Assessing performance of a Hepatitis C Emergency Department (HepC-END) Screening Tool

评估丙型肝炎急诊科 (HepC-END) 筛查工具的性能

基本信息

批准号：
10754614
负责人：
Haesuk Park
金额：
$ 70.24万
依托单位：
UNIVERSITY OF FLORIDA
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：
2023-09-15 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10754614
关键词：
Accident and Emergency department Adult Algorithms Antiviral Agents Antiviral Therapy Artificial Intelligence Caring Chronic Hepatitis C Clinical Clinical Data Clinical Research Continuity of Patient Care Data Development Diagnosis Diagnostic Disease Progression Educational workshop Effectiveness Electronic Health Record Emergency department screening Exploration, Preparation, Implementation, and Sustainment Feedback Florida Funding Goals HCV screening HIV Health Healthcare Systems Hepatitis Hepatitis C Hepatitis C Therapy Hepatitis C Transmission Hepatitis C virus Homelessness Individual Infection Intervention Interview Link Liver Machine Learning Methods Morbidity - disease rate National Institute of Drug Abuse Natural Language Processing Notification Nurses Patient Self-Report Patients Performance Persons Physicians Play Positioning Attribute Proctor framework Provider Public Health Questionnaires Recommendation Research Risk Risk Assessment Risk Behaviors Risk Factors Role Route Screening procedure Social Behavior Substance Use Disorder Testing Time Translating United States Universities Venous blood sampling Visit Work clinical practice co-infection cost cost effective cost effectiveness design economic outcome electronic health record system high risk high risk behavior implementation framework implementation outcomes improved injection drug use innovation machine learning algorithm machine learning prediction algorithm mortality opioid epidemic prediction algorithm predictive modeling prototype recurrent neural network risk stratification screening screening program structured data tool transmission process usability viral transmission

项目摘要

PROJECT SUMMARY/ABSTRACT Hepatitis C virus (HCV) infection has markedly increased in the United States, primarily resulting from injection drug use (IDU) associated with the ongoing opioid epidemic. Furthermore, >50% of 3.2 million individuals with chronic HCV remain undiagnosed, leading to significant morbidity and mortality despite the availability of effective direct-acting antiviral therapy. Due to shared routes of transmission, HCV infection occurs in 15%-40% of persons infected with human immunodeficiency virus (HIV) and may be used as a marker of HIV exposure. Emergency departments (EDs) play major roles in screening for HCV infection and HIV infection. Several ED- based HCV screening programs have been implemented and have identified previously unrecognized HCV infections, but many challenges remain. Because targeted screening programs use methods that often fail to detect high-risk behaviors (e.g., self-reported information on prescreening questionnaires or review of patient problem lists at time of visit), they do not effectively identify persons at high risk of HCV infection (e.g., IDU). Nontargeted HCV screening strategies require less assessment of risk behaviors. However, concerns such as high costs and unnecessary tests make nontargeted screening strategies difficult to implement and sustain. Therefore, an innovative, effective, and sustainable HCV screening strategy is urgently needed. We propose to develop, implement, and evaluate a tailored, effective, and sustainable, prediction algorithm- based screening tool called Hepatitis C Emergency Department (HepC-EnD) that can be used by health care systems to identify patients at high risk of HCV infection. We will achieve these goals through three specific aims. Aim 1 will develop and validate prediction algorithms using machine learning and natural language processing to identify patients at risk of HCV infection through Florida’s all-payer electronic health records (EHRs) accessed via the OneFlorida+ Clinical Research Consortium. In Aim 2, we will design a HepC-EnD prototype that incorporates the best prediction algorithms to provide automatic notification to ED providers of patients at high risk of HCV infection. Informed by implementation science frameworks, we will enhance the functionality and usability of HepC-EnD through a workshop and qualitative interviews. In Aim 3, we will integrate HepC-EnD into the University of Florida Health EHR system to deploy and test HepC-EnD in two EDs (Gainesville and Jacksonville) and compare the performance of HepC-EnD with nontargeted screening using a difference-in- differences approach. Performance will be assessed by evaluating the usability, acceptability, effectiveness, and cost-effectiveness of the tool. Our proposed research is highly significant in its integration of a cutting-edge machine-learning–based prediction and risk stratification tool into an e-platform that will better inform clinical practice for improving HCV/HIV screening and linking patients with care. Our findings will provide timely data and adaptable strategies that are key in attaining the national and global goals of eliminating HCV infection.

项目摘要/摘要在美国，丙型肝炎病毒（HCV）的感染显着增加，主要是由注射引起的与正在进行的阿片类药物流行有关的药物使用（IDU）。此外，有320万个人中有50％慢性HCV保持未诊断，导致明显的发病率和死亡率目的地可用有效的直接作用抗病毒疗法。由于共享的传输途径，HCV感染发生在15％-40％感染了人类免疫缺陷病毒（HIV）的人，可以用作HIV暴露的标志。急诊科（EDS）在筛查HCV感染和艾滋病毒感染中起着重要作用。几个Ed- 基于基于的HCV筛选程序已经实施并确定了先前未认可的HCV 感染，但仍有许多挑战。因为有针对性的筛选程序使用通常无法的方法检测高风险行为（例如，关于预筛查问卷的自我报告信息或患者的审查访问时的问题清单），他们没有有效地识别出患有HCV感染的高风险的人（例如，IDU）。非目标的HCV筛选策略需要更少的风险行为评估。但是，诸如高成本和不必要的测试使得难以实施和维持的非目标筛选策略。因此，迫切需要一种创新，有效和可持续的HCV筛选策略。我们建议开发，实施和评估量身定制，有效且可持续的预测算法 - 基于肝炎急诊科（HEPC-END）的基于筛查工具可供卫生保健使用鉴定患有HCV感染高风险的患者的系统。我们将通过三个具体目标来实现这些目标。 AIM 1将使用机器学习和自然语言处理来开发和验证预测算法通过访问佛罗里达州的全付电子健康记录（EHRS）识别有HCV感染风险的患者通过Oneflorida+临床研究联盟。在AIM 2中，我们将设计一个HEPC-End原型结合最佳预测算法，以向高的患者提供自动通知 HCV感染的风险。通过实施科学框架，我们将增强功能和 HEPC-End的可用性通过研讨会和定性采访。在AIM 3中，我们将将HEPC-End整合到佛罗里达大学卫生EHR系统将在两种EDS中部署和测试HEPC-END（Gainesville and Gainesville and 杰克逊维尔（Jacksonville），并使用差异 - 差异方法。绩效将通过评估可用性，可接受性，有效性和工具的成本效益。我们提出的研究在整合尖端方面非常重要基于机器学习的预测和风险分层工具，将电子平台更好地告知临床练习改善HCV/HIV筛查并将患者与护理联系起来。我们的发现将提供及时的数据适应性的策略是消除HCV感染的国家和全球目标的关键。