从中分离小麦:如何在例行数据中衡量医院质量?
Jana Bilger1, Mark Pletscher1, Tobias Müller1
1Department of Health, Institute of Health Economics & Policy, Bern University of Applied Sciences, Bern, Switzerland.
Health services research
|January 23, 2024
概括
常规医院数据可以测量患者选择的质量指标. 像 LASSO 和 Mundlak 校正等先进模型可以改善风险调整,有效地识别高质量的医院.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 衡量医院质量对于患者决策和医疗保健系统改进至关重要.
- 现有的方法通常依赖于有限的数据或标准的风险调整技术.
- 质量指标的国际变化需要强大且可适应的测量方法.
研究的目的:
- 评估在国际医疗保健系统中测量医院质量的常规行政数据的使用情况.
- 为了比较先进的统计模型 (LASSO,Mundlak校正) 与医院质量评估标准后勤回归的性能.
- 用改进的风险调整方法识别高质量和低质量医院的选择性手术.
主要方法:
- 利用瑞士行政医院的住院数据 (2017-2019年).
- 应用了来自国际领先机构的 inguinal hernia修复和关节置换手术的质量指标.
- 用于最少绝对收缩和选择操作员 (LASSO) 进行变量选择和 Mundlak 纠正未观察到的医院异质性,以加强风险调整.
主要成果:
- 与标准逻辑回归相比,LASSO选择和Mundlak校正的随机效应逻辑模型表现出优越的统计特性和解释能力.
- 这些先进的模型确定了一致的高质量和低质量的医院.
- 观察到医院质量的显著变化,强调需要准确的测量.
结论:
- 常规医院数据足以开发临床相关的质量指标.
- 先进的统计技术为公共卫生当局提供了与标准模型有价值的替代方案.
- 改善风险调整有助于患者做出明智的选择,提高医疗保健质量.
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