Estimating Hospital Quality with Quasi-Experimental Data

Peter Hull
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引用次数: 73

Abstract

Non-random sorting can bias observational measures of institutional quality and distort quality-based polices. I develop alternative quasi-experimental approaches to quality estimation that accommodate nonlinear causal effects, institutional specialization, and unobserved selection-on-gains. I use this framework to compute empirical Bayes posteriors of the quality of 4,821 U.S. hospitals, combining estimates from ambulance referral quasi-experiments with predictions from observational risk-adjustment models. Higher-spending, higher-volume, and privately-owned hospitals are of higher quality, and most healthcare markets exhibit positive Roy selection-on-gains. I then simulate Medicare reimbursement and consumer guidance programs based on different hospital quality measures. Higher-spending providers tend to see moderately larger performance-linked subsidies when quality posteriors replace conventional rankings, while teaching hospitals are reimbursed relatively less. Admissions policy simulations highlight limitations of consumer guidance programs in settings with unobserved Roy selection: redirecting patients to top-ranked hospitals may worsen expected survival when based on observational rankings, while quasi-experimental rankings appear to generate modest gains.
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用准实验数据估计医院质量
非随机排序会对机构质量的观察性测量产生偏差,并扭曲基于质量的政策。我开发了另一种准实验方法来进行质量估计,以适应非线性因果效应、制度专业化和未观察到的增益选择。我使用这个框架来计算4821家美国医院质量的经验贝叶斯后验,将救护车转诊准实验的估计与观察性风险调整模型的预测相结合。支出较高、业务量较大的私立医院质量较高,大多数医疗保健市场表现出积极的Roy选择收益。然后,我根据不同的医院质量衡量标准模拟医疗保险报销和消费者指导计划。当高质量排名取代传统排名时,高支出的医疗机构往往会获得相对较大的与绩效挂钩的补贴,而教学医院的报销相对较少。招生政策模拟强调了消费者指导计划在未观察到的罗伊选择环境中的局限性:当基于观察性排名时,将患者重新定向到排名靠前的医院可能会降低预期生存率,而准实验排名似乎会产生适度的收益。
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