Predictive Analytics and Clinical Decision Support to Improve PrEP Prescribing in Community Health Centers (PrEDICT)

预测分析和临床决策支持，以改善社区健康中心的 PrEP 处方 (PrEDICT)

基本信息

批准号：
10699074
负责人：
Douglas Scott Krakower
金额：
$ 80.79万
依托单位：
HARVARD PILGRIM HEALTH CARE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-06 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10699074
关键词：
Address Adherence Area Under Curve Black race California Caring Clinical Clinical Informatics Cluster randomized trial Code Community Health Networks Counseling Cultural Sensitivity Data Diagnosis Disparity population Dose Electronic Health Record Epidemic Equity Face Familiarity Formulation General Practices Guidelines HIV HIV Infections HIV diagnosis HIV risk Health Personnel Health system Healthcare Systems Incidence Individual Intervention Interview Laboratories Language Latina Latino Latinx Machine Learning Massachusetts Methods Modeling Monitor Neighborhood Health Center Patient Selection Patients Perception Performance Persons Play Population Poverty Predictive Analytics Provider Qualitative Methods Race Randomized Research Resources Risk Risk Factors Role Sex Behavior Sexuality Sexually Transmitted Diseases Support System Testing Underserved Population Uninsured United States National Institutes of Health Visit acceptability and feasibility artificial intelligence algorithm candidate identification care delivery cis-female clinical decision support digital health disparity reduction electronic health data ethnic disparity ethnic diversity ethnic minority follow-up gender disparity implicit bias improved innovation men who have sex with men patient oriented patient population population health pre-exposure prophylaxis predictive modeling provider intervention racial disparity racial diversity racial minority racial minority population safety net scale up substance use support tools tool uptake

项目摘要

PROJECT SUMMARY Rates of new HIV infections are disproportionately high, and uptake of preexposure prophylaxis (PrEP) low, in Black, Latino/a/x, and uninsured individuals in the US. Healthcare providers in community health centers (CHCs) could play a critical role in increasing PrEP prescribing to racial and ethnic minorities and other underserved populations. However, providers face barriers to PrEP prescribing, such as difficulty identifying candidates for PrEP; discomfort discussing sexual behavior; implicit biases about sexuality, race, and substance use; and lack of familiarity with PrEP care. We have found that providers are enthusiastic about the potential benefits of decision support tools to mitigate these barriers to PrEP provision, and that patients would find such tools acceptable if implemented sensitively. We previously showed that data from electronic health records (EHRs) can be used to identify patients at increased risk of HIV acquisition in two large, general practice healthcare systems. In our formative R34 research, we expanded on this approach in a safety-net setting, incorporating strategies to support not only identification of PrEP candidates but also PrEP discussions and prescribing. In a national network of CHCs serving 6.2 million patients in 46 states (OCHIN), we used machine learning with EHR data to identify patients at increased risk for incident HIV diagnosis (area under the curve 0.84). Using stakeholder-engaged qualitative methods, we then built an EHR-based decision support tool that uses our prediction model to prompt PrEP discussions with patients likely to benefit. The tool features a suite of resources to support initial PrEP prescribing, including suggested language for patient-centered discussions; information about PrEP indications, formulations, and dosing; laboratory order sets; diagnosis codes; and automated clinical notes. We piloted this tool at 3 CHCs, establishing feasibility and acceptability. We now propose Predictive Analytics and Clinical Decision Support to Improve PrEP Prescribing in Community Health Centers (PrEDICT) to evaluate the impact of our tool on PrEP provision in OCHIN CHCs. Our specific aims are to 1) expand and refine the decision support tool to facilitate PrEP follow-up care, and therefore patients’ persistence on PrEP; 2) quantify the impact of the decision support tool on PrEP initiation and persistence in a pragmatic stepped-wedge trial across 16 CHCs; and 3) identify patient populations with whom providers are less inclined to discuss PrEP when prompted to do so, and explore facilitators and barriers to equitable selection of patients for PrEP discussions. We will engage a diverse advisory group of patients from OCHIN CHCs in tool expansion, refinement, and implementation. This project is innovative in its use of predictive analytics and decision support to improve PrEP provision in safety-net settings. The research is significant because it has the potential to facilitate large increases in PrEP utilization using highly scalable tools. Our intervention addresses NIH priorities, aligns with the federal Ending the HIV Epidemic initiative, and could become a best practice for how CHCs and other healthcare systems support PrEP care delivery.

项目概要艾滋病毒新发感染率异常高，而暴露前预防 (PrEP) 的采用率却很低，美国社区卫生中心的黑人、拉丁裔/a/x 人和未参保的个人。（CHC）可以在增加针对少数种族和族裔以及其他群体的 PrEP 处方方面发挥关键作用。然而，服务提供者在 PrEP 处方方面面临障碍，例如难以识别。 PrEP 候选人；对讨论性行为感到不安；我们发现提供者对 PrEP 护理很热情。决策支持工具的潜在好处是可以减轻提供 PrEP 的这些障碍，并且患者会我们发现，如果实施得当，此类工具是可以接受的。我们之前曾展示过来自电子健康的数据。记录（EHR）可用于识别在两个大的、一般的情况下感染艾滋病毒风险增加的患者在我们的形成性 R34 研究中，我们在安全网中扩展了这种方法。设置，纳入策略不仅支持 PrEP 候选者的识别，而且支持 PrEP 讨论在为 46 个州的 620 万名患者提供服务的全国 CHC 网络中（OCHIN），我们使用了利用 EHR 数据进行机器学习，以识别艾滋病毒诊断风险增加的患者（区域然后，我们使用利益相关者参与的定性方法建立了基于 EHR 的决策支持。该工具使用我们的预测模型来促使与可能受益的患者进行 PrEP 讨论。一套支持初始 PrEP 处方的资源，包括以患者为中心的建议语言讨论；有关 PrEP 适应症、配方和剂量的信息；我们在 3 个 CHC 试用了该工具，确定了可行性和可接受性。我们现在提出预测分析和临床决策支持，以改善 PrEP 处方社区健康中心 (PrEDICT) 评估我们的工具对 OCHIN CHC 提供 PrEP 的影响。我们的具体目标是 1) 扩展和完善决策支持工具以促进 PrEP 后续护理，以及因此，患者对 PrEP 的坚持程度；2) 量化决策支持工具对 PrEP 启动的影响；并坚持在 16 个 CHC 中进行务实的阶梯式楔形试验，以及 3) 确定患有以下疾病的患者群体；当被提示时，提供者不太愿意讨论 PrEP，并探索促进因素和障碍我们将聘请多元化的患者咨询小组，以公平地选择患者进行 PrEP 讨论。 OCHIN CHC 在工具扩展、完善和实施方面具有创新性。预测分析和决策支持，以改善安全网环境中的 PrEP 提供。意义重大，因为它有潜力利用高度可扩展性来促进 PrEP 利用率的大幅增加我们的干预措施涉及 NIH 的优先事项，与联邦结束艾滋病毒流行倡议保持一致，以及可能成为 CHC 和其他医疗保健系统如何支持 PrEP 护理服务的最佳实践。