Understanding hospital value: provider, hospital and community effects

摘要 美国的政策制定者和支付者现在正密切关注医疗保健价值，通常定义为 每一美元所实现的质量，作为同时提高护理质量和减少或 稳定成本。2017年，所有支付者34%的支付是通过基于价值的模式进行的，而只有 41%是通过传统的按服务收费模式。住院护理占美国医疗保健总量的三分之一 这是一项支出最大、历史最悠久的以价值为基础的模式。然而，到目前为止，基于价值的 住院护理计划的结果喜忧参半，或令人沮丧。在没有清楚地了解 帮助医院和社区提供高价值医疗服务的因素，基于价值的支付运动可能不会 成功。该续签提案扩展了在第一个获得资金的R01中成功完成的工作，该R01探索了 与再入院有关的医院和社区因素(质量和成本的一个具体例子)。在.期间 这笔赠款已经在《新英格兰杂志》等期刊上发表了14篇论文 我们开发了一个强大的数据基础设施，将680万条 住院人数超过70个医院和社区因素。在这项研究中，我们将以这些数据为基础 探索从业者、医院和社区与整体价值相关的基础设施 住院医疗保健。在目标1中，我们将使用医疗保险和医疗补助服务中心(CMS)的星级评级 由我们的团队开发的作为主要质量结果的衡量标准(它聚合了57个衡量标准的绩效 死亡率、再入院、安全性、体验、有效性和成像的使用)，以及CMS Medicare 将每个受益人的支出作为我们的主要成本结果。在目标2中，我们将使用其他质量衡量标准， 如死亡率方面表现，或特定条件下的总体质量，以及其他成本衡量标准，如 作为我们团队开发的CMS条件特定风险标准化支付措施。在这两个目标中，我们 将探索提供者、医院和社区因素对结果的影响，以确定 它们有助于医疗保健价值和调解患者因素。在目标1-2中，我们将使用措施 已在CMS、美国国家卫生研究院、人口普查局、 美国医院协会、城市健康仪表板、县健康排名和其他机构。在《目标3》中， 我们将通过直接调查高价值和低价值医院的具体做法来确定新的预测因素 在现有数据集中不可用：例如，质量基础设施、董事会和员工的各个方面 参与、电子健康记录能力、数据基础设施、社区协调等。我们 将评估这些新的预测指标与目标1和目标2中使用的结果的关联。 在授权期内，我们希望对医疗保健价值有一个细致入微的理解，这将使 临床医生和政策制定者改善所有患者的医疗保健服务。

Abstract Policymakers and payers in the US are now focusing intensively on healthcare value, commonly defined as quality achieved per dollar spent, as a means of simultaneously improving quality of care and reducing or stabilizing costs. In 2017, 34% of payments across all payers were through value-based models, while only 41% were through traditional fee-for-service models. Inpatient care accounts for one third of all US health expenditures and has had the most long-standing value-based models. To date, however, value-based programs for inpatient care have had mixed or discouraging results. Without a clear understanding of what factors help hospitals and communities provide high value care, the value-based payment movement may not succeed. This renewal proposal extends work successfully done in the first funded R01, which explored hospital and community factors associated with readmission (one specific example of quality and cost). During that grant, which has already generated 14 publications in journals such as The New England Journal of Medicine and JAMA and 174 citations, we developed a robust data infrastructure that links 6.8 million hospitalizations to over 70 hospital and community factors. In this study, we will build upon that data infrastructure to explore practitioner, hospital and community factors associated with the overall value of inpatient healthcare. In Aim 1, we will use the Centers for Medicare & Medicaid Services (CMS) Star Ratings measure developed by our team as our main quality outcome (which aggregates performance on 57 measures of mortality, readmission, safety, experience, effectiveness, and use of imaging), and the CMS Medicare Spending per Beneficiary measure as our main cost outcome. In Aim 2 we will use other measures of quality, such as performance on mortality, or overall quality for specific conditions, and other measures of cost, such as the CMS condition-specific risk-standardized payment measures developed by our team. In both aims, we will explore the influence of provider, hospital and community factors on outcomes to determine the degree to which they contribute to healthcare value and mediate patient factors. In Aims 1-2 we will use measures already available in a number of datasets from CMS, the National Institutes of Health, the Census Bureau, the American Hospital Association, the City Health Dashboard, the County Health Rankings and others. In Aim 3, we will identify new predictors by directly surveying high and low value hospitals about specific practices that are not available in existing datasets: for instance, aspects of quality infrastructure, Board and staff engagement, electronic health record capabilities, data infrastructure, community coordination and others. We will assess the association of those new predictors with the outcomes used in Aims 1 and 2. By the end of the grant period, we expect to have developed a nuanced understanding of healthcare value that will enable clinicians and policymakers to improve healthcare delivery for all patients.