| 基于时间序列分析法的静脉用药调配工作量波动分析及预测 |
| 投稿时间：2022-06-04 修订日期：2023-03-27 点此下载全文 |
| 引用本文：宗留留,杨蕴智,老东辉,李晓宇,吕迁洲.基于时间序列分析法的静脉用药调配工作量波动分析及预测[J].药学实践杂志,2023,41(9):561~565 |
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| 中文摘要:目的 探讨静脉用药调配中心（pharmacy intravenous admixture services，PIVAS）长期医嘱工作量的波动特征，构建以日为单位频次的工作量波动预测模型，为PIVAS工作模式的调整提供参考。方法 选取复旦大学附属中山医院东院区PIVAS2020年7月至2021年6月长期医嘱日工作量数据，采用时间序列分析法分析工作量波动特征并构建预测模型，通过拟合参数及预测结果验证模型精度。结果 PIVAS长期医嘱日工作量数据波动具有周期性、短期缓慢上升和不规则变动等特征；采用温特斯乘法模型拟合序列，Ljung-Box统计量的显著性值（P值）为 0.060（P > 0.05），R2 = 0.777，拟合值与实际值的平均绝对误差为4.45%，表明模型拟合精度高；预测结果与实测结果平均相对偏差为3.81%，说明模型预测有效。结论 时间序列分析法可用于PIVAS长期医嘱工作量的分析预测，但因医嘱工作量存在周期性、不规则变动等波动规律，故需根据工作量波动特征及预测结果调整工作模式，以保障PIVAS高效运营。 |
| 中文关键词:静脉用药调配中心 工作量 时间序列分析法 |
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| Fluctuation analysis and prediction of intravenous medication dispensing workload based on time series analysis method |
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| Abstract:Objective To explore the fluctuation characteristics of long-term doctor's order workload in pharmacy intravenous admixture services (PIVAS) and build a daily workload fluctuation prediction model and provide reference for the adjustment of PIVAS work mode. Methods Daily workload data of long-term doctor’s orders from PIVAS in the East Campus of Zhongshan Hospital affiliated to Fudan University from July 2020 to June 2021 were selected , and the time series analysis method was used to analyze the workload fluctuation characteristics and a prediction model was established. The accuracy of the model was verified by fitting parameters and prediction results. Results The fluctuation of PIVAS long-term doctor's daily workload data had the characteristics of periodicity, short-term slow rise and irregular variation. The Winters multiplier model was used to fit the series with R2 = 0.777, the significance value of Ljung-Box statistic value (P value) was 0.060, and the mean absolute error percentage between the fitted and actual values was 4.45%, indicating that the model fitting accuracy was high. The average relative deviation between the predicted and actual results was 3.81%, indicating that the model prediction was effective. Conclusion The model constructed in this study could be used for the analysis and prediction of long-term doctor's orders workload of PIVAS. However, because the workload of doctor's orders has fluctuations such as periodicity and irregular changes, it is necessary to adjust the working model according to the fluctuation characteristics of the workload and the prediction results to ensure the efficient operation of PIVAS. |
| keywords:pharmacy intravenous admixture services workload time series analysis method |
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