Leveraging electronic health record tools to reduce health disparities for patients with uncontrolled hypertension

高血压影响三分之一的美国成年人，但只有50%的人有适当的血压 控制。高血压在黑人成年人中的患病率是非西班牙裔白人的两倍;因此， 死于高血压相关心脏病的可能性是白色人的2到3倍。而 造成这些差异的因素很多，缺乏强化治疗， 基于种族的口服抗高血压药和次优的随访护理被认为是 种族/民族差异。 健康信息技术（健康IT）解决方案，例如电子健康记录中的决策支持 (EHR)系统，可以帮助减少这些差距的照顾，但很少有研究评估这些使用 卫生系统为照顾大量少数民族患者制定的战略。同样，虽然决策支持 它被广泛使用，很少被用来强调护理中的种族差异，以支持种族- 敏感的治疗建议，或帮助解决更常见的护理障碍， 少数群体。决策支持通常被用作单一方法， 与其他健康IT战略（如患者门户网站）相结合，以改善患者沟通。更一般地说， 大多数工具也没有使用来自快速出现的行为科学领域的原理来设计。 为此，我们提出了一个务实的随机有效性-实施混合2型试验来检验 针对种族的多成分健康信息技术干预对种族和民族差异的影响， 高血压控制。这项试验将在Advocate Health进行，Advocate Health是美国最大的综合服务网络。 伊利诺伊州，它为整个芝加哥地区的多种族患者群体提供服务， 与众多学术合作伙伴开展研究和实用临床试验的经验。 研究的具体目的是：（1）设计和试点测试种族知情的健康信息技术工具，以解决 使用患者咨询委员会和提供者焦点小组控制高血压的种族/民族差异;（2） 进行一项具有部分交叉的双臂实用性群集随机试验，以确定种族- 知情的健康信息技术工具改善血压控制，减少少数群体的差异;和（3） 评估患者、提供者和诊所对卫生信息技术干预的可接受性和可采用性， 包括干预是否减少了患者门户网站采用率的差异。 使用严格的随机设计，我们提出了一个务实和可扩展的方法来优化 卫生信息技术工具，以解决差距。我们的多学科研究团队由种族/民族专家组成， 差异，高血压，健康IT，患者和提供者的行为变化，以及务实的试验。预期 这一创新提案的总体影响是，它将从根本上推动卫生IT的优化 以可扩展的方式改善健康差距，并将其纳入医疗保健系统。

Hypertension affects one in three US adults, yet only 50% have their blood pressure adequately controlled. Hypertension is twice as prevalent in black adults than non-Hispanic whites; consequently, blacks are 2 to 3 times more likely to die of hypertension-related heart disease than their white counterparts. While many factors contribute to these disparities, a lack of treatment intensification, the underuse or prescribing of racially-based oral antihypertensives, and suboptimal follow-up care are thought to be central in the context of racial/ethnic disparities. Health information technology (health IT) solutions, such as decision support in electronic health record (EHR) systems, could help reduce these gaps in care, but few studies have evaluated the use of these strategies in health systems caring for a large population of minority patients. Similarly, while decision support has been widely-used, it has rarely been employed to emphasize racial differences in care, to support racially- sensitive treatment recommendations, or to help address barriers to care that are more common among minority subgroups. Decision support has generally been used as a single approach and has not been integrated with other health IT strategies like patient portals to improve patient communication. More generally, most tools have also not been designed using principles from the rapidly-emerging field of behavioral science. To this end, we propose a pragmatic randomized effectiveness-implementation hybrid type 2 trial to test the impact of a racially-tailored multicomponent health IT intervention on racial and ethnic disparities in hypertension control. This trial will be conducted at Advocate Health, the largest integrated delivery network in Illinois, which serves a multi-ethnic patient population throughout the Chicago area and has extensive experience conducting research studies and pragmatic clinical trials with numerous academic partners. The specific aims of the study are to: (1) design and pilot test racially-informed health IT tools to address racial/ethnic disparities in hypertension control using a patient advisory board and provider focus groups; (2) conduct a 2-arm pragmatic cluster randomized trial with partial crossover to determine whether racially- informed health IT tools improve blood pressure control and reduce disparities in minority populations; and (3) evaluate the acceptability and adoptability of the health IT interventions by patients, providers, and clinics, including whether the intervention reduces disparities in adoption rates of patient portals. Using a rigorous randomized design, we have proposed a pragmatic and scalable approach to optimizing health IT tools to address disparities. Our multidisciplinary study team consists of experts in racial/ethnic disparities, hypertension, health IT, patient and provider behavior change, and pragmatic trials. The expected overall impact of this innovative proposal is that it will fundamentally advance how health IT can be optimized to improve health disparities in ways that are scalable and integrated into healthcare systems.