OBJECTIVE:To strengthen application management of antibiotics in outpatients,promote rational use of antibiot-ics,and to provide reference for scientific management and decision-making in the hospital. METHODS:The proportion of outpa-tients receiving antibiotics in total outpatients was analyzed statistically during Jan. 2008-Jun. 2016. Utilization rate data of antibiot-ics 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. CONCLU-SIONS: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.%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.
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