA. Forecasting cocoa production of six major producers through ARIMA and grey models. Grey Syst, 2021, 11(3): 434-462.
13. 楊圓, 鄒霞, 凌莉. 應用傳播動力學模型模擬我國人口流動情況對耐多藥結核病發病率的影響. 熱帶醫學雜志, 2022, 22(7): 1001-1005.
14. 謝國艷. 縣級公立醫院全面預算管理問題研究——以 TZ 縣人民醫院為例. 老字號品牌營銷, 2023(10): 64-66.
15. 李依恬, 喬富宇. ARIMA 模型在北京市西城區手足口病月發病率預測中的應用. 職業與健康, 2018, 34(7): 925-928.
16. 王晨, 郭倩, 周羅晶. ARIMA 模型與指數平滑法預測門診量效果比較. 預防醫學, 2018, 30(11): 1152-1155.
17. 邵靖靖. 中醫院門急診量 ARIMA 分析與對策研究. 中醫藥管理雜志, 2021, 29(18): 47-49.
18. 姜迪, 楊洋, 孔曉穎, 等. 時間序列分析方法在醫院管理中的應用. 預防醫學論壇, 2022, 28(2): 158-161.
19. 戴靈, 劉印, 康孟利, 等. 新冠疫情對兒科門急診的影響及對策研究. 醫院管理論壇, 2022, 39(2): 22-25.
20. 虞佩, 周帥, 成杰, 等. 面對激增的新型冠狀病毒感染患者瑞金醫院門急診工作布局調整方案. 內科理論與實踐, 2023, 18(1): 38-39.
21. 任佳樂, 郭璇, 許珊珊, 等. 2018—2021 年新疆某三級甲等醫院急診量變化趨勢分析. 中國醫院統計, 2023, 30(5): 391-395.

West China Medical Journal

Application of autoregressive integrated moving average model in prediction of outpatient and emergency visits in a district-level public hospital

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