Technology Diffusion, Health Outcomes, and Healthcare Expenditures

技术扩散、健康成果和医疗支出

• 批准号: 9111769
• 负责人: JONATHAN S SKINNER
• 金额: $ 101.39万
• 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 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9111769
• 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; randomized trial; rapid growth; rosiglitazone; social; technology diffusion; treatment group

DESCRIPTION: 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?

描述：在先前的研究中，在医疗保健支出和健康结果中都发现了广泛的变化，两者之间的相关性几乎没有。这些研究仅限于横截面分析，几乎没有说明这些模式出现的动态过程。一个假设是，无论是高度有效的治疗方法（对健康结果的影响很大）还是对具有未知价值的昂贵处理（对支出产生很大影响）的技术扩散率之间的变化，都可以解释观察到的支出和成果的横截面模式。在此提案中，AIM 1试图更好地了解高效医疗保健的扩散，例如血红蛋白A1C（HBA1C）测试糖尿病患者的血糖控制。研究团队将在美国的每位医生中使用国家Doximity数据库，以及有关医师院医院网络（PHN）和医师社交网络的信息，研究团队将测试HBA1C在某些领域（以及某些领域）而不是其他领域而不是其他领域的HBA1C如此迅速地扩散了。他们还将测试HBA1C的更快扩散是否降低了神经病，视网膜病和截肢的速率。 AIM 2的重点是通常有益治疗的扩散，但治疗实际上可能损害特定类型的患者。考虑了两个例子：植入式心脏逆转除颤器（ICD）的快速增长以及新的且昂贵的抗凝剂的生长-Dabigatran，Apixaban和Rivaroxaban。 AIM 3研究扩散的对立面是“分发”或使用的撤退 - 询问医师院网络和区域如何根据新发现的治疗方法缩减对患者亚组的价值较差。该提案考虑了两种特定的治疗方法：颈动脉内膜切除术（手术和支架）的急剧减少，以及在2007年出版的严重心血管疾病风险之后，抗糖尿病药物（一种抗糖尿病药物）的使用（Avandia）的使用下降。在这些情况下，最有效的分发模式应遭受较不合适的患者使用的最大使用情况。 AIM 4检查了具有未知甚至不利后果的治疗的扩散，例如某些地区（但其他地区）在ICU床容量中的迅速增长。研究小组将研究“外部”治疗的网络和扩散模式 - 非法行为是由利润促进的，对患者没有好处，一个例子是2002 - 2005年免疫球蛋白输注的兴衰。最后，研究小组将利用这四个目的的结果来返回中心假设：可以观察到的特定于治疗特异性扩散的差异解释了健康结果和支出的区域变化中观察到的模式吗？