开发一种新型医院支付系统 - 大数据诊断和干预包
Hua Xie1, Xin Cui1, Xiaohua Ying2
1Shanghai Municipal Health Bureau, Office of Information and Technology, Shanghai, China.
一个新的大数据诊断和干预包 (BD-DIP) 系统提供了一个比诊断相关组 (DRG) 更同质的医院支付模式. 在试点研究中,BD-DIP实现了显著的成本节约和提高医院效率.
科学领域:
- 卫生经济学 卫生经济学
- 医疗保健服务研究 医疗服务研究
- 大数据分析大数据分析
背景情况:
- 诊断相关组 (DRG) 是一个常见的未来医院支付系统.
- 由于分组中的同质性不足,DRG系统在准确比较资源需求方面存在局限性.
研究的目的:
- 开发和评估一个新的医院支付和管理系统,大数据诊断和干预包 (BD-DIP).
- 通过创建更均的分组方法来解决DRG系统的局限性.
主要方法:
- 使用案例组合指数 (CMI) 的原则开发了BD-DIP,并根据ICD-10和ICD-9v3代码进行分组.
- 将原型应用于上海的医院出院记录,并在中国广州进行试点.
- 将BD-DIP的变化系数与美国DRG系统进行比较.
主要成果:
- BD-DIP的平均变化系数比美国DRG系统小约三分之一.
- 试点实施导致医院预算节省约5%并提高了护理效率.
- 观察到机构CMI增加,入院率降低,费用变化减少,患者成本共享减少.
结论:
- 与DRG模型相比,BD-DIP提供了优势,包括更好的资源利用均性,更简单的设计和动态分组.
- 该系统通过反映现实世界的治疗途径和成本,提供了更高的报销价值.
- BD-DIP易于实施,并通过监控工具促进识别潜在的不规则行为.
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