基于ARIMA模型的医院超声检查工作量预测研究

doi:10.3969/j.issn.1006-5253.2023.06.008

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Abstract Objective To observe the application of ARIMA model in predicting the workload of ultrasonic examination in a hospital, and to provide basis for the rational allocation of medical resources.Methods The workload data of ultrasonic -examination in the monthly report of hospital business volume in the hospital from January 2011 to December 2021 were collected. The SPSS 26.0 software was used to construct the ARIMA (p,d,q)×(P,D,Q)_s model to predict the workload of ultrasonic-examination in 2021, and the prediction effect was evaluated by model fitting parameter R²and average absolute percentage error (MAPE).Results The ARIMA (0,1,1) (0,1,1)₁₂ model is the relatively optimal model of ultrasonic examination workload. The R² of the model is 0.901, and the residual error of the model is confirmed to be a white noise sequence by Ljung-Box test (Ljung-Box Q (18) =14.939, P=0.529). The MAPE of the model is 7.28%, the actual value is within 95% confidence interval of the predicted value, and the prediction accuracy of the model is high.Conclusion The ARIMA (0,1,1) (0,1,1)₁₂ model can better predict the workload of ultrasonic examination. Hospitals should allocate medical and health resources reasonably according to the changing law of ultrasonic examination workload so as to enhance the fine management level of modern hospitals.

Key words： ARIMA ultrasound medicine workload prediction

Received: 08 September 2023

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	Articles by authors
	Liang Danyan
	Zhang Feifei
	Liu Qian
	Cao Yang
	Li Chenhao

Cite this article:

Liang Danyan,Zhang Feifei,Liu Qian, et al. Prediction of hospital ultrasonic examination workload based on ARIMA model[J]. journal1, 2023, 30(6): 436-439.

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http://manu58.magtech.com.cn/Jwk_zgyytj/EN/10.3969/j.issn.1006-5253.2023.06.008 OR http://manu58.magtech.com.cn/Jwk_zgyytj/EN/Y2023/V30/I6/436

［1］国务院办公厅关于推动公立医院高质量发展的意见:国办发〔2021〕18号［J］．中华人民共和国国务院公报，2021(17)：174-178.

［2］国家卫生健康委，国家中医药管理局.关于印发公立医院高质量发展促进行动（2021-2025年）的通知［A/OL］.(2021-09-14)［2023-08-03］.https://www.gov.cn/zhengce/zhengceku/2021-10/14/content_5642620.htm.

［3］胡小靖，房洪军，孙玮，等．医院门诊量与资源配置策略研究［J］．医院管理论坛，2021，38(10)：22-26．DOI: 10.3969/j.issn.1671-9069.2021.10.006.

［4］王燕．时间序列分析［M］．北京：中国人民大学出版社，2015．

［5］张蔚，张彦琦，杨旭．时间序列资料ARIMA季节乘积模型及其应用［J］．第三军医大学学报，2002，24(8)：955-957．DOI: 10.3321/j.issn:1000-5404.2002.08.026.

［6］张红梅，尹立雪，李春梅，等．四川省超声医学专业质量控制基线调查报告［J］．中华医学超声杂志(电子版)，2021，18(3)：313-320．DOI: 10.3877/cma.j.issn.1672-6448.2021.03.013.

［7］李斌．基于灰色模型的某三甲医院门诊量与住院量预测［J］．中国病案，2019，20(6)：36-39．DOI: 10.3969/j.issn.1672-2566.2019.06.014.

［8］刘焰，卢萍萍．基于移动平均季节指数法的门诊量分析及预测［J］．医学信息，2021，34(23)：156-158．DOI: 10.3969/j.issn.1006-1959.2021.23.047.

［9］徐珊珊，俞琤．分时段分项目定量预约分诊对超声工作效率的影响［J］．中医药管理杂志，2020，28(19)：218-219．

［10］桑泉红，徐培文．基于时序序列模型预测医院门诊人次［J］．中国医院统计，2022，29(1)：25-28．DOI: 10.3969/j.issn.1006-5253.2022.01.005.

［11］耿娟．ARIMA模型在医院门诊量预测中的应用［J］．中国卫生统计，2014，31(4)：643-645．DOI: 10.3969/j.issn.1671-8348.2012.13.005.

［12］刘国柱．ARIMA模型与GM(1，1)模型在医院门诊人次预测中的比较研究［J］．中国医院统计，2017，24(1)：5-8．DOI: 10.3969/j.issn.1006-5253.2017.01.002.