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增强路易斯安那州东南部的 PrEP 获取：社区知情方法

• 批准号: 10459860
• 负责人: Meredith Edwards Clement
• 金额: $ 87万
• 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/10459860
• 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; Effectiveness; Electronic Health Record; Ensure; Epidemiological trend; Epidemiology; Evaluation; Event; Focus Groups; Foundations; Future; Geographic state; 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; Periodicity; Persons; Play; Population; Precede-Proceed Model; Primary Health Care; Public Health Informatics; Randomized; Readiness; Reporting; Risk; Role; Schedule; Testing; Time; Work; acceptability and feasibility; acute care; base; care providers; empowered; health care delivery; high risk population; implementation questions; implementation science; implementation strategy; implementation trial; innovation; insight; machine learning algorithm; machine learning model; member; multidisciplinary; novel strategies; pilot trial; point of care; pre-exposure prophylaxis; predictive modeling; prevent; risk prediction; risk prediction model; scale up; surveillance study; tool; treatment as usual; trial comparing; uptake; urgent care

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.

项目总结 路易斯安那州举例说明了暴露前艾滋病毒预防(PrEP)需求和在 南方，2018年艾滋病毒发病率在美国各州中排名第四，而PrEP吸收率排名第46 年。到目前为止，在路易斯安那州和 总体来说是南边。我们的团队此前已经对电子健康记录的效用进行了概念验证 基于(EHR)的机器学习(ML)算法，用于识别突发HIV病例(PrEP的替代 候选人)在医疗系统内，表现优于目前的疾病控制和预防中心 (CDC)PrEP适应症指南。这一有希望的方法从未在南方实施过 医疗保健系统，以及纳入基于卫生系统的电子病历风险预测模型的最佳方法 社区预防艾滋病毒的努力尚不清楚。拟议的项目旨在评估两部小说 将基于电子病历的模式实施扩展到其原始卫生系统之外并扩展到 他们服务的社区：1)涉及学习团队和基于当地社区的异步策略 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的人员 风险模型2)使用最佳实践建议提醒ED和UC提供商人员的实时策略 在急性护理期间被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系统：新奥尔良的LCMC Health和圣母医院 巴吞鲁日的健康湖。为了涵盖艾滋病毒高危人群，这项研究将集中在 通过急诊科(ED)和紧急护理专门参与卫生系统的卫生系统 (UC)相遇。该项目的具体目标是：1)推导和验证基于电子健康记录的艾滋病毒风险预测 模型利用来自ED和UC在路易斯安那州东南部两个卫生系统遇到的临床数据。2) 制定利益相关者知情的实施战略，以扩大基于EHR的预测的覆盖范围 医疗系统之外的模式。3)评估两个面向社区的方案的可行性和可接受性 EHR艾滋病毒风险预测模型部署的实施方法。目标1将使我们基于电子病历的 将风险预测模型纳入当地艾滋病毒流行病学背景。目标2将获取关键利益相关者的意见以指导 制定文化响应战略，通知由以下人员确定的高危个人的风险状况 模特。目标3将进行一项试点实施试验，以评估两项实施战略： 为了实现这些目标，我们组建了一支由艾滋病毒健康服务专家组成的多学科团队 研究、艾滋病毒预防流行病学、卫生信息学和实施科学。这个团队将成为合作伙伴 与主要的以社区为基础的组织(纽约圣约翰5传教浸信会ACE营 奥尔良和巴吞鲁日的Metro Health)，以利用卫生系统EHR的力量和覆盖范围 向社区成员提供他们需要的数据，以做出有关使用PrEP的明智决定。