An interventional study of Agentic LLM Chatbot and Usual Care LLM Chatbot in Shared Decision Making, Health Literacy and Patient Participation, sponsored by Harding Center for Risk Literacy. Recruiting at 1 site in Germany. Open to female participants aged 40 Years to 70 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-10-30.
Sponsored by Harding Center for Risk Literacy · Not applicable, Interventional, and Health services research
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?
409 studies on the registry are indexed under Patient Participation; 101 are open to participants now.
This study's planned enrollment of 330 is above the median of 128 across 322 interventional studies indexed under Patient Participation.
Browse Patient Participation studies →Harding Center for Risk Literacy is the lead sponsor of 3 studies on the registry; 1 is open to participants now.
Counted across the registry records on this site, refreshed daily.
The criteria were revised on the registry on Oct 30, 2026. See what changed
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
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.
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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.
Behavioral: Agentic 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.
Behavioral: Usual Care LLM Chatbot
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.
Behavioral: IQWiG Standard Brochure
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.
An unmodified, standard generative Large Language Model chatbot acting as a baseline control, used by participants to discuss mammography screening.
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.
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 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 1
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 1
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 1
Plan to share: Yes — De-identified individual participant data (IPD) underlying the results reported in the published article, including survey data (SDM-Q-9, decisional conflict, knowledge scores) and anonymized dialog transcripts, will be shared.
Time frame: Data will become available immediately following publication of the primary results and will be accessible indefinitely.
Access criteria: Data will be made publicly available as supplement material on the publishers website to any researcher or individual for non-commercial research purposes.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
Eligibility is decided by the study team. Share this record with your doctor or contact the team directly.
Contact study teamGet an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Harding Center for Risk Literacy