Fluctuation analysis and prediction of intravenous medication dispensing workload based on time series analysis method
10.12206/j.issn.2097-2024.202206012
- VernacularTitle: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
- Author:
Liuliu ZONG
1
;
Yunzhi YANG
1
;
Donghui LAO
1
;
Xiaoyu LI
1
;
Qianzhou LYU
1
Author Information
1. Department of Pharmacy, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China.
- Keywords:
pharmacy intravenous admixture services;
workload;
time series analysis method.
- From:
Journal of Pharmaceutical Practice
2023;41(9):561-565
- CountryChina
- Language:Chinese
-
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.
