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Not yet recruitingNCT07406490HepC-EnDUpdated Apr 29, 2026

Assessing Performance of a Hepatitis C Emergency Department (HepC-EnD) Screening Tool: IT Integration Process for Electronic Health Record System

An observational study in Hepatitis C Virus (HCV), Hepatitis C Virus (HCV) Infection and HIV (Human Immunodeficiency Virus), sponsored by University of Florida. Not yet recruiting at 3 sites in United States. Open to participants aged 18 Years to 79 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-04-29.

Sponsored by University of Florida · Observational

Study type
Observational
Model
Cohort
Time perspective
Prospective
Enrollment
6,466
Ages
18 Years to 79 Years
Sex
All
01

Study 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.

Read the detailed 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 screening programs are limited, and have not been systematically developed or rigorously evaluated in clinical practice. Thus, there is a critical public health need to develop innovative, tailored, effective, and sustainable screening strategies to enhance HCV screening in EDs.

This study will accomplish three specific aims:

  1. Develop and validate prediction algorithms using machine learning and natural language processing (NLP) to identify patients at high risk of HCV infection
  2. Develop the HCV screening tool prototype HepC-EnD for implementation in EDs
  3. Compare the usability, effectiveness, and cost-effectiveness of an automated HepC-EnD prompt for HCV (with HIV) testing versus universal and physician-initiated screening strategies

This study is guided by multiple implementation science frameworks, including the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, Proctor's Implementation Outcomes, and Five Rights, which will greatly increase the tool's utility, sustainability, and generalizability.

The investigators will conduct a quasi-experimental study to compare HepC-EnD to two existing screening strategies across three UF Health EDs over 12 months (6 months pre-implementation and 6 months post-implementation). UF Jacksonville Downtown ED will transition from universal screening (FOCUS) to HepC-EnD. UF Jacksonville North ED will continue FOCUS throughout the study period to serve as a control. UF Gainesville ED will pilot HepC-EnD, as FOCUS has not previously been implemented at that site. The study will evaluate the effectiveness of HepC-EnD's within-site and between-site comparisons.

The investigator's central hypothesis is that the use of HepC-EnD will have lower HCV and HIV screening rates but higher diagnosis rates and will be more cost-effective than universal screening and physician-initiated screening strategies.

02

Conditions studied

  • Hepatitis C Virus (HCV)
  • Hepatitis C Virus (HCV) Infection
  • HIV (Human Immunodeficiency Virus)

Keywords

  • Emergency Department
  • Hepatitis C
  • Human Immunodeficiency Virus
  • Implementation Study
  • Machine Learning
  • Screening Algorithm
  • High-Risk Prediction
03

Who can participate

Ages eligible
18 Years to 79 Years
Sexes eligible
All
Accepts healthy volunteers
Yes
Sampling method
Probability sample

Study population

Patients visiting UF Health emergency departments.

Inclusion criteria

  • 18-79 years of age

Exclusion criteria

Exclusion Criteria:

  • \< 18 years of age
  • Medically unstable
04

Study design

Observational model
Cohort
Time perspective
Prospective
Enrollment
6,466 participants (estimated)
Patient registry
No

Groups and cohorts

  • UF Jacksonville North ED

    Patients presenting to UF Jacksonville North ED who opt-in for HCV screening during nurse triage.

    Other: FOCUS (Universal Screening)

  • UF Jacksonville Downtown ED

    Patients presenting to UF Jacksonville Downtown ED who opt-in for HCV screening during nurse triage (pre- and post-implementation).

    Other: FOCUS (Universal Screening) · Other: HepC-EnD (Targeted Screening)

  • UF Gainesville ED

    Patients presenting to UF Gainesville ED (pre-implementation) and patents presenting to UF Gainesville ED who opt-in for HCV during nurse triage (post-implementation).

    Other: Physician-Initiated Screening (Conventional Screening) · Other: HepC-EnD (Targeted Screening)

Interventions

  • OtherPhysician-Initiated Screening (Conventional Screening)

    Screening for HCV and HIV in patients presenting to the ED occurs when an ED provider initiates screening based on symptoms or clinical judgement. Providers will manually order individual tests in the EHR.

