运用时间序列模型预测门诊患者抗菌药物使用率趋势

Trend Prediction of Antibiotics Utilization Rate in Outpatients by Time Series Model

目的:加强抗菌药物门诊应用管理,促进抗菌药物的合理使用,为医院的科学管理决策提供参考。方法:统计我院2008年1月-2016年6月的门诊患者使用抗菌药物例次占同期门诊总例次比例,将2008-2015年的门诊患者抗菌药物使用率数据用于建立自回归移动平均模型(ARIMA),2016年上半年数据用于验证所建立的模型,并预测2016年下半年门诊患者抗菌药物使用率趋势;采用SPSS 20.0软件进行统计分析。结果:建立的ARIMA(2,1,0)(2,1,0)12模型具有较高的拟合度,2016年上半年门诊患者抗菌药物使用率实际值与拟合值相差很小,平均绝对误差为0.72%,平均相对误差为4.20%,且都在拟合值的95%置信区间内;模型预测值的动态趋势与实际值基本一致。结论:ARIMA较好地模拟了医院门诊患者抗菌药物使用率趋势,可用于门诊患者抗菌药物使用率趋势的短期预测和动态分析,但在远期预测时,还应综合多方面因素考虑。

OBJECTIVE：To strengthen application management of antibiotics in outpatients,promote rational use of antibiotics,and to provide reference for scientific management and decision-making in the hospital. METHODS：The proportion of outpatients receiving antibiotics in total outpatients was analyzed statistically during Jan. 2008-Jun. 2016. Utilization rate data of antibiotics in outpatients during 2008-2015 were used to establish Autoregressive integrated moving average model（ARIMA）,and the data of the first half of 2016 was used to validate established model;the utilization rate trend of antibiotics in outpatients in the second half of 2016 was predicted. SPSS 20.0 statistical software was adopted for statistical analysis. RESULTS：Established ARIMA（2,1,0）（2,1,0）12 model has higher fitting degree. There was a small difference between measured value and fitted value of utilization rate of antibiotics in outpatients in 2016. Average absolute error was 0.72%,and average relative error was 4.20%,within 95%confidence interval of fitted value. Dynamic trend of model predicted value was basically consistent with measured value. CONCLUSIONS：ARIMA model simulates utilization rate trend of antibiotics in outpatients well,can be used for short-term prediction and dynamic analysis of utilization rate trend of antibiotics. However,for long-term prediction,various factors should be considered.