Assessing Performance of a Hepatitis C Emergency Department (HepC-EnD) Screening Tool: IT Integration Process for Electronic Health Record System
University of Florida
Summary
The goal of this observational study is to develop, implement, and evaluate a machine learning algorithm-based Hepatitis C Emergency Department (HepC-EnD) screening tool for use in emergency departments (EDs) to identify patients at high risk of hepatitis C virus (HCV) infection. HepC-EnD will be integrated into the University of Florida Health electronic health record (EHR) system as a best practice alert (BPA) pop-up for ED providers, notifying them of patients at high risk for HCV infection and recommending both HCV and human immunodeficiency virus (HIV) screening. Investigators aim to enhance the screening and diagnosis of individuals who may otherwise remain undiagnosed and untreated. The implementation outcomes (e.g., usability) and effectiveness outcomes (e.g., HCV screening and diagnosis rates) of HepC-EnD targeted screening will be compared with universal screening (FOCUS) and conventional physician-initiated screening programs in EDs.
Description
HCV infection has markedly increased in the United States, primarily resulting from injection drug use associated with the ongoing opioid epidemic. Despite the availability of highly effective direct-acting antiviral therapy, more than half of individuals with chronic HCV remain undiagnosed, leading to significant morbidity and mortality. EDs represent a critical setting for HCV and HIV screening, as they are currently the most common setting for missed diagnostic opportunities. However, universal ED-based screening programs are often costly and unsustainable. Moreover, existing targeted scree…