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.

项目概要 路易斯安那州体现了 HIV 暴露前预防 (PrEP) 的需求和接受率之间的差异 南州 2018 年 HIV 发病率在美国各州中排名第 4，而 PrEP 排名第 46 位 迄今为止，在路易斯安那州和美国，几乎没有出现解决 PrEP 最佳利用障碍的解决方案。 总体而言，我们的团队之前已经展示了电子健康记录实用性的概念验证。 基于 (EHR) 的机器学习 (ML) 算法，用于识别 HIV 病例（PrEP 的替代品） 候选人）在医疗保健系统内，表现优于当前的疾病控制和预防中心 (CDC) PrEP 适应症指南从未在南方实施过。 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 拟议的项目旨在评估两本小说。 将基于电子病历的模型实施扩展到其原始卫生系统之外的方法 他们服务的社区：1）涉及研究团队和当地社区的异步策略 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) 使用最佳实践建议来提醒 ED 和 UC 提供者的实时策略 模型将其标记为在急性护理期间感染艾滋病毒的风险增加。 路易斯安那州东南部的两个医疗保健系统：新奥尔良的 LCMC Health 和 Our Lady of the 巴吞鲁日的 Lake Health 为了解艾滋病毒高危人群，该研究将重点关注以下地区的人群。 专门通过急诊科 (ED) 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) 推导并验证基于 EHR 的 HIV 风险预测。 模型利用来自路易斯安那州东南部两个卫生系统的 ED 和 UC 病例的临床数据 2)。 制定利益相关者知情的实施策略，以扩大基于电子病历的预测的范围 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 的方法。 目标 2 将获得关键利益相关者的意见，以指导风险预测模型融入当地艾滋病毒流行病学背景。 制定文化响应策略，以通知由 该模型的目标 3 将进行试点实施试验，以评估两种实施策略： 为了实现这些目标，我们组建了一支由艾滋病毒卫生服务专家组成的多学科团队 该团队将合作研究、艾滋病毒预防流行病学、健康信息学和实施科学。 与主要社区组织（新圣约翰五传教浸信会教堂的 ACE 营） 奥尔良和巴吞鲁日 Metro Health），利用卫生系统 EHR 的力量和覆盖范围 为社区成员提供他们所需的数据，以便他们就使用 PrEP 做出明智的决定。