| Volatility Analysis and Forecast of long-term prescribing workload in PIVAS based on Time series analysis techniques |
| 投稿时间：2022-02-16 修订日期：2022-03-15 Click here to download the full text |
| Citation of this paper:宗留留.基于时间序列分析技术的PIVAS长期医嘱工作量波动分析及预测[J].中国临床医学,0,(): |
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| Abstract:To explore the fluctuation characteristics of long-term medical prescription workload in Pharmacy Intravenous Admixture Services (PIVAS) and construct a workload fluctuation prediction model with a frequency of days, so as to provide reference for the adjustment of the work pattern of PIVAS. METHODS: Daily data on long-term medical prescription workload from July 2020 to June 2021 were selected to analyse the workload fluctuation characteristics. The model was constructed using time series analysis techniques, and the accuracy of the model was verified by fitting parameters and prediction results. Results: The PIVAS daily long-term medical workload data were characterised by cyclicality, short-term slow upward trend and irregular variation; the Winters multiplier model was used to fit the series with R2 = 0.837, the significance value of Ljung-Box statistic was P = 0.171 > 0.05, and the mean absolute error percentage between the fitted and actual values was 2.98% < 10%, indicating that the model fitting accuracy was high; the average relative deviation between the predicted and actual results was 2.67% < 10%, indicating that the model prediction was valid. Conclusion: The model constructed in this study can be used for the analysis and prediction of long-term medical workload in PIVAS. It is recommended to adjust the working model according to the fluctuation characteristics of workload and the prediction results to ensure the efficient operation of PIVAS. |
| keywords:Pharmacy Intravenous Admixture Services workload time series analysis techniques |
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