Leukaemia, Lymphoma, Multiple Myeloma (MM), Lymphoma, Large B-Cell, Diffuse (DLBCL), Lymphoma
Conditions
Keywords
Leukaemia, Multiple Myeloma, Lymphoma, health education, artificial intelligence
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.
Detailed description
This study aims to evaluate the effectiveness of artificial intelligence technology in health education, focusing on haematological cancer patients' awareness of and intention to participate in clinical trials. Through an AI-robot-mediated clinical trial science communication intervention, the research will systematically assess its impact on patients' cognitive levels, attitudes, and participation intentions, exploring a scalable new model for AI-assisted health interventions. Specific objectives include: (1) Investigating current levels of clinical trial awareness and participation attitudes among haematological malignancy patients; (2) Assessing the practical impact of AI-bot-delivered clinical trial awareness interventions on patients' understanding and intention to participate; (3) Exploring the feasibility and scalability of AI-assisted health education in promoting patient engagement in clinical trials.
Interventions
DEVICEArtificial Intelligence Health Education
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.
OTHERArtificial health education
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.
Sponsors
Zhongnan Hospital
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE (Outcomes Assessor)
Eligibility
Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
No
Inclusion criteria
(1) Aged ≥18 years with clear consciousness; (2) Diagnosed with haematological malignancy meeting clinical treatment criteria (WHO criteria); (3) Capable of understanding health education content and possessing basic communication skills; (4) Willing to participate in this study and sign an informed consent form.
Exclusion criteria
(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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| intention to participate | The first day of patient enrolment and the seventh day following completion of the one-week intervention | Measurement via a questionnaire on patients' intention to participate in clinical trials and influencing factors. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| User experience | The seventh day following completion of the one-week intervention | Through questionnaires on intention to use and satisfaction with robots, alongside interview guidelines, we gathered patients' willingness to use intelligent robots and their satisfaction, perceptions, and feedback regarding the robots' role in supporting health education. |
Countries
China
Contacts
Primary ContactFuling Zhou
Outcome results
None listed