基于大数据的病种分值付费组别高套发现机制研究  

Monitoring mechanism for high group behavior based on the big data diagnosis-intervention packet

作　　者：崔欣 谢桦 杨羽佳 宣建伟 李莉[3] 汪森然[3] 许速 Cui Xin;Xie Hua;Yang Yujia;Xuan Jianwei;Li Li;Wang Senran;Xu Su(Shanghai Information Center for Health,Shanghai 200040,China;School of Pharmaceutical Sciences,Sun Yat-Sen University,Guangzhou 510275,China;Wonders Information Co.,Ltd.,Shanghai 200040,China;Shanghai Municipal Health Commission,Shanghai 200125,China)

机构地区：[1]上海市卫生健康信息中心,200040 [2]中山大学药学院,广州510275 [3]万达信息股份有限公司,上海200040 [4]上海市卫生健康委员会,200125

出　　处：《中华医院管理杂志》2021年第3期196-198,共3页Chinese Journal of Hospital Administration

基　　金：国家重点研发计划重点专项 (2016YFC1305605);国家自然科学基金 (71003024);上海市卫生健康委员会政策研究课题 (2020HP17)。

摘　　要：如何发现和避免医疗机构的组别高套行为是按病种付费支付方式面临的挑战之一。作者依据大数据的分析方法,通过分析某一诊断对应治疗方式的客观分布特征,以区域内高、低分值病种的分布情况为标准,对比发现各医院针对相同诊断的治疗方式选择趋势,结合医院定位,发现是否存在组别高套倾向,为医保费用合理支付和医院发展规划提供科学支撑。One of the challenges to diagnosis-intervention packet is how to detect and avoid the institutional behavior of pursuing a higher score group.Based on the analysis method of big data,the authors analyzed the objective distribution characteristics of the treatment methods corresponding to a diagnosis,and compared the distribution of diseases with high and low scores in the region to find out the selection trend of treatment methods for the same diagnosis in various hospitals.Combined with hospital positioning,the authors found out whether there was a tendency of pursuing a higher score group.Scientific support will be provided for the reasonable payment of medical insurance expenses and the development planning of hospitals.

关 键 词：大数据 病种分值付费 组别高套 发现机制 

分 类 号：R197.1[医药卫生—卫生事业管理]