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

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: