| 引用本文: | 吴义来,卢猛猛,张烨雯,周洲,鲍泽响,徐青文,吴敏,刘孟雪,陶云松,陈文刚,张文,邹建军,栾家杰.基于ARIMA模型的医疗机构药品需求量预测研究——以他汀类药物为例[J].中国现代应用药学,2025,42(9):109-115. |
| Wu yilai,Lu mengmeng,Zhang yewen,Zhou zhou,Bao zexiang,Xu qingwen,Wu min,Liu mengxue,Tao yunsong,Chen wengang,Zhang wen,Zou jianjun,Luan jiajie.Research on Drug Demands Forecasting in Medical Institutions Based on an ARIMA Model: a Case Study of Statins[J].Chin J Mod Appl Pharm(中国现代应用药学),2025,42(9):109-115. |
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| 基于ARIMA模型的医疗机构药品需求量预测研究——以他汀类药物为例 |
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吴义来,卢猛猛,张烨雯,周洲,鲍泽响,徐青文,吴敏,刘孟雪,陶云松,陈文刚,张文,邹建军,栾家杰
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1.皖南医学院弋矶山医院;2.中国药科大学基础医学与临床药学学院;3.南京市第一医院;4.芜湖市第二人民医院;5.皖南医学院第二附属医院
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| 摘要: |
| 目的? 构建自回归移动平均(ARIMA)模型预测他汀类药物需求量,为医疗机构制定药品采购计划,尤其是集采药品的科学报量提供可靠工具。方法? 收集某三甲医院2022年1月至2024年6月间每月他汀类药物的使用量数据,基于其使用强度DDDs建立时间序列,构建ARIMA模型。以该院2024年7月至12月的他汀类药物使用强度作为内部验证数据评估模型预测效果。收集两所同级别医疗机构2022年1月至2024年12月他汀类药物使用量数据对模型进行外部验证。结果? 医疗机构他汀类药物月度DDDs数据波动具有季节周期性和不规则变动等特征,构建乘积季节性ARIMA(1,1,0)(0,1,0)12模型拟合序列,最小拟合BIC=439.1,残差序列通过Ljung-Box Q检验。内部验证和外部验证均提示模型预测值与实际DDDs相对误差较小,模型预测精度较高。结论 本研究构建的ARIMA模型对他汀类药物使用量预测效果良好,可为医疗机构药品采购及科学报量提供决策参考。 |
| 关键词: 需求预测 ARIMA模型 药品采购 国家带量采购 他汀类药物 |
| DOI： |
| 分类号:R95?????? |
| 基金项目:国家自然科学基金项目（面上项目，重点项目，重大项目） |
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| Research on Drug Demands Forecasting in Medical Institutions Based on an ARIMA Model: a Case Study of Statins |
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Wu yilai1, Lu mengmeng2, Zhang yewen2, Zhou zhou3, Bao zexiang2, Xu qingwen1, Wu min4,5,6, Liu mengxue7, Tao yunsong4,5,6, Chen wengang7, Zhang wen8, Zou jianjun3, Luan jiajie8
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1.Yijishan hospital of Wannan Medical College;2.School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University;3.Nanjing First Hospital;4.the Second People'5.'6.s Hospital of Wuhu;7.the Second Affiliated Hospital of Wannan Medical College;8.Yijishan Hospital of Wannan Medical College
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| Abstract: |
| OBJECTIVE To construct an autoregressive integrated moving average (ARIMA) model for predicting statins demands, we aim to provide medical institutions with a reliable tool to formulate drug procurement plans, especially for the scientific reporting of drug demands in centralized procurement. METHODS Monthly data on statins usage from January 2022 to June 2024 were collected from a tertiary hospital. The defined daily dose frequency (DDDs) was calculated, and an ARIMA model was constructed. The statins usage intensity data of the hospital from July to December 2024 were used for internal validation to assess the model’s predictive performance. Additionally, data from two other tertiary hospitals from January 2022 to December 2024 were collected for external validation of the model. RESULTS The monthly DDDs of statins in medical institutions exhibited seasonal periodicity and irregular fluctuations. A multiplicative seasonal ARIMA(1,1,0)(0,1,0)12 model was constructed to fit the series, with a BIC value of 439.1. The residual series passed the Ljung-Box Q-test. Both internal and external validations indicated that the relative errors between the model's predicted values and the actual DDDs were acceptable, and the model had high prediction accuracy.? CONCLUSION The ARIMA model constructed in this study demonstrated good performance in predicting statin usage and can provide a reference for drug procurement and scientific reporting of drug demands in medical institutions. |
| Key words: demand forecast ARIMA drug procurement national volume-based procurement statins |
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