Silber Jeffrey H, Satopää Ville A, Mukherjee Nabanita, Rockova Veronika, Wang Wei, Hill Alexander S, Even-Shoshan Orit, Rosenbaum Paul R, George Edward I
Center for Outcomes Research, The Children's Hospital of Philadelphia, Philadelphia, PA.
The Department of Pediatrics, The University of Pennsylvania School of Medicine, Philadelphia, PA.
Health Serv Res. 2016 Jun;51 Suppl 2(Suppl 2):1229-47. doi: 10.1111/1475-6773.12478. Epub 2016 Mar 14.
To improve the predictions provided by Medicare's Hospital Compare (HC) to facilitate better informed decisions regarding hospital choice by the public.
DATA SOURCES/SETTING: Medicare claims on all patients admitted for Acute Myocardial Infarction between 2009 through 2011.
Cohort analysis using a Bayesian approach, comparing the present assumptions of HC (using a constant mean and constant variance for all hospital random effects), versus an expanded model that allows for the inclusion of hospital characteristics to permit the data to determine whether they vary with attributes of hospitals, such as volume, capabilities, and staffing. Hospital predictions are then created using directly standardized estimates to facilitate comparisons between hospitals.
DATA COLLECTION/EXTRACTION METHODS: Medicare fee-for-service claims.
Our model that included hospital characteristics produces very different predictions from the current HC model, with higher predicted mortality rates at hospitals with lower volume and worse characteristics. Using Chicago as an example, the expanded model would advise patients against seeking treatment at the smallest hospitals with worse technology and staffing.
To aid patients when selecting between hospitals, the Centers for Medicare and Medicaid Services (CMS) should improve the HC model by permitting its predictions to vary systematically with hospital attributes such as volume, capabilities, and staffing.
改进医疗保险机构的医院比较（HC）所提供的预测，以便公众在选择医院时能做出更明智的决策。
数据来源/背景：2009年至2011年期间所有因急性心肌梗死入院患者的医疗保险理赔数据。
采用贝叶斯方法进行队列分析，比较HC目前的假设（对所有医院随机效应使用恒定均值和恒定方差）与一个扩展模型，该扩展模型允许纳入医院特征，以便数据能够确定这些特征是否因医院的属性（如规模、能力和人员配备）而有所不同。然后使用直接标准化估计值生成医院预测结果，以方便医院之间的比较。
数据收集/提取方法：医疗保险按服务收费理赔数据。
我们纳入医院特征的模型所产生的预测结果与当前的HC模型有很大不同，规模较小且特征较差的医院预测死亡率较高。以芝加哥为例，扩展模型会建议患者不要在技术和人员配备较差的最小型医院寻求治疗。
为了在患者选择医院时提供帮助，医疗保险和医疗补助服务中心（CMS）应改进HC模型，使其预测结果能根据医院的规模、能力和人员配备等属性系统地变化。
