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

项目总结/摘要 由于整合趋势的增加，集成交付系统变得越来越重要 在《平价医疗法案》颁布的迅速扩大的医疗改革努力中。巨大 各交付系统在组织特征和运作方面存在差异。 然而，关于护理质量如何或为何不同或个人如何衡量 由于缺乏系统收集的有关卫生系统附属机构、系统 特性或使用内部机制来进一步实现质量目标。因此，本研究针对 文献中的重大差距有三个目的：（1）研究供应商质量绩效的变化 水平，（2）研究系统特性和质量绩效之间的关联，（3）研究 质量改进活动与质量的关系。该研究依赖于先进的经验 方法和使用几个独特的数据集，包括个人层面的护理经验和坚持护理 流程，以及一个新的，全面的，国家级的数据集， 卫生系统和具有全国代表性的关于组织运作的调查， 提高保健服务的效力和效率的机制。我将采用分层线性 模型（HLM）研究目标1和2。对于目标1，系统和实践标识符的随机截取是 用于评估输送系统内和输送系统间的差异。为了了解系统之间的差异程度， 在他们护理不同患者组的能力方面，将患者协变量的随机斜率添加到模型中。 为了研究目标2，我使用HLM，其中系统具有随机截距，系统属性具有固定效应 以确定组织结构特征与更好或更差相关的程度 在乎对于目标3，我采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。 质量成果的激励机制。

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.