Epidemiology of Care Teams: Network Analysis of Providers and Shared Patients

描述(由申请人提供)：在一项对照顾联邦医疗保险受益人的初级保健医生的调查中，平均一名医生与在117个诊所工作的229名其他医生协调护理。正常情况下，这相当于每100名医疗保险受益人中有99名医生和53家诊所。美国医疗保健系统规模庞大，为超过3.5亿名患者提供服务，这使得这成为一个“大数据”问题。每一位患者都是一群提供者的中心。我们寻求在一个系统中衡量医疗保健提供者之间的相互联系，在这个系统中，数据往往是由付款人孤立的，因此没有反映许多提供者之间的联系。我们构建了由单一支付者超渗透的选定地理定义的市场的独特数据集，并与之一起定义了卫生保健团队和团队结构的流行病学。基于我们在保险索赔数据网络建模方面的经验，我们采用社交网络分析工具来描述美国卫生系统中支离破碎的团队护理的性质。我们开发了计算方法，以使政策制定者能够理解医疗保健社交网络基础设施的“大数据”问题，这些基础设施与医疗服务提供者和患者有关。除了对提供者关系的社交网络分析以及通过大型网络的信息流和影响力之外，我们还开发了以患者为中心的团队(本质上是围绕单个患者的提供者网络)为主要单位的医疗系统数据的新型社交网络方法。构建了提供商网络，其中每个节点都是单独的提供商。提供者对之间的链接根据它们共享的患者数量进行加权。我们引入了网络衍生的“团队稳定性”概念，并开发了新的方法来量化它。这些创新的指标允许通过美国医疗保健系统中对团队结构的第一次大规模系统测量来阐明团队的基线流行病学。我们假设，国家人口的提供者不断地分类和重新分类成数量惊人的不同护理团队(可能是数百万)。作为我们团队结构有效性的初步测试，我们对团队特征和医疗质量指标之间的关联进行了形成性研究。

DESCRIPTION (provided by applicant): In a survey of primary care physicians taking care of Medicare beneficiaries, an average physician coordinates care with 229 other physicians working in 117 practices. Normalized, this is equivalent to 99 physicians and 53 practices for every 100 Medicare beneficiaries. The sheer magnitude of the US Healthcare system, providing services to more than 350 million patients, makes this a "big data" problem. Each patient is at the center of a cluster of providers. We seek to measure interconnectedness among health care providers in a system where data tend to be siloed by payor, and therefore do not reflect many inter-provider connections. We construct a unique dataset of selected geographically-defined markets, ultra-penetrated by a single payor and with it, define the epidemiology of health care teams and team structure. Building on our experience in network modeling with insurance claims data, we adapt tools from social network analysis to characterize the nature of fragmented team care in the US health system. We develop computational methods to allow policy makers to understand the "big data" problem of the healthcare social network infrastructure of linked providers and patients. Beyond social network analysis of provider relationships and the flow of information and influence through large networks, we develop novel social networking methods for health system data using the patient-centered team (essentially a network of providers around a single patient) as the primary unit. A provider network is built where each node is an individual provider. Links between provider pairs are weighted by the number of patients they share. We introduce the network-derived concept of "team stability" and develop novel methods to quantify it. These innovative metrics allow elucidation of the baseline epidemiology of teams through the first broad-scale system measurements of team structure in the US Healthcare system. We hypothesize that that the national population of providers constantly assorts and re-assorts into a staggeringly large number of different care teams (perhaps millions). As an initial test of the validity of our team constructs, we conduct a formative study of the association between team characteristics and health care quality metrics.