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

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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.