Designing a Large Language Model Architecture for Shared Decision Making and Informed Intentions
Trial statusNot yet recruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexFemale
Age40-70
SponsorHarding Center for Risk Literacy
About this trial
This study investigates a novel, agentic Large Language Model (LLM) architecture designed to facilitate Shared Decision-Making (SDM) in healthcare. While patients increasingly use standard LLMs for health information, these models often struggle with multi-turn conversations and fail to adapt to varying reading levels, disadvantaging vulnerable groups. By utilizing an agentic state-machine, this project aims to overcome common LLM deficits-such as context loss and uncritical agreement-to ensure clinically accurate, participatory patient conversations. The study evaluates whether this architecture improves informed decision-making compared to standard LLMs, particularly for patients with low health literacy.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Biological sex: Female.
Age: 40 to 70 years old.
Country of residence: United States (US) or United Kingdom (UK).
Language: Native English speaker (English as first language).
Disqualifiers
Individuals who do not meet the automated pre-screening criteria on the Prolific platform.
Inability to use or access a computer, smartphone, or internet browser required to complete the digital study.
Trial design
Design model
Parallel
Treatments tested in this trial
Agentic LLM Chatbot
BehavioralAn interactive AI chatbot built with an agentic state-machine architecture designed to systematically guide users through the Shared Decision-Making process regarding mammography screening, with built-in ethical guardrails to prevent hallucination and sycophancy.
Usual Care LLM Chatbot
BehavioralAn unmodified, standard generative Large Language Model chatbot acting as a baseline control, used by participants to discuss mammography screening.
IQWiG Standard Brochure
BehavioralThe standard digital informational brochure on mammography screening provided by the german Institute for Quality and Efficiency in Health Care (IQWiG), used as a usual care baseline for patient education.
Treatment groups
330 Participants
are divided into 3 treatment groupsGroup A: Shared Decision Making ChatbotExperimental treatment 1 intervention
Group B: Standard ChatbotActive comparator 1 intervention
Group C: Information brochureActive comparator 1 intervention
Trial outcomes
Primary outcomes
1
Patient-Reported Quality of Shared Decision-Making
Assessed using the 9-item Shared Decision Making Questionnaire (SDM-Q-9). This validated instrument measures the patient's perceived involvement in the medical decision-making process. The questionnaire consists of 9 items, each rated on a 6-point scale ranging from 0 ("completely disagree") to 5 ("completely agree"). The raw scores are summed and multiplied by 20/9 to yield a total score ranging from 0 to 100. Higher scores indicate a higher perceived quality and greater patient involvement in shared decision-making.
Time frame
Day 1Secondary outcomes
1
Multidimensional Informed Decision-Making
Based on Marteau's conceptualization of informed decision-making. An informed decision is achieved when a participant has sufficient objective knowledge about mammography screening and their behavioral intention (to screen or not to screen) is congruent with their personal attitudes toward the screening. This is calculated as a composite measure integrating an objective knowledge test, an attitude assessment, and a screening intention question. Higher objective knowledge scores and higher attitude-intention congruence indicate a better-informed decision.
Time frame
Day 12
Subjective Decisional Conflict
Assessed using a standardized Decisional Conflict Scale. This measures the participant's perceived uncertainty in choosing a screening option, their clarity of personal values, and their feeling of being supported in decision-making. Scores are transformed to a 0 to 100 scale. Lower scores indicate less decisional conflict and greater decision certainty.
Time frame
Day 13
Medical Accuracy and Safety of Generated Information
A quantitative and qualitative assessment of the medical accuracy of the AI-generated texts. This is operationalized by counting the frequency (number of occurrences) and categorizing the severity of deviations from established medical evidence (i.e., misinformation or "hallucinations"). These deviations are identified via an integrated ethics module and/or expert review. Lower frequencies and lower severity ratings indicate higher medical accuracy.
Time frame
Day 1Locations
Sponsors and contacts
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Source ClinicalTrials.gov
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