Predictive Analytics and Clinical Decision Support to Improve PrEP Prescribing in Community Health Centers (PrEDICT)

项目摘要 新的艾滋病毒感染率高得不成比例，暴露前预防（PrEP）的吸收率低， 黑人，拉丁美洲人/a/x，以及在美国没有保险的人。社区卫生服务中心的医疗服务提供者 （CHCs）可以在增加种族和少数民族以及其他种族的PrEP处方方面发挥关键作用 服务不足的人群。然而，提供者面临PrEP处方的障碍，例如难以识别 PrEP的候选人;讨论性行为的不适;对性行为的隐性偏见，种族， 物质使用;以及缺乏对PrEP护理的熟悉。我们发现，供应商对 决策支持工具的潜在好处，以减轻这些障碍，以准备提供，病人将 认为如果敏感地执行这些工具，是可以接受的。我们之前的研究表明，来自电子健康的数据 在两个大的，一般的， 实践医疗保健系统。在我们形成的R34研究中，我们在安全网中扩展了这种方法 设置，纳入战略，不仅支持识别PrEP候选人，还支持PrEP讨论 开处方在为46个州的620万患者提供服务的全国CHC网络（OCHIN）中，我们使用了 使用EHR数据进行机器学习，以识别艾滋病毒诊断风险增加的患者（ 曲线0.84）。使用企业主参与的定性方法，我们然后建立了一个基于EHR的决策支持 该工具使用我们的预测模型来提示与可能受益的患者进行PrEP讨论。该工具的特点 一套支持初始PrEP处方的资源，包括以患者为中心的建议语言 讨论;关于PrEP适应症，制剂和剂量的信息;实验室订单集;诊断 代码;和自动化临床笔记。我们在3个社区卫生中心试用了这一工具，确定了可行性和可接受性。 我们现在提出预测分析和临床决策支持，以改善PrEP处方， 社区卫生中心（PrEDICT）评估我们的工具对OCHIN CHCs提供PrEP的影响。 我们的具体目标是：1）扩大和完善决策支持工具，以促进PrEP后续护理， 因此，患者对PrEP的持续性; 2）量化决策支持工具对PrEP启动的影响 在16个CHC中进行的实用性阶梯楔形试验中的持久性;以及3）确定具有以下特征的患者人群： 当被提示这样做时，提供者不太倾向于讨论PrEP，并探索促进者和障碍 公平选择患者进行PrEP讨论。我们会让一个多元化的病人咨询小组 OCHIN CHC在工具扩展、改进和实施方面的优势。这个项目是创新的使用， 预测分析和决策支持，以改善安全网环境中的PrEP供应。这项研究是 重要的是，它有可能促进使用高度可扩展的PrEP利用率的大幅增加 工具.我们的干预措施解决了NIH的优先事项，与联邦终止艾滋病毒流行倡议保持一致， 可能成为CHCs和其他医疗保健系统如何支持PrEP护理提供的最佳实践。

PROJECT SUMMARY Rates of new HIV infections are disproportionately high, and uptake of preexposure prophylaxis (PrEP) low, in Black, Latino/a/x, and uninsured individuals in the US. Healthcare providers in community health centers (CHCs) could play a critical role in increasing PrEP prescribing to racial and ethnic minorities and other underserved populations. However, providers face barriers to PrEP prescribing, such as difficulty identifying candidates for PrEP; discomfort discussing sexual behavior; implicit biases about sexuality, race, and substance use; and lack of familiarity with PrEP care. We have found that providers are enthusiastic about the potential benefits of decision support tools to mitigate these barriers to PrEP provision, and that patients would find such tools acceptable if implemented sensitively. We previously showed that data from electronic health records (EHRs) can be used to identify patients at increased risk of HIV acquisition in two large, general practice healthcare systems. In our formative R34 research, we expanded on this approach in a safety-net setting, incorporating strategies to support not only identification of PrEP candidates but also PrEP discussions and prescribing. In a national network of CHCs serving 6.2 million patients in 46 states (OCHIN), we used machine learning with EHR data to identify patients at increased risk for incident HIV diagnosis (area under the curve 0.84). Using stakeholder-engaged qualitative methods, we then built an EHR-based decision support tool that uses our prediction model to prompt PrEP discussions with patients likely to benefit. The tool features a suite of resources to support initial PrEP prescribing, including suggested language for patient-centered discussions; information about PrEP indications, formulations, and dosing; laboratory order sets; diagnosis codes; and automated clinical notes. We piloted this tool at 3 CHCs, establishing feasibility and acceptability. We now propose Predictive Analytics and Clinical Decision Support to Improve PrEP Prescribing in Community Health Centers (PrEDICT) to evaluate the impact of our tool on PrEP provision in OCHIN CHCs. Our specific aims are to 1) expand and refine the decision support tool to facilitate PrEP follow-up care, and therefore patients’ persistence on PrEP; 2) quantify the impact of the decision support tool on PrEP initiation and persistence in a pragmatic stepped-wedge trial across 16 CHCs; and 3) identify patient populations with whom providers are less inclined to discuss PrEP when prompted to do so, and explore facilitators and barriers to equitable selection of patients for PrEP discussions. We will engage a diverse advisory group of patients from OCHIN CHCs in tool expansion, refinement, and implementation. This project is innovative in its use of predictive analytics and decision support to improve PrEP provision in safety-net settings. The research is significant because it has the potential to facilitate large increases in PrEP utilization using highly scalable tools. Our intervention addresses NIH priorities, aligns with the federal Ending the HIV Epidemic initiative, and could become a best practice for how CHCs and other healthcare systems support PrEP care delivery.