EHR-Based Prediction Models to Improve PrEP Use in Community Health Centers

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， 美国的黑人、拉丁美洲人和保险不足的个人安全网社区卫生保健提供者 中心（CHC）为保险不足率高的种族多样化人群提供护理。然而，在这方面， 供应商列举了PrEP处方的障碍，包括缺乏识别PrEP候选人的工具。 实用的工具，以帮助提供者识别有感染艾滋病毒风险的患者，并在适当的时候开出PrEP， PrEP的人口水平效益不太可能实现。使用数据的电子临床决策支持 嵌入在患者电子健康记录（EHR）中的电子病历具有满足这一需求的潜力。EHR含有丰富的 有助于识别HIV感染高危患者的数据，包括人口统计学、诊断、检测 模式、处方和健康的社会决定因素。在我们之前的工作中，我们开发并验证了 预测模型使用来自马萨诸塞州和加州两个大型医疗保健系统的EHR数据， 110万和430万的患者人群，以确定艾滋病毒事件的高风险患者。这些机器 学习模型具有很强的预测性能，C统计量高达0.91。本提案的目的 是检验一个假设，即一个包含艾滋病毒风险预测模型的临床决策支持工具， 帮助提供者识别HIV感染高风险患者，并改善PrEP处方。我们的研究背景是 拥有280万患者的全国社区卫生中心网络（OCHIN）。我们将首先调整我们的艾滋病毒预测模型， 这个诊所网络，然后与提供者进行形成性工作，以告知我们警报的开发， 额外的PrEP决策支持工具将是有效的和受欢迎的。该研究小组包括专家， 艾滋病毒、PrEP实施、预测分析和社区卫生中心的医疗保健服务。我们的具体目标是：（1） 优化使用EHR数据的预测模型，以确定潜在的PrEP候选人在种族， 社会经济和地理上多样化的患者人群; 2）探索提供者对 PrEP处方的障碍，以及他们对PrEP决策支持的偏好，以告知制定 为社区健康中心提供以健康档案为本的决策支援工具;及3）进行试验计划，以评估 以及基于EHR的临床决策支持干预对CHCs中PrEP相关护理的初步影响。 我们将评估对整个PrEP护理连续体指标的影响，包括处方、持续性、临床 艾滋病毒和其他性传播感染的监测、检测和诊断。这项建议是 创新地使用预测分析和临床决策支持来优化PrEP。该项目 重要的是，我们的干预措施将在全国范围内的社区卫生中心和其他医疗保健系统中推广 与EHR，因为它解决了联邦倡议，以结束艾滋病毒流行的规模扩大PrEP在高- 发生率设置。预期的结果是一个集群随机试验的基础，以测试是否EHR- 基于PrEP的决策支持可以预防全国CHC网络中的新艾滋病毒感染。

PROJECT SUMMARY Rates of new HIV infections are disproportionately high, and uptake of preexposure prophylaxis (PrEP) low, in Black, Latino, and underinsured individuals in the U.S. Healthcare providers at safety net community health centers (CHCs) provide care to racially diverse populations with high rates of underinsurance. However, providers cite barriers to PrEP prescribing, including lack of tools to identify candidates for PrEP. Without practical tools to help providers identify patients at risk for HIV infection and prescribe PrEP when appropriate, the population-level benefits of PrEP are unlikely to be realized. Electronic clinical decision support using data embedded in patients’ electronic health records (EHRs) has the potential to fulfill this need. EHRs contain rich data that can help identify patients at high risk of HIV acquisition, including demographics, diagnoses, testing patterns, prescriptions, and social determinants of health. In our prior work, we developed and validated prediction models using EHR data from two large healthcare systems in Massachusetts and California, with patient populations of 1.1 and 4.3 million, to identify patients at high risk for incident HIV. These machine learning models had strong predictive performance, with C-statistics up to 0.91. The objective of this proposal is to test the hypothesis that a clinical decision support tool that incorporates an HIV risk prediction model can help providers identify patients at high risk for HIV infection and improve PrEP prescribing. Our study setting is a national network of CHCs with 2.8 million patients (OCHIN). We will first tailor our HIV prediction models to this clinic network, and then conduct formative work with providers to inform our development of alerts and additional PrEP decision support tools that will be effective and welcomed. The study team includes experts in HIV, PrEP implementation, predictive analytics, and healthcare delivery in CHCs. Our specific aims are to 1) optimize prediction models that use EHR data to identify potential PrEP candidates in racially, socioeconomically, and geographically diverse patient populations; 2) explore providers’ perspectives on barriers to PrEP prescribing, and their preferences for PrEP decision support, to inform development of an EHR-based decision support tool for CHCs; and 3) conduct a pilot trial to assess the feasibility, acceptability, and preliminary impact of an EHR-based clinical decision support intervention on PrEP-related care in CHCs. We will assess impact on metrics across the PrEP care continuum, including prescriptions, persistence, clinical monitoring, and tests and diagnoses of HIV and other sexually transmitted infections. This proposal is innovative in its use of predictive analytics and clinical decision support to optimize PrEP. The project is significant because our intervention will be scalable across CHCs nationally and to other healthcare systems with EHRs, and because it addresses the federal initiative to end the HIV epidemic by scaling up PrEP in high- incidence settings. The expected outcome is the foundation for a cluster randomized trial to test whether EHR- based decision support for PrEP can prevent new HIV infections in a national network of CHCs.