Methods for Profiling Hospital Performance Based on Healthcare-AssociatedInfections

项目总结/摘要 医疗保健相关感染（HAI）影响31例住院患者中的1例， 潜在可预防的患者伤害的原因。医疗保险和医疗补助服务中心 (CMS)将结肠手术部位感染（SSI）和其他HAI率纳入指标， 用来对医院的医疗质量进行排名国家政策对医院排名的依赖 强调需要一种可靠的方法，可以正确区分有意义的差异 与患者群体的差异或随机变化相反。拟议工作 旨在开发改进的医院分析方法，并解决三个方法上的差距 利用来自189家社区医院网络的详细管理和临床数据。 分析医院绩效需要风险调整，这需要选择患者水平 这些特征预测SSI风险，同时考虑医院内的集群。然而，在这方面， 由于在处理聚类数据时存在挑战， 复杂的依赖结构目标1提出了一个新的变量选择框架 对于高维聚类数据，容纳缺失的协变量。关切 提出了关于手术量低的医院排名的可靠性。目标2建议， 开发分析工具，可用于确定特定环境下所需的外科手术 用户指定的错误分类为性能最差的 四分位数旨在提高医院排名可靠性的方法也将通过 汇集多年或多个指标的信息。目标3：发展 比较不同排名系统和识别医院特征的有效方法 造成了差异。将开发方便用户的软件， 实施新方法。方法开发将由HCA结肠SSI指导 数据集和AHRQ HCUP的NIS数据库（2014-2016）。该方法具有广泛的应用前景 HAI和其他重要疾病（如败血症）的结局。拟议的研究是 重要的是，因为成功解决这些问题将提高区分 医院间真正有意义的HAI率差异与不同人为因素之间的差异 患者人群，或者可能被低手术量所掩盖的人群。创新 在于开发新的方法和工具，以更好地调整风险， 医院排名的可靠性，并比较排名系统。建议的结果 研究将有助于为正在进行的绩效工资计划的决策提供信息， 最终提高我们预防HAI的能力并提高护理质量。

Project Summary/Abstract Healthcare-associated infections (HAIs) affect 1 in 31 hospitalized patients and are a significant cause of potentially preventable patient harm. The Centers for Medicare and Medicaid Services (CMS) incorporates colon surgical site infections (SSIs) and other HAI rates in metrics that are used to rank hospitals on their quality of care. The reliance of national policy on hospital rankings underscores the need for robust methodology that can properly distinguish meaningful differences in care as opposed to differences in patient populations or random variation. The proposed work aims to develop improved methods for hospital profiling and addresses three methodological gaps leveraging detailed administrative and clinical data from a network of 189 community hospitals. Profiling hospital performance requires risk-adjustment, which entails selecting patient-level characteristics that predict SSI risks while accounting for clustering within hospitals. However, variable selection procedures are limited for clustered data due to challenges in handling the complex dependence structure. Aim 1 proposes to develop a new variable selection framework for high-dimensional clustered data, accommodating missing covariates. Concerns have been raised about the reliability of rankings for hospitals with a low surgical volume. Aim 2 proposes to develop analytic tools that can be used to determine, for a particular setting, the required surgical volume for a user-specified threshold of the rate of misclassifying into the worst-performing quartile. Methods that aim to improve the reliability of hospital rankings will also be developed by pooling information from multiple years or from multiple indicators. Aim 3 proposes to develop valid methods for comparing different ranking systems and for identifying hospital characteristics that contribute to the differences. User-friendly software will be developed to facilitate the implementation of new methods. The methods development will be guided by an HCA colon SSI dataset and the AHRQ HCUP’s NIS database (2014-2016). The methods can be applied broadly to HAIs and outcomes of other important conditions such as sepsis. The proposed research is significant, because success in addressing these issues will improve the ability to distinguish differences in HAI rates across hospitals that are truly meaningful versus an artifact of different patient populations, or that might otherwise be masked by the low surgical volume. Innovation lies in the development of new methods and tools for better risk-adjustment, to increase the reliability of hospital rankings, and for comparing ranking systems. The results of the proposed research will help inform decision-making on the ongoing pay-for-performance programs, and ultimately improve our capacity to prevent HAIs and improve quality of care.