Union-FAST: An Intelligent-Agent Intervention to Increase Antiviral Treatment Uptake in Diagnosed-but-Untreated Hepatitis B Patients

Summary

The World Health Organization (WHO) has set a target to eliminate viral hepatitis by 2030, aiming for a 90% diagnosis rate and an 80% treatment rate for chronic hepatitis B (CHB). However, as of 2024, only 26.1% of CHB infections globally have been diagnosed, and only 14.6% have received treatment, with treatment coverage falling far short of the target. A large number of patients are in a "Diagnosed-but-Untreated (DBU)" state, with major barriers including: low disease awareness, concerns about medication side effects, fragmented healthcare pathways, and poor physician-patient communication. Traditional hospital-based follow-up models are constrained by human resources and the capacity for health information system integration, making them difficult to scale widely in primary care settings.

Supported by the National Key R\&D Program of China, our team has successfully developed the world's first infectious disease agent (Union-Agent) after more than two years of research. This study aims to conduct a multicenter, prospective, two-cohort observational and interventional investigation to identify the reasons why DBU patients fail to initiate treatment and to explore whether an intervention using the Union-Agent can significantly increase the rate of antiviral treatment initiation within six months among DBU patients who meet the antiviral indications according to the 2022 Chinese guidelines for the prevention and treatment of chronic hepatitis B. The study hypothesizes that, compared to baseline, the Union-Agent can enable 50%-60% of treatment-eligible DBU patients to initiate antiviral therapy within six months.

Conditions

• Chronic Hepatitis B

Interventions

OTHER

Agent Intelligence education

Patients, while receiving usual care, will also receive four services from Union-Agent: (i) guideline-based educational modules tailored to patients' baseline knowledge of hepatitis B; (ii) personalized medication reminders with real-time adherence records; (iii) semantic follow-up that converts patient-reported symptoms into structured data and generates early risk alerts; and (iv) algorithm-driven referral suggestions aligned with China's three-tiered healthcare referral system.

Sponsors & Collaborators

• First Affiliated Hospital of the School of Medicine, Shihezi University

  collaborator UNKNOWN

• Taihe Hospital in Shiyan

  collaborator UNKNOWN

• Yichang Central People's Hospital

  collaborator OTHER

• The Second Hospital of Anhui Medical University

  collaborator OTHER

• Wuhan Jinyintan Hospital

  collaborator UNKNOWN

• Zigui County People's Hospital

  collaborator UNKNOWN

• Shanghai Zhongshan Hospital

  collaborator OTHER

• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

  lead OTHER

Study Design

Allocation

NA

Purpose

SUPPORTIVE_CARE

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2026-05-01

Primary Completion

2027-11-01

Completion

2027-12-31

Countries

• China

Study Locations