摘要：
目的:探索不同反向事实构建方法对医院财务数据预测的效率,以期对政策进行更有效的评估.方法:借助R软件,用南京市公立医院A在2011—2016年的药品收入、医疗服务收入建立测试数据集,分别用ARIMA模型、BP神经网络、ARIMA+BP组合模型进行预测并与实际拟合,并比较改革前后补偿率.结果:三个模型对药品收入的均方根误差分别为692.82、501.44、380.80,医疗服务收入的均方根误差分别为184.04、215.63、168.65,组合模型预测效率更高.用组合模型计算改革后A医院药品收入净损失为12044.03万元,医疗服务收入净增长为18532.60万元,为药品收入损失的153.87%.结论:医院财务数据因其线性与非线性的组合特征,使用组合预测模型的预测效果最佳.但在实际应用中,ARIMA模型操作简单,与组合模型预测趋势也较为一致,在实际卫生政策评估中也推荐使用.%Objective:To study the effectiveness of different time series models in the prediction of financial data in public hospitals,with the aim of obtaining a more reliable counterfactual in health policy evaluation. Methods:ARI-MA model,BP neural network and their combination were used for the estimation and prediction of drug revenue and medical service revenue based on a dataset for the period from November,2011 to October,2016 for hospital X before and after Nanjing medical pricing reform. Root mean square error (RMSE) was used to estimate the model accuracy. Results:RMSE of drug revenue from the three models were 692.82,501.44 and 380.80,and of medical service were 184.04,215.63 and 168.65. The findings shows that the combination model was proved to be the most efficient one a-mong the three. The combined model was used to calculate the net loss of drug revenue which was estimated to be 120, 440 million,and the net increase of medical service was estimated to be 185,326 million after the reform,which was 1. 539 times of the drug loss. Conclusions:The revenue data of public hospitals are usually complex with a both linear and non-linear trend. The combination model of ARIMA and BP neural network could solve the problem for once with an acceptable accuracy. However,ARIMA model is simpler to operate as compared to other two models, and also more consistent with the forecasting trend,therefore ARIMA is also recommended in the evaluation for health policies.
