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Designing a Large Language Model Architecture for Shared Decision Making and Informed Intentions
Sponsor: Harding Center for Risk Literacy
Summary
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.
Official title: DEONCAi 3-1: Can Large Language Models Support Shared Decision-Making and Informed Intentions in the Field: An Online Experiment Comparing Them to a Standard Model and to an Evidence-Based Decision Aid
Key Details
Gender
FEMALE
Age Range
40 Years - 70 Years
Study Type
INTERVENTIONAL
Enrollment
330
Start Date
2026-09-15
Completion Date
2026-09-30
Last Updated
2026-09-18
Healthy Volunteers
Yes
Interventions
Agentic LLM Chatbot
An 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
An unmodified, standard generative Large Language Model chatbot acting as a baseline control, used by participants to discuss mammography screening.
IQWiG Standard Brochure
The 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.