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Principal Investigator: Haesuk Park
Organization: UNIVERSITY OF FLORIDA
Fiscal Year: 2024
Award: $651,976
Funding agency: National Institute on Drug Abuse
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.
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