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

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， 美国黑人、拉丁裔和保险不足的个人-安全网社区健康的医疗保健提供者 中心(CHC)为种族多样化、保险覆盖率较高的人群提供护理。然而， 提供者列举了开PrEP处方的障碍，包括缺乏确定PrEP候选人的工具。如果没有 实用工具，帮助提供者识别有艾滋病毒感染风险的患者，并在适当时开出PrEP， PrEP在人口层面的好处不太可能实现。基于数据的电子临床决策支持 嵌入患者电子健康记录(EHR)中的电子病历具有满足这一需求的潜力。EHR含有丰富的 可帮助识别感染艾滋病毒高危患者的数据，包括人口统计数据、诊断、检测 健康的模式、处方和社会决定因素。在我们之前的工作中，我们开发并验证了 使用来自马萨诸塞州和加利福尼亚州两个大型医疗保健系统的EHR数据的预测模型， 患者人数分别为110万和430万，以确定感染艾滋病毒的高危患者。这些机器 学习模型具有较强的预测性能，C-统计量高达0.91。这项提议的目的是 是为了测试这样一种假设，即结合艾滋病毒风险预测模型的临床决策支持工具可以 帮助提供者识别艾滋病毒感染的高危患者，并改进PrEP处方。我们的研究背景是 拥有280万患者的国家社区卫生中心网络(人道协调厅)。我们将首先调整我们的艾滋病毒预测模型，以 这个诊所网络，然后与提供商一起进行形成性工作，以告知我们警报和 额外的PrEP决策支持工具将是有效和受欢迎的。研究小组包括以下专家 HIV、PrEP实施、预测性分析和CHC中的医疗保健提供。我们的具体目标是1) 优化预测模型，使用EHR数据在种族上识别潜在的PrEP候选对象， 社会经济和地理上不同的患者群体；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，以帮助制定 和3)进行试点试验，以评估可行性、可接受性、 以及基于EHR的临床决策支持干预对CHC中PrEP相关护理的初步影响。 我们将评估对整个PrEP护理连续体系的指标的影响，包括处方、持久性、临床 监测、检测和诊断艾滋病毒和其他性传播感染。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。该项目是 意义重大，因为我们的干预将可在全国范围内的CHCs和其他医疗系统中扩展 与EHR，因为它解决了联邦倡议，通过扩大PrEP在高- 关联设置。预期结果是一项整群随机试验的基础，以测试EHR- 为预防预防接种计划提供基于决策的支持，可以预防全国预防控制和预防中心网络中新的艾滋病毒感染。

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.