Technology Diffusion, Health Outcomes, and Healthcare Expenditures

技术扩散、健康成果和医疗支出

• 批准号: 8628306
• 负责人: JONATHAN S SKINNER
• 金额: $ 83.1万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8628306
• 关键词: Adopted; Adverse effects; Age; Amputation; Anticoagulants; Antidiabetic Drugs; Area; Atrial Fibrillation; Back; Beds; Belief; Blood Glucose; Blood Vessels; Cardiac; Cardiovascular Diseases; Carotid Endarterectomy; Categories; Characteristics; Clinical; Cross-Sectional Studies; Data; Databases; Deep Vein Thrombosis; Diffuse; Diffusion; Drops; Ethnic group; Exhibits; Expenditure; Fee-for-Service Plans; Glycosylated hemoglobin A; Growth; Health; Health Care Costs; Health Personnel; Healthcare; Hemoglobin; Hospitals; Immunoglobulins; Implantable Defibrillators; Individual; Infusion procedures; Intensive Care Units; Link; Marketing; Masks; Measures; Medical; Medicare; Medicare claim; Monitor; Neuropathy; Operative Surgical Procedures; Outcome; Patient Selection; Patients; Pattern; Pharmaceutical Preparations; Physicians; Population; Process; Provider; Publications; Quality of Care; Race; Registries; Research; Residencies; Retinal Diseases; Risk; Social Network; Speed; Stents; Structure; Subgroup; System; Technology; Testing; Variant; adverse outcome; bed capacity; blood glucose regulation; cardiovascular risk factor; data registry; diabetic; diabetic patient; effective therapy; experience; falls; illegal behavior; medical schools; medical specialties; organizational structure; public health relevance; randomized trial; rapid growth; rosiglitazone; social

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?

项目总结 在之前的研究中，人们发现医疗支出和健康结果都有很大的差异， 两者之间几乎没有相关性。这些研究仅限于横断面分析，结果很少。 关于这些模式产生的动态过程。一种假设是， 技术扩散速度，无论是高效治疗(对健康结果有很大影响) 或者对于价值未知的昂贵治疗(对支出有很大影响)，可以解释 观察支出和结果的横截面模式。 在这项提案中，Aim 1试图更好地了解高效医疗保健的传播，如 糖化血红蛋白(HbA1c)试验用于糖尿病患者的血糖控制。利用国家的多氧性 美国每个医生的数据库，以及有关医生-医院网络(PHN)和 医生社交网络，研究小组将测试为什么HbA1c传播如此迅速(以及在所有种族和 少数民族)在一些地区，但在其他地区不是。他们还将测试HbA1c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，但治疗实际上可能伤害特定类型的患者。我们考虑了两个例子： 植入式心脏转复除颤器(ICD)的快速增长，以及新的和昂贵的 抗凝剂--达比加特兰、阿皮沙班和利伐沙班。 Aim 3研究了扩散的对立面--“创新”或停止使用--以询问医生-医院如何 网络和地区缩减了新发现的对患者亚群价值较低的治疗。 该提案考虑了两种具体的治疗方法：大幅减少颈动脉内膜切除术(两种手术 和支架)，以及抗糖尿病药物罗格列酮(文迪雅)的使用在2007年之后的下降 显示严重心血管风险的出版物。在这些情况下，最有效的创新模式 对于不太合适的患者，应该经历最大的降幅。 目标4检查具有未知甚至不良后果的治疗的扩散，例如快速 一些地区(但不是其他地区)ICU床位容量增长。研究小组将研究该网络并 “非医疗”治疗的扩散模式--以利润为动机的非法行为，对 一个例子是2002-2005年免疫球蛋白输注量的上升和下降。最后， 研究小组将使用这四个目标的结果来返回中心假设：可以观察到 治疗特异性扩散的差异解释了健康结果地区差异的观察模式 那么支出呢？