在诊断相关群体中使用病例混合指数来评估大型学术医疗中心住院费用的变化
Selina Pi1, Jonathan Masterson2, Stephen P Ma3
1Department of Biomedical Data Science, Stanford, California, USA.
与诊断相关的群体权重 (DRG) 解释了只有37%的住院费用变化. 使用入院前数据的预测模型确定了可修改的因素,为卫生系统提供了提高质量和降低成本的机会.
科学领域:
- 卫生经济学 卫生经济学
- 医疗保健管理的管理
- 临床信息学 临床信息学
背景情况:
- 目前的指标,如诊断相关组 (DRG) 权重,解释了住院费用的有限变化.
- 单独的案例组合指数 (CMI) 无法完全考虑成本变化,留下了显著的不明原因差异.
研究的目的:
- 识别和准特定的DRG,以高于预期的成本为质量改善举措.
- 开发医院住院费用的预测模型,使用入院前的数据来发现可修改的因素.
主要方法:
- 从学术医疗中心分析直接住院费用和临床数据.
- 使用CMI作为基准来隔离高成本的DRG.
- 开发使用入院前患者信息的预测模型.
主要成果:
- DRG 权重仅解释了 37% 的成本变化.
- 使用入学前数据的预测模型解释了高负载DRG的成本变化率高达60%.
- 无法解释的成本变化表明存在可修改的运营因素.
结论:
- 医院住院费用变化的很大一部分仍然无法通过当前的指标来解释.
- 入院前的数据可以预测成本变化的很大一部分,突出患者特定的因素.
- 对不明原因变异的进一步调查可以揭示改善医疗保健质量和价值的操作目标.
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