A Multilevel, Multiphase Optimization Strategy for PrEP: Patients and Providers in Primary Care

我们所知：美国有120万人符合PrEP的适应症；但存在差异 在吸收方面。例如，只有9%的黑人和16%的拉丁裔，相比之下，白人的比例为65%， 已经开了PrEP处方药。在亨利福特健康系统中，只有10%的符合条件的患者 处方药PrEP。初级保健是将PrEP作为艾滋病毒预防方法提供的理想环境，因为 提供者看到的是大量艾滋病毒阴性的患者，其中一些人感染艾滋病毒的风险增加， 而初级保健设置通常是进入保健系统的入口点。多阶段优化 战略(MOST)框架是确定有效干预措施的一种新颖、创新的方法。我们将做的工作：在 在这个优化试验中，我们将测试干预组件的有效性，无论是单独的还是组合的 HFH初级保健中新的PrEP处方。首先，我们将针对特定于上下文的(系统)生成反馈 和个人水平)通过具有提供者的焦点小组(n=15)提供干预组件的因素 符合PrEP条件的患者30例。然后，我们将在优化试验中测试这四个干预组件， 16项条件在32家诊所实施。最后，我们将针对以下因素生成反馈 通过与提供者(n=30)和患者(n=30)的半结构化访谈来影响实施。参与者 将是亨利·福特健康系统中有资格获得PrEP的初级保健提供者(PCP)和患者。诊所将 随机化(是/否)以接受提供者和患者干预组件的任意组合。提供商 干预组件包括基于计算机的模拟培训和/或通过 电子健康记录(EHR)。患者干预部分包括艾滋病毒风险评估和/或PrEP 信息性视频-两者都通过EHR提供。主要结果是新的PrEP处方率 临床层面。次要结果将包括PrEP维护、初级保健医生安排的艾滋病毒检测次数，以及 已培训的PCP数量。子分析将测试哪些因素为中等(例如，患者性别、种族、年龄、性别、 性取向)或中介(例如，感知的艾滋病毒风险、提供者和患者的PrEP知识)PrEP摄取， 重点关注优先人群和PrEP处方率的差异。含义：1)理解 哪些干预成分导致PrEP处方增加将代表着 艾滋病毒预防工作。2)优化提供者和患者的多层次干预，提高PrEP 处方将带来一种新的、有效的、以证据为基础的选择。3)确定与哪些因素相关 PREP的使用将有助于减少最有需要的人在PrEP启动方面的差异。4)了解 与干预组件实施相关的特定于环境的因素将有助于确定最佳方法 在其他医疗保健系统中复制/适应。总而言之，我们的团队带来了一种新颖、创新的方法， 强大的跨学科经验，在艾滋病毒、PrEP、MOST和初级保健方面的强大前期工作，以及 科学的严谨性将在该领域产生重大影响。

What we know: There are 1.2 million people in the US who meet the indications for PrEP; yet, disparities exist in uptake. For example, only 9% of Black and 16% of Latino individuals, compared to 65% of White individuals, have been prescribed PrEP. At Henry Ford Health (HFH) system, only 10% of eligible patients have been prescribed PrEP. Primary care is an ideal setting for PrEP to be offered as an HIV prevention method since providers see large numbers of patients who are HIV negative, with some who are at increased risk for HIV, and the primary care setting is often the point of entry to the healthcare system. The multiphase optimization strategy (MOST) framework is a novel, innovative way to identify an efficient intervention. What we will do: In this optimization trial, we will test the effectiveness of intervention components, alone and in combination, on new PrEP prescriptions in primary care at HFH. First, we will generate feedback on context-specific (system and individual level) factors for intervention component delivery via focus groups with providers (n=15) and patients eligible for PrEP (n=30). Then, we will test the four intervention components in an optimization trial, with 16 conditions being implemented at 32 clinics. Finally, we will generate feedback on the factors that affected implementation via semi-structured interviews with providers (n=30) and patients (n=30). Participants will be primary care providers (PCPs) and patients eligible for PrEP in Henry Ford Health System. Clinics will be randomized (yes/no) to receive any combination of provider and patient intervention components. Provider intervention components include computer-based simulation training and/or best practice alerts delivered via the electronic health record (EHR). Patient intervention components include HIV risk assessment and/or PrEP informational video – both delivered via the EHR. Primary outcome is the rate of new PrEP prescriptions at the clinic level. Secondary outcomes will include PrEP maintenance, number of HIV tests ordered by a PCP, and number of PCPs trained. Sub analyses will test which factors moderate (e.g., patient sex, race, age, gender, sexual orientation) or mediate (e.g., perceived HIV risk, provider and patient PrEP knowledge) PrEP uptake, focusing on priority populations and disparities in rates of PrEP prescription. Implications: 1) Understanding which intervention components lead to increased PrEP prescriptions will represent an important advance in HIV prevention efforts. 2) Optimizing a multi-level intervention for providers and patients to increase PrEP prescriptions would lead to a new, efficient, evidence-based option. 3) Determining what factors are related to PrEP uptake will help reduce disparities in PrEP initiation among those most in need. 4) Understanding the context specific factors related to intervention component implementation will help identify best methods for replication/adaptation in other healthcare systems. In sum, our team brings a novel, innovative approach, robust interdisciplinary experience, strong preliminary work in HIV, PrEP, MOST, and primary care, and scientific rigor to make a significant impact on the field.