Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare
面向公开报告的死亡率估计与标准化：Medicare 医院比较AI 解读 ↓
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面向公开报告的死亡率估计与标准化：Medicare 医院比较
- 研究问题
- 如何校准医院层面的个体化死亡率预测，并在患者风险构成不同的情况下生成可比较的医院标准化死亡率？
- 方法与思路
- 作者以样本外汇总预测核对另一样本的群体经验结论，修订随机效应 logit 模型，并比较直接与间接标准化。
- 材料陈述的贡献
- 摘要称加入医院规模、人员与手术能力信息可改善校准；现用间接标准化低估低量医院死亡率，作者建议直接标准化。
- 阅读关注点
- 应核对样本外校准的分组和验证设计、医院特征的纳入方式、标准化目标人群，以及模型风险调整对医院比较的影响。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；摘要未提供验证细节或标准化权重，结论限于所述急性心肌梗死死亡率分析。
原文摘要
Bayesian models are increasingly fit to large administrative datasets and then used to make individualized recommendations. In particular, Medicare’s Hospital Compare webpage provides information to patients about specific hospital mortality rates for a heart attack or acute myocardial infarction (AMI). Hospital Compare’s current recommendations are based on a random-effects logit model with a random hospital indicator and patient risk factors. Except for the largest hospitals, these individual recommendations or predictions are not checkable against data, because data from smaller hospitals are too limited to provide a meaningful check. Before individualized Bayesian recommendations, people derived general advice from empirical studies of many hospitals, for example, prefer hospitals of Type 1 to Type 2 because the risk is lower at Type 1 hospitals. Here, we calibrate these Bayesian recommendation systems by checking, out of sample, whether their predictions aggregate to give correct general advice derived from another sample. This process of calibrating individualized predictions against general empirical advice leads to substantial revisions in the Hospital Compare model for AMI mortality. To make appropriately calibrated predictions, our revised models incorporate information about hospital volume, nursing staff, medical residents, and the hospital’s ability to perform cardiovascular procedures. For the ultimate purpose of comparisons, hospital mortality rates must be standardized to adjust for patient mix variation across hospitals. We find that indirect standardization, as currently used by Hospital Compare, fails to adequately control for differences in patient risk factors and systematically underestimates mortality rates at the low volume hospitals. To provide good control and correctly calibrated rates, we propose direct standardization instead. Supplementary materials for this article are available online.