Variation in Adoption of Evidence-Based and Patient Centered Care at the Delivery System Level

项目摘要/摘要 由于日益增长的整合趋势，集成交付系统正变得越来越重要 在《平价医疗法案》颁布的迅速扩大的医疗改革努力中。巨大的 不同的交付系统在其组织特征和业务方面存在差异。 然而，对于护理质量如何或为什么不同，或者个人如何衡量，知之甚少。 由于缺乏系统收集的关于卫生系统从属关系、系统 特点或其内部机制的使用，以进一步实现质量目标。因此，这项研究解决了 文献中的显著差距有三个目标：(1)研究不同提供商的质量绩效差异 水平，(2)研究系统特征和质量性能之间的关系，以及(3)研究 质量改进活动与质量的关系。这项研究依赖于先进的经验 方法并使用几个独特的数据集，包括个人级别的护理体验和对护理的坚持 收费医疗保险参保人的流程，以及新的全面的国家级美国数据集。 卫生系统和具有全国代表性的关于组织业务的调查，包括其使用特定的 提高卫生保健服务效果和效率的机制。我将采用层次化线性 研究目标1和目标2的建模(HLM)。对于目标1，系统和练习识别符的随机截获为 用于评估交付系统内部和之间的差异。要了解系统的不同程度 在他们照顾不同患者群体的能力中，患者协变量的随机斜率将被添加到模型中。 为了研究目标2，我使用了对系统具有随机截获和对系统属性具有固定影响的HLMS 确定组织的结构特征与更好或更差的关联程度 关心。对于目标3，我使用了一种双重稳健的方法来评估使用金融和非金融工具的影响 关于质量结果的激励机制。

Project Summary/Abstract Integrated delivery systems are becoming increasingly important due to increasing trends toward consolidation in the midst of rapidly expanding health care reform efforts enacted by the Affordable Care Act. Enormous variation exists across delivery systems in terms of their organizational characteristics and operations. However, little knowledge exists regarding how or why quality of care varies or how individual measures correlate at this level due to a lack of systematically collected data regarding health system affiliations, system characteristics or their use of internal mechanisms to further quality goals. This study, therefore, addresses significant gaps in the literature with three aims: (1) study variation in quality performance across provider levels, (2) study the association between system characteristics and quality performance, and (3) study the relationship between quality improvement activities and quality. The study relies on advanced empirical methods and uses several unique data sets including individual-level care experiences and adherence to care processes for Fee-for-service Medicare enrollees, and a new, comprehensive, national-level dataset on U.S. health systems and a nationally representative survey on organizational operations including its use of specific mechanisms to improve the effectiveness and efficiency of health care delivery. I will employ hierarchical linear modeling (HLM) to study aims 1 and 2. For Aim 1, random intercepts for system and practice identifiers are used to assess variation within and across delivery systems. To understand the extent to which systems differ in their ability to care for different patient groups, random slopes for patient covariates will be added to models. To study Aim 2, I use HLMs with a random intercept for the system and fixed effects for the system attributes to determine the extent to which structural features of the organizational are associated with better or worse care. For Aim 3, I employ a doubly-robust method to evaluate the impact of using financial and non-financial incentive mechanisms on quality outcomes.