An interventional study of three minutes six dimensions education and ChatGPT in Condition Category Concordance, sponsored by Capital Medical University. Not yet recruiting at 3 sites in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-28.
Sponsored by Capital Medical University · Not applicable, Interventional, and Health services research
This study will evaluate whether three-minute six-dimension education (3M-6D education) can improve the reliability of large language models (LLMs) as medical assistants in neurology outpatients. Participants will be randomly assigned to receive or not receive 3M-6D education before using ChatGPT for medical consultation.
This randomized, controlled clinical trial will evaluate whether three-minute six-dimension education (3M-6D education) can improve the reliability of large language models (LLMs) as medical assistants in neurology outpatients.
Eligible participants will be randomly assigned in a 1:1 ratio to the 3M-6D education GPT group or the GPT group. Participants in the 3M-6D education GPT group will first receive approximately three minutes of structured education and then use ChatGPT for medical consultation. Participants in the GPT group will directly use ChatGPT for medical consultation without receiving 3M-6D education. All participants will communicate with ChatGPT using unrestricted natural language.
The 3M-6D education is a structured framework designed to help patients describe their symptoms and provide more complete medical information during LLM-assisted medical consultation. It guides patients to describe six key dimensions.
Exclusion Criteria:
Participants will first be trained in 3M-6D education, then use ChatGPT to complete a consultation task in unrestricted natural language.
Behavioral: three minutes six dimensions education · Other: ChatGPT
Participants will use ChatGPT to complete a consultation task in unrestricted natural language.
Other: ChatGPT
3M-6D education is designed based on Cognitive Load Theory to reduce the cognitive burden on patients during medical interactions with AI and to improve the clarity and completeness of symptom reporting. Guided by cognitive load theory and the natural process physicians use to take medical histories, the investigators identified candidate information dimensions and developed a structured expression framework with six dimensions for public health queries through a Delphi expert consensus process. Participants were instructed to use the framework to describe their symptoms across these six dimensions; this process can typically be completed within three minutes, so the investigators call this approach three minutes six dimensions education (3M-6D education).
Also known as: 3M-6D education
Participants use ChatGPT to complete a consultation task in unrestricted natural language.
Condition category concordance
The primary outcome of the LAMP-2 trial was condition category concordance, defined as at least one condition listed in a participant's response falling within the same prespecified neurological disease category as the final reference diagnosis.
Time frame: 30 days
Preparation for Decision Making Scale
Preparation for Decision Making Scale (PrepDM) is a self-reported scale used to assess participants' readiness for medical decision-making after medical consultation. Higher scores indicate greater preparation for decision-making.
Time frame: Within 1 hour
Consultation duration
Consultation duration is defined as the time from the patient's arrival at the outpatient clinic to the completion of the outpatient visit.
Time frame: Within 1 hour
Patient satisfaction with this consultation
Patient satisfaction is a self-reported measure used to assess participants' satisfaction with the outpatient visit. Higher scores indicate greater satisfaction.
Time frame: Within 1 hour after the outpatient visit
Red-flag identification
Red-flag identification is defined as the proportion of participants whose final response includes the key warning signs predefined by experts for the participant's presenting symptoms.
Time frame: Within 1 hour after the outpatient visit
NASA-TLX score
NASA-TLX score is a self-reported task-load score measured immediately after the LLM-assisted medical consultation with ChatGPT. It includes six domains: mental demand, physical demand, temporal demand, effort, frustration, and performance. Each domain is scored from 0 to 100. The total score is the mean of the six domains. Higher scores indicate greater perceived task load.
Time frame: Within 1 hour
Plan to share: Undecided
No publications or documents are linked to this record.
This study is not yet recruiting, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.
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Capital Medical University