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.

项目概要/摘要丙型肝炎病毒 (HCV) 感染在美国显着增加，主要由注射引起此外，超过 50% 的 320 万人患有阿片类药物滥用。尽管有可用的治疗方法，但慢性丙型肝炎仍未得到诊断，导致发病率和死亡率显着升高有效的直接抗病毒治疗由于共同的传播途径，HCV 感染发生率为 15%-40%。感染人类免疫缺陷病毒 (HIV) 的人的数量，可用作 HIV 暴露的标志。急诊科 (ED) 在筛查 HCV 感染和 HIV 感染方面发挥着重要作用。基于 HCV 筛查计划已经实施，并已识别出以前未识别的 HCV 感染，但仍然存在许多挑战，因为有针对性的筛查计划使用的方法往往失败。检测高风险行为（例如，预筛问卷或患者审查中的自我报告信息）访视时的问题清单），但它们不能有效识别 HCV 感染高危人群（例如注射吸毒者）。非针对性 HCV 筛查策略需要较少的风险行为评估，但存在以下问题。高成本和不必要的测试使得非靶向筛查策略难以实施和维持。因此，迫切需要一种创新、有效、可持续的HCV筛查策略。我们建议开发、实施和评估量身定制的、有效的、可持续的预测算法—— 基于名为丙型肝炎急诊科 (HepC-EnD) 的筛查工具，可供医疗保健部门使用我们将通过三个具体目标来实现这些目标。目标 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费电子健康记录 (EHR) 来识别有 HCV 感染风险的患者通过 OneFlorida+ 临床研究联盟，在目标 2 中，我们将设计一个 HepC-EnD 原型。结合了最好的预测算法，向急诊室提供者提供高危患者的自动通知根据实施科学框架的信息，我们将增强功能和通过研讨会和定性访谈提高 HepC-EnD 的可用性在目标 3 中，我们将把 HepC-EnD 集成到其中。佛罗里达大学健康 EHR 系统在两个急诊室（盖恩斯维尔和 Jacksonville）并使用差异比较 HepC-EnD 与非靶向筛查的性能差异方法将通过评估可用性、可接受性、有效性和我们提出的研究对于尖端技术的集成非常重要。基于机器学习的预测和风险分层工具集成到电子平台中，更好地为临床提供信息我们的研究结果将提供及时的数据。以及对实现消除丙肝病毒感染的国家和全球目标至关重要的适应性战略。