    Also known as: Standard of Care

  • OtherFOCUS (Universal Screening)

    During nurse triage, a FOCUS screening question will appear in the EHR and the patient will be asked to opt-in to HCV and HIV testing. For those who consented, if an ED provider enters a phlebotomy order for any reason in the EHR, a BPA will alert the providers to suggest HCV and HIV testing. The provider can decide to "order" or "do not order" for each test individually. Ordered tests automatically trigger the following in the EHR: HCV antibody with reflex to RNA and HIV 1/2 antigen/antibody with reflex to confirmation. For all patients who received positive test result in the ED, standardized linkage-to-care processes will be performed. These procedures are currently implemented in clinical practice.

    Also known as: FOCUS, Universal Screening

  • OtherHepC-EnD (Targeted Screening)

    HepC-EnD will run in real time once integrated into the hospital's Epic EHR system. When the patient comes to the ED waiting room, a risk score generated from HepC-EnD will be available and determine if the patient is at high risk of HCV infection (\> cutoff risk score). If the patient is determined to be at high risk, a HepC-EnD screening question will appear in the EHR during nurse triage and the patient will be asked will be asked to opt-in to HCV and HIV testing. For those who consented, a BPA will alert the ED provider to suggest HCV and HIV testing. The provider can decide to "order" or "do not order" for each test individually. Ordered tests automatically trigger the following in the EHR: HCV antibody with reflex to RNA and HIV 1/2 antigen/antibody with reflex to confirmation. For all patients who received positive test result in the ED, standardized linkage-to-care processes will be performed.

    Also known as: HepC-EnD

05

What researchers measure

Primary outcomes

  1. Proportion of new HCV or HIV diagnoses

    Proportion of positive results among performed tests. HCV diagnosis is defined as a positive RNA test result. HIV diagnosis is defined as an acute (i.e., antigen positive but antibody negative) or established (i.e., antibody positive) infection.

    Time frame: Time Frame: 6 months pre- and post-implementation

  2. Absolute number of new HCV or HIV diagnoses

    Absolute number of positive results among performed tests. HCV diagnosis is defined as a positive RNA test result. HIV diagnosis is defined as an acute (i.e., antigen positive but antibody negative) or established (i.e., antibody positive) infection.

    Time frame: 6 months pre- and post-implementation

Secondary outcomes

  1. Proportion of BPA alerts among individuals presenting to EDs

    BPA alert for HepC-End or universal screening

    Time frame: 6 months pre- and post-implementation

  2. Proportion of HCV and HIV tests performed among BPA alerts

    Time frame: 6 months pre- and post-implementation

  3. Proportion of patients linked to care among those with positive HCV and HIV diagnoses

    Linkage to care will be defined as a patient attending a first medical appointment within 3 months of receiving an HCV or HIV diagnosis.

    Time frame: 3 months after diagnosis

  4. Composite HCV or HIV Diagnoses

    All HCV and HIV diagnoses will be considered separately and will include both new and repeat diagnoses.

    Time frame: 6 months pre- and post-implementation

06

Study locations

3 sites
  • UF Health Shands Emergency Room / Trauma Center
    Gainesville, Florida 32608, United States
  • UF Health Jacksonville Emergency Room
    Jacksonville, Florida 32209, United States
  • UF Health North Emergency Room
    Jacksonville, Florida 32218, United States
07

References and documents

Publications

  • Jang SC, Lo-Ciganic WH, Hernandez-Con P, Jenjai C, Huang J, Stultz A, Yan S, Wilson DL, Norse A, Guirgis FW, Cook RL, Gage C, Nguyen KA, Hornes P, Wu Y, Nelson DR, Park H. Development and Validation of a Machine Learning-Based Screening Algorithm to Predict High-Risk Hepatitis C Infection. Open Forum Infect Dis. 2025 Aug 15;12(8):ofaf496. doi: 10.1093/ofid/ofaf496. eCollection 2025 Aug. PubMed 40874186 ↗
08

Registry details

Key details

Study ID
NCT07406490
Lead sponsor
University of Florida
Collaborators
National Institute on Drug Abuse (NIDA)
Responsible party
Sponsor
First posted
Feb 12, 2026
Start date
Jul 1, 2026 (estimated)
Primary completion
Jun 30, 2027 (estimated)
Completion
Jul 2027 (estimated)
Last update
Apr 29, 2026

Study contacts

Haesuk Park, PhD
Contact
hpark@cop.ufl.edu
352-273-6261
Khoa A Nguyen, PharmD
Contact
nguyen.khoa@ufl.edu
352-273-9418
Haesuk Park, PhD
principal investigator · University of Florida

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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