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
| Registry | NCT07827924, acronym DEONCAI3-1 |
|---|---|
| Status | Not yet recruiting |
| Phase | Not applicable |
| Study type | Interventional; randomized, parallel, masking: none, health services research |
| Enrollment | 330 (Estimated) |
| Sponsor | Harding Center for Risk Literacy (Other) |
| Conditions | Shared Decision Making, Health Literacy, Patient Participation, Artificial Intelligence |
| Interventions | Agentic LLM Chatbot, Usual Care LLM Chatbot, IQWiG Standard Brochure (Behavioral) |
| Dates | start 2026-09-15 (estimated), primary completion 2026-09-30 (estimated), completion 2026-09-30 (estimated), first posted 2026-09-18, last updated 2026-09-23 |
Related Papers Related Patents Related Grants Related Experts
All trials for Shared Decision Making All trials for Health Literacy All trials of Agentic LLM Chatbot All trials of Usual Care LLM Chatbot
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.
Background \& Current State of Research Shared Decision-Making is widely recognized as the gold standard of patient-centered care. However, its successful implementation in clinical practice is frequently hindered by systemic time and budget constraints. Consequently, patients are increasingly turning to Large Language Models to independently access health information and navigate their medical options.
The Problem: Limitations of Standard LLMs Recent studies indicate that using standard LLMs can digitally reproduce existing health inequalities and participation gaps. These models struggle to adapt to different reading levels without a significant loss in quality. While highly developed prompting techniques can enhance clinical accuracy, the results remain highly variable depending on the specific model and technique used.
A central deficit becomes apparent in multi-turn interactions, which are essential for a natural SDM workflow. In these prolonged conversations, standard LLMs tend to exhibit "context rot," leading to a sharp decline in accuracy as the dialogue progresses. This issue is further exacerbated by model sycophancy (an uncritical tendency to agree with the user) and completion eagerness (a tendency to prematurely conclude the conversation). Because current LLMs often fail to meet evidence-based reporting standards and lack systematic validation of user comprehension, the burden of fact-checking remains entirely on the user. This dynamic significantly disadvantages vulnerable groups, particularly those with lower health literacy.
Project Objectives and Research Questions
This project proposes a technical and clinical solution to these challenges through the following core research questions:
Technical-Conceptual Framework: How can we develop a transparent and replicable workflow that formalizes the SDM process through an agentic state-machine, thereby eliminating structural deficits of LLMs (e.g., context rot and sycophancy) in prolonged interactions?
Clinical Evaluation (Primary Objective): Does this newly developed agentic LLM architecture achieve higher scores in participatory conversation management and demonstrate a significantly greater objective increase in informed decision-making among users during multi-turn interactions, compared to a standard baseline LLM?
Health Equity (Secondary Objective): To what extent can this agentic LLM architecture guarantee effective participatory conversation management for vulnerable patient groups with low health literacy?
| Arm | Type | Interventions | Description |
|---|---|---|---|
| Shared Decision Making Chatbot | Experimental | Behavioral: Agentic LLM Chatbot | Participants will engage in a multi-turn, AI-assisted consultation about mammography screening. They will interact with a newly developed agentic Large Language Model architecture that uses an integrated state-machine designed to actively guide the Shared Decision-Making process, adapt to the user's reading level, and prevent context loss. |
| Standard Chatbot | Active comparator | Behavioral: Usual Care LLM Chatbot | Participants will engage in a conversation about mammography screening using a standard, usual care Large Language Model (Mistral Large). This model represents the current consumer standard for AI health queries and lacks the specialized agentic state-machine and SDM workflow guidance. |
| Information brochure | Active comparator | Behavioral: IQWiG Standard Brochure | Participants will receive and read the standard informational patient brochure on mammography screening published by the German Institute for Quality and Efficiency in Health Care (IQWiG). This represents the current standard of care for patient information. |
Sex: Female, ages: 40 Years to 70 Years, accepts healthy volunteers
Inclusion Criteria: * 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). * Registered and verified user on the academic research platform Prolific. * Able to read, understand, and provide informed consent digitally. Exclusion Criteria: * 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. Note: Quotas will be enforced during recruitment to ensure a 50/50 stratified split between participants with and without university entrance qualifications to guarantee variance in educational backgrounds. Once a quota is filled, further participants matching that educational profile will be excluded. \-