Effectiveness of advanced practice pharmacy services among American Indian and Alaska Native adults with diabetes

高级实践药房服务在美国印第安人和阿拉斯加原住民中的有效性 糖尿病 印第安人健康服务(IHS)为大约220万美国印第安人和阿拉斯加人提供服务 原住民(人工智能/ANS)。由于人均支出有限，IHS卫生保健资源紧张， 治疗患有糖尿病的AI/AN的不成比例的高成本，以及提供者短缺。全死因死亡 AI/ANS比非西班牙裔白人高46%，部分原因是与以下疾病相关的较高死亡率 糖尿病。在IHS内部，为成人糖尿病患者提供高级实践药房(APP)服务 增加了。在2008财年(FY)期间，大约1%的患有糖尿病的AI/AN使用APP。只有5年 之后，9.6%(n=4,620)的受访者在2013财年至少访问过一次应用程序。到目前为止，IHS还没有足够的机构能力 充分描述APP服务的提供特点，也不研究其有效性或成本。 考虑到既需要改善糖尿病患者的预后，又需要有效利用有限的IHS 资源，本研究的目标是描述和评估IHS内新兴APP模式的有效性 用于治疗糖尿病。自2010年以来，IHS和部落一直与美国印第安人中心合作 和科罗拉多大学的阿拉斯加原住民健康中心合作，创建一个纵向数据基础设施 超过640,000个人工智能/人工智能的状态、服务利用率和治疗成本数据，占近30% 使用IHS服务的AI/AN。通过改善医疗服务提供数据创建的基础设施 项目是来自多个IHS平台的现有电子数据的综合，目前包括7个 15个项目工地的年度(2007-2013财年)。我们建议通过更新 使用最新数据(2014财年-2017财年)评估AI/AN成人应用程序的有效性 使用统计技术使纵向数据成为可能。这项研究有三个目标： 1.描述成人糖尿病患者在站点内和随时间使用站点的应用程序交付模式 特征和患者健康风险概况(例如，血糖水平[A1c]，血压[BP]， 胆固醇[CHL]，心血管疾病)我们预计可能会出现多达3种模型(例如， 目标、有限和一般)；以及 2.对于每种APP模式，评估患者使用APP与应用程序之间关系的性质和程度 结果。在每种模式中，我们预计APP将改善服药依从性和A1c、BP和 降低CHL水平，减少并发症的发生和可预防的住院时间；以及 3.估计应用程序交付成本、与减少使用其他健康服务相关的治疗成本变化， 和成本效益。我们预计这些应用程序的型号将具有成本效益。 1

Effectiveness of advanced practice pharmacy services among American Indian and Alaska Native adults with diabetes The Indian Health Service (IHS) funds services for approximately 2.2 million American Indians and Alaska Natives (AI/ANs).IHS health care resources are strained due to limited per capita spending, the disproportionate high costs of treating AI/ANs with diabetes, and provider shortages. All-cause mortality of AI/ANs is 46% higher than that of non-Hispanic whites, attributable in part to higher mortality associated with diabetes. Within IHS, the provision of advanced practice pharmacy (APP) services for adults with diabetes has increased. During fiscal year (FY) 2008, approximately 1% of AI/ANs with diabetes used APP. Just 5 years later, 9.6% (n=4,620) had at least 1 APP visit in FY2013. To date, IHS has not had the institutional capacity to fully characterize the provision of APP services, nor study its effectiveness or costs. Given the need to both improve outcomes for patients with diabetes and effectively utilize limited IHS resources, this study's goal is to describe and assess the effectiveness of emerging models of APP within IHS for treatment of diabetes. Since 2010, IHS and Tribes have collaborated with the Centers for American Indian and Alaska Native Health at the University of Colorado to create a longitudinal data infrastructure with health status, service utilization, and treatment cost data for over 640,000 AI/ANs who represent nearly 30% of AI/ANs who use IHS services. The infrastructure, created through the Improving Health Care Delivery Data Project, is a synthesis of existing electronic data from multiple IHS platforms and currently includes data for 7 years (FY2007-2013) for 15 project sites. We propose to continue this collaboration by updating the infrastructure with recent data (FY2014-2017) to evaluate APP effectiveness among AI/AN adults with diabetes using statistical techniques made possible by the longitudinal data. The study has 3 aims: 1. Characterize APP delivery models for adults with diabetes within sites and over time using site characteristics and patient health risk profiles (e.g., glycemic level [A1c], blood pressure [BP], cholesterol [CHL], cardiovascular disease). We anticipate that up to 3 models may emerge (e.g., Targeted, Limited, and General); and 2. For each APP model, evaluate the nature and extent of the relationship between patient APP use and outcomes. Within each model, we expect that APP will improve medication adherence and A1c, BP, and CHL levels, and reduce onset of complications and preventable hospital stays; and 3. Estimate APP delivery costs, treatment cost changes associated with lower use of other health services, and cost-effectiveness. We expect the APP models to be cost-effective. 1