Randomised Controlled Trial of Artificial Intelligence-assisted Health Education

NCT07305337 | Recruiting DMC

• 1 other identifier: 0603
• Study Type: interventional
• Target: 196
• Locations: 1 country
• Sites: 1

Brief Summary

With the rapid advancement of biopharmaceutical technology, clinical trials have become the crucial bridge connecting new drugs from the laboratory to clinical application. Despite the increasing number of clinical trial projects being conducted, nearly all such projects face the common challenge of recruitment difficulties. Subject recruitment constitutes a pivotal stage in clinical trials; the ability to recruit a sufficient number of subjects meeting the trial requirements significantly impacts trial quality and also serves as a key factor influencing trial progress. Hematologic cancers constitute a highly heterogeneous group of malignant diseases originating in the haematopoietic organs and primarily affecting the haematopoietic system. They encompass acute and chronic leukaemias, malignant lymphomas, multiple myeloma, myelodysplastic syndromes, and related disorders. For patients facing treatment decisions, clinical trials represent not only a vital avenue for accessing cutting-edge therapies but also impose heightened demands on their capacity for informed decision-making. Conversational artificial intelligence (AI) based on large language models is rapidly advancing in health education and public health communication. Medical chatbots offer scalable and personalised advantages in delivering health information, promoting behavioural change, and enhancing patient engagement, providing a viable pathway for improving trial literacy and decision support. Accordingly, this study proposes to conduct a clinical trial literacy intervention using AI-powered chatbots among haematological malignancy patients. Through a randomised controlled trial (RCT), it aims to evaluate the impact of AI-assisted health education on patients' understanding of clinical trials and intention to participate. This research seeks to validate the application value of AI technology in health education and explore scalable AI-assisted health education intervention models.

Conditions

Leukaemia; Multiple Myeloma (MM), Lymphoma, Large B-Cell, Diffuse (DLBCL), Lymphoma; Lymphoma

Keywords

Leukaemia; Multiple Myeloma; Lymphoma; health education; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• intention to participate

  Measurement via a questionnaire on patients' intention to participate in clinical trials and influencing factors.

  The first day of patient enrolment and the seventh day following completion of the one-week intervention

Secondary Outcomes (1)

• User experience

  The seventh day following completion of the one-week intervention

Study Arms (2)

Artificial Intelligence Health Education

EXPERIMENTAL

In addition to receiving standard health education, participants underwent clinical trial-specific education delivered via an AI robot. This educational content was designed around fundamental concepts of clinical trials, implementation procedures, clarification of common misconceptions, ethical safeguards, and potential benefits of participation. Its aim was to enhance patients' overall understanding of clinical trials and willingness to participate. The AI robot featured voice interaction capabilities and integrated text-image displays with video materials to enhance the interactivity and comprehensibility of information delivery.

Device: Artificial Intelligence Health Education

Artificial health education

ACTIVE COMPARATOR

Received only routine health education delivered by departmental healthcare staff, covering fundamental disease knowledge, treatment protocols, nursing management, and discharge instructions. This education forms part of the hospital's standard clinical practice and typically does not systematically incorporate content related to clinical trials or dedicated educational modules.

Other: Artificial health education

Interventions

Artificial Intelligence Health Education DEVICE

In addition to receiving standard health education, participants underwent clinical trial-specific education delivered via an AI robot. This educational content was designed around fundamental concepts of clinical trials, implementation procedures, clarification of common misconceptions, ethical safeguards, and potential benefits of participation. Its aim was to enhance patients' overall understanding of clinical trials and willingness to participate. The AI robot featured voice interaction capabilities and integrated text-image displays with video materials to enhance the interactivity and comprehensibility of information delivery.

Artificial Intelligence Health Education

Artificial health education OTHER

Received only routine health education delivered by departmental healthcare staff, covering fundamental disease knowledge, treatment protocols, nursing management, and discharge instructions. This education forms part of the hospital's standard clinical practice and typically does not systematically incorporate content related to clinical trials or dedicated educational modules.

Artificial health education

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may not qualify if:

• (1) Patients with concomitant cognitive impairment, psychiatric disorders, or other conditions severely affecting comprehension; (2) Anticipated hospital stay of less than 3 days, rendering completion of the intervention unfeasible; (3) End-of-life palliative care; (4) Previous participation in other clinical trial education programmes.

Sponsors & Collaborators

• Zhongnan Hospital: lead

Study Sites (1)

Zhongnan Hospital of Wuhan University

Wuhan, Hubei, 430071, China

RECRUITING

MeSH Terms

Conditions

Leukemia; Multiple Myeloma; Lymphoma; Health Education

Condition Hierarchy (Ancestors)

Neoplasms by Histologic Type; Neoplasms; Hematologic Diseases; Hemic and Lymphatic Diseases; Neoplasms, Plasma Cell; Hemostatic Disorders; Vascular Diseases; Cardiovascular Diseases; Paraproteinemias; Blood Protein Disorders; Hemorrhagic Disorders; Lymphoproliferative Disorders; Immunoproliferative Disorders; Immune System Diseases; Lymphatic Diseases; Adherence Interventions; Medication Adherence; Patient Compliance; Patient Acceptance of Health Care; Treatment Adherence and Compliance; Health Behavior; Behavior

Study Officials

• Fuling Fu Zhou

  Zhongnan Hospital of Wuhan Universty

  STUDY DIRECTOR

Central Study Contacts

Fuling Zhou

CONTACT

027-67813137; zhoufuling@163.com.cn

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dean, School of Nursing, Wuhan University; Tenured Full Professor, Director of Department of Hematology, zhongnan Hospital,Wuhan University

Study Record Dates

• First Submitted: June 3, 2025
• First Posted: December 26, 2025
• Study Start: June 28, 2025
• Primary Completion (Estimated): August 28, 2026
• Study Completion (Estimated): August 30, 2026
• Last Updated: December 26, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)