Technology Diffusion, Health Outcomes, and Healthcare Expenditures

项目摘要 在以前的研究中，在医疗保健支出和健康结果方面都发现了广泛的差异， 两者之间几乎没有相关性。这些研究仅限于横截面分析， 关于这些模式产生的动态过程。一种假设是， 技术传播速度，无论是高效治疗（对健康结果有很大影响） 或用于价值未知的昂贵治疗（对支出有很大影响），可以解释 观察支出和成果的横截面模式。 在这项提案中，目标1旨在更好地了解高效医疗保健的传播， 血红蛋白A1 c（HbA 1c）测试用于糖尿病患者的血糖控制。使用国家Doximity 美国所有医生的数据库沿着有关医生-医院网络（PHN）的信息， 医生社交网络，研究小组将测试为什么HbA 1c扩散如此迅速（以及在所有种族和 在某些地区，而不是其他地区。他们还将测试HbA 1c的更快扩散是否会减少 神经病变、视网膜病变和截肢率。目标2侧重于传播普遍有益的 治疗，但治疗实际上可能会伤害特定类型的患者。考虑两个例子： 植入式心律转复除颤器（ICD）的快速增长，以及新的和昂贵的 抗凝剂-达比加群、阿哌沙班和利伐沙班。 目标3研究扩散的对立面--“扩张”或从使用中撤退--以询问医生-医院如何 网络和区域缩减了新发现的对患者亚组价值不高的治疗。 该建议考虑了两种特殊的治疗方法：颈动脉内膜切除术（包括手术） 和支架），以及抗糖尿病药物罗格列酮（文迪雅）的使用减少， 证明存在严重心血管风险的出版物。在这些情况下，最有效的外显模式 对于不太适合的患者，使用率下降最大。 目标4审查了具有未知甚至不良后果的治疗的扩散，例如快速 某些地区（但不是其他地区）ICU床位容量的增长。研究小组将研究该网络， “医疗外”治疗的扩散模式-受利润驱使的非法行为， 2002-2005年免疫球蛋白输注量的上升和下降就是一个例子。最后 研究小组将利用这四个目标的结果回到中心假设：可以观察到 治疗特异性扩散的差异解释了观察到的健康结果区域差异模式 和支出？

PROJECT SUMMARY In previous research wide variations have been found in both healthcare spending and in health outcomes, with little correlation between the two. These studies were limited to cross-sectional analysis, and tell little about the dynamic process by which these patterns arise. One hypothesis is that variation across regions in rates of technology diffusion, whether for highly effective treatments (with a large impact on health outcomes) or for expensive treatments with unknown value (with a large impact on expenditures), can explain the observed cross-sectional patterns of spending and outcomes. In this proposal, Aim 1 seeks to better understand the diffusion of highly effective healthcare such as hemoglobin A1c (HbA1c) tests for blood glucose control among diabetic patients. Using the national Doximity database on every physician in the U.S., along with information about physician-hospital networks (PHN) and physician social networks, the research team will test why HbA1c diffused so rapidly (and among all racial and ethnic groups) in some areas but not others. They will also test whether more rapid diffusion of HbA1c reduced rates of neuropathy, retinopathy, and amputation. Aim 2 focuses on the diffusion of generally beneficial treatments but where the treatment can actually harm specific types of patients. Two examples are considered: the rapid growth in implantable cardioverter defibrillators (ICDs), and the growth in new and expensive anticoagulants - dabigatran, apixaban and rivaroxaban. Aim 3 studies the opposite of diffusion - "exnovation" or a retreat from use - to ask how physician-hospital networks and regions scaled back on treatments newly found to have poor value for subgroups of patients. The proposal considers two specific treatments: the sharp reduction in carotid endarterectomy (both surgery and stents), and the decline in the use of Rosiglitazone (Avandia), an anti-diabetic drug, following a 2007 publication demonstrating serious cardiovascular risks. In these cases, the most effective exnovation patterns should experience the largest drop in use for the less appropriate patients. Aim 4 examines the diffusion of treatments with unknown or even adverse consequences, such as the rapid growth in some regions (but not others) in ICU bed capacity. The research team will study the network and diffusion patterns for "extramedical" treatments - illegal behavior motivated by profit and with no benefit for patients, with one example being the rise and fall of immunoglobulin infusions in 2002-2005. Finally, the research group will use results from these four aims to return to the central hypothesis: can observed differences in treatment-specific diffusion explain observed patterns in regional variations in health outcomes and spending?