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

项目总结 新的艾滋病毒感染率高得不成比例，暴露前预防(PrEP)的接受率很低， 美国的黑人、拉丁裔/a/x和未参保的个人。社区卫生中心的医疗服务提供者 (CHC)可以在增加针对种族和族裔少数群体以及其他人的PrEP处方方面发挥关键作用 服务不足的人群。然而，提供者在开PrEP处方时面临障碍，例如难以识别 PrEP的候选人；讨论性行为的不适；对性、种族和性别的含蓄偏见 物质使用；不熟悉PrEP护理。我们发现，供应商对 决策支持工具的潜在好处，以减轻提供PrEP的这些障碍，患者将 如果实施得当，你会发现这样的工具是可以接受的。我们之前展示了来自电子健康的数据 记录(EHR)可用于在两个大的、一般的情况下识别感染艾滋病毒风险增加的患者 实行医疗保健制度。在我们形成性的R34研究中，我们在安全网中扩展了这种方法 设定，纳入战略，不仅支持确定PrEP候选人，而且还支持PrEP讨论 还有开处方。在为46个州的620万名患者提供服务的全国CHC网络(OCHIN)中，我们使用了 使用电子病历数据进行机器学习，以识别感染艾滋病毒风险较高的患者(位于 曲线0.84)。利用利益相关者参与的定性方法，我们构建了基于电子病历的决策支持 使用我们的预测模型与可能受益的患者进行PrEP讨论的工具。工具功能 支持初始PrEP处方的一套资源，包括以患者为中心的建议语言 讨论；关于PrEP适应症、配方和剂量的信息；实验室顺序设置；诊断 代码；以及自动化的临床记录。我们在3个社区卫生控制中心试用了这个工具，确定了可行性和可接受性。 我们现在建议使用预测性分析和临床决策支持来改进PrEP的预测 社区卫生中心(预测)评估我们的工具对OCHIN CHC提供PrEP的影响。 我们的具体目标是1)扩展和完善决策支持工具，以促进PrEP后续护理，以及 因此，患者对PrEP的坚持；2)量化决策支持工具对PrEP启动的影响 在16家CHC中进行务实的阶梯式楔形试验；以及3)通过 哪些提供商在被提示时不太愿意讨论PrEP，并探讨促进者和障碍 公平地选择患者进行PrEP讨论。我们将聘请一个多样化的患者咨询小组 来自OCHIN CHC的工具扩展、改进和实施。这个项目在使用上是创新的 预测性分析和决策支持，以改进安全网环境中的PrEP拨备。这项研究是 意义重大，因为它有可能使用高度可扩展的 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，以及 可能成为CHC和其他医疗系统支持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.