Leveraging Local Health System Electronic Health Record Data to Enhance PrEP Access in Southeastern Louisiana: A Community-Informed Approach

利用当地卫生系统电子健康记录数据来增强路易斯安那州东南部的 PrEP 获取：一种社区知情的方法

• 批准号: 10651808
• 负责人: Meredith Edwards Clement
• 金额: $ 98.83万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-22 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10651808
• 关键词: AIDS prevention; Accident and Emergency department; Address; Algorithms; Area; Baptist Church; Big Data; Caring; Centers for Disease Control and Prevention (U.S.); Client; Clinical; Clinical Data; Communities; Comprehensive Health Care; Country; Data; Development; Diagnosis; Disparity; Effectiveness; Electronic Health Record; Ensure; Epidemiological trend; Epidemiology; Evaluation; Event; Focus Groups; Foundations; Future; Guidelines; HIV; HIV Infections; HIV risk; Health; Health Services Research; Health care facility; Health system; Healthcare; Healthcare Systems; Human Resources; Incidence; Individual; Interview; Link; Louisiana; Machine Learning; Methodology; Missionary; Modeling; Notification; Output; Periodicals; Persons; Population; Precede-Proceed Model; Primary Care; Public Health Informatics; Randomized; Readiness; Reporting; Risk; Schedule; Testing; Time; US State; Work; acceptability and feasibility; acute care; candidate identification; community organizations; empowerment; health care delivery; high risk population; implementation questions; implementation science; implementation strategy; implementation trial; indicated prevention; innovation; insight; longitudinal care; machine learning algorithm; machine learning model; member; multidisciplinary; novel strategies; pilot trial; point of care; pre-exposure prophylaxis; predictive modeling; predictive tools; prevent; risk prediction; risk prediction model; scale up; surveillance study; tool; treatment as usual; trial comparing; uptake; urgent care; urgent care provider

PROJECT SUMMARY Louisiana exemplifies the disparity between HIV pre-exposure prophylaxis (PrEP) need and uptake in the South, ranking 4th among US states in HIV incidence in 2018 while ranking 46th in PrEP uptake the following year. To date, few solutions have emerged to address barriers to optimal PrEP utilization in Louisiana and the South overall. Our team has previously demonstrated proof-of-concept of the utility of electronic health record (EHR)-based machine learning (ML) algorithms for identifying incident HIV cases (surrogate for PrEP candidates) within healthcare systems, outperforming current Centers for Disease Control and Prevention (CDC) PrEP indication guidelines. This promising methodology has never been implemented in a Southern healthcare system, and the best approach for incorporating health system-based EHR risk prediction models into community HIV prevention efforts is unclear. The proposed project seeks to evaluate two novel approaches to expanding EHR-based model implementation beyond their originating health systems and into the communities they serve: 1) an asynchronous strategy involving study team and local community-based personnel notifying community members at risk of HIV infection using a monthly report generated by the EHR risk model 2) a real-time strategy using best practice advisories to alert ED and UC providers of persons flagged as increased risk for HIV by the model during acute care encounters. We will test these strategies within two healthcare systems in Southeastern Louisiana: LCMC Health in New Orleans and Our Lady of the Lake Health in Baton Rouge. To capture a high HIV risk population, the study will focus on persons in the health system who exclusively engage the health system through emergency department (ED) and urgent care (UC) encounters. The project’s specific aims are to: 1) Derive and validate an EHR-based HIV risk prediction model utilizing clinical data from ED and UC encounters in two Southeastern Louisiana health systems. 2) Develop stakeholder-informed implementation strategies for extending the reach of the EHR-based prediction model beyond the health system. 3) Evaluate feasibility and acceptability of two community-facing implementation approaches to EHR HIV risk prediction model deployment. Aim 1 will adapt our EHR-based risk prediction model into the local HIV epidemiologic context. Aim 2 will obtain key stakeholder input to guide the development of culturally-responsive strategies for risk status notification of at-risk individuals identified by the model. Aim 3 will feature a pilot implementation trial to assess the two implementation strategies: To execute these objectives, we have assembled a multidisciplinary team of experts in HIV health services research, HIV prevention epidemiology, health informatics and implementation science. This team will partner with key community-based organizations (Camp ACE of the St. John 5 Missionary Baptist Church in New Orleans and Metro Health of Baton Rouge), to leverage the power and reach of health system EHR towards empowering community members with the data they need to make informed decisions about using PrEP.

项目摘要 路易斯安那州证实了艾滋病毒暴露前预防（PrEP）的需求和吸收之间的差距， 南部，2018年艾滋病毒发病率在美国各州中排名第4，而PrEP吸收率排名第46， 年到目前为止，几乎没有解决方案可以解决路易斯安那州和路易斯安那州最佳PrEP利用的障碍。 南方整体我们的团队之前已经证明了电子健康记录实用性的概念验证 （EHR）为基础的机器学习（ML）算法，用于识别艾滋病毒事件（替代PrEP） 候选人）在医疗保健系统内，表现优于目前的疾病控制和预防中心 (CDC)PrEP适应症指南。这种有前途的方法从未在南方国家实施过。 医疗保健系统，以及整合基于卫生系统的EHR风险预测模型的最佳方法 社区艾滋病预防工作的进展尚不清楚。该项目旨在评估两个新的 将基于EHR的模型实施扩展到其原有卫生系统之外， 他们所服务的社区：1）一个异步的战略，涉及研究小组和当地社区为基础的 工作人员使用EHR生成的月度报告通知有艾滋病毒感染风险的社区成员 风险模型2）使用最佳实践警报的实时策略，以提醒艾德和UC提供者 在急性护理接触期间，该模型标记为艾滋病毒风险增加。我们将测试这些策略 路易斯安那州东南部的两个医疗保健系统：新奥尔良的LCMC健康和 巴吞鲁日的湖泊健康。为找出爱滋病病毒感染的高危人群，研究会集中于 专门通过急诊科（艾德）和紧急护理参与卫生系统的卫生系统 (UC)遭遇该项目的具体目标是：1）推导和验证基于电子健康档案的艾滋病毒风险预测 该模型利用路易斯安那州东南部两个卫生系统中艾德和UC的临床数据。（二） 制定知情的实施战略，以扩大基于EHR的预测范围 超越卫生系统。3)评估两个面向社区的可行性和可接受性 EHR HIV风险预测模型部署的实施方法。目标1将调整我们基于EHR的 风险预测模型纳入当地艾滋病毒流行病学背景。目标2将获得关键利益相关者的意见， 制定文化应对战略，通知通过以下方式确定的风险个人的风险状况： 该模型目标3将以试点实施试验为特色，以评估两项实施战略： 为了实现这些目标，我们组建了一个艾滋病毒卫生服务方面的多学科专家小组， 研究、艾滋病毒预防流行病学、卫生信息学和实施科学。这个团队将与 与主要的社区组织（圣约翰5传教士浸信会在纽约的ACE营）合作， 奥尔良和巴吞鲁日的Metro Health），以利用卫生系统EHR的力量和范围， 为社区成员提供所需的数据，以便他们就使用PrEP做出明智的决定。