Designing a Large Language Model Architecture for Shared Decