| 引用本文：马爱霞, 谢静, 唐文熙.ARIMA模型、BP神经网络及其组合模型在卫生政策评估中的实证比较:以公立医院价格改革为例[J].中国卫生政策研究,2018,11(1):76-83 |
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| ARIMA模型、BP神经网络及其组合模型在卫生政策评估中的实证比较:以公立医院价格改革为例 |
| 投稿时间：2017-07-17 修订日期：2017-10-18 PDF全文浏览 HTML全文浏览 |
| 马爱霞, 谢静, 唐文熙 |
| 中国药科大学国际医药商学院 江苏南京 211198 |
| 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12 044.03万元，医疗服务收入净增长为18 532.60万元，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。 |
| 关键词:ARIMA模型 BP神经网络 组合模型 药品收入 医疗服务收入 政策评价 |
| 基金项目:国家自然科学基金青年项目（71603278）；江苏省教育厅哲学社会科学基金（2016SJD630007） |
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| A comparison of ARIMA Model, BP Neural Network Model and combined model in health policy evaluation: An empirical study of public hospitals pricing reform |
| MA Ai-xia, XIE Jing, TANG Wen-xi |
| School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing Jiangsu 211198, China |
| Abstract: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:ARIMA 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 among 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. |
| Key words:ARIMA model BP neural network Combined model Drug revenue Medical service revenue Policy evaluation |
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