In the context of the new round of medical and health reform, in order to alleviate the problem of “difficult to see a doctor and expensive to see a doctor,” the state focuses on reducing the cost of medical services, so it puts forward the calculation and method research of medical costs. The purpose of this study is to calculate and predict the cost of medical services in a DRG-oriented integrated environment. In this study, activity-based costing and weighted moving average methods are used. First, basic data of medical services are collected, then all medical activities are confirmed and all service costs are collected, then a cost database is established, and a calculation model of medical costs is designed. Finally, calculation suggestions and optimization methods are put forward by analyzing the calculated data. The experimental results show that the actual demand of drugs predicted by the general moving average method is relatively insufficient, with the maximum error of 41%, the minimum of 5%, and the average error of 19.8%; the maximum error of drug demand predicted by the weighted moving average method is 24%, the minimum is 2%, and the average is 15.4%. It can be concluded that the prediction effect of the weighted moving average method is better than that of the ordinary moving average method, which plays a good and effective role in the prediction of medical cost. The activity-based costing method is more detailed and organized for the cost calculation and classification of medical services. It provides a certain value for the effective management and control of medical service cost.

中文翻译：

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面向DRG的综合医疗服务成本数学计算模型与方法

在新一轮医疗卫生改革背景下，为缓解“看病难、看病贵”问题，国家着力降低医疗服务成本，提出医疗费用的计算与方法研究。本研究的目的是计算和预测面向 DRG 的集成环境中的医疗服务成本。在这项研究中，使用了基于活动的成本计算和加权移动平均方法。首先收集医疗服务基础数据，确定所有医疗活动，收集所有服务成本，然后建立成本数据库，设计医疗成本计算模型。最后通过对计算数据的分析提出计算建议和优化方法。实验结果表明，一般移动平均法预测的药品实际需求量相对不足，最大误差为41%，最小误差为5%，平均误差为19.8%；加权移动平均法预测的药物需求误差最大为24%，最小为2%，平均值为15.4%。可以得出结论，加权移动平均法的预测效果优于普通移动平均法，对医疗费用的预测起到了良好而有效的作用。活动成本法对于医疗服务的成本计算和分类更加详细和有条理。为有效管理和控制医疗服务成本提供了一定的价值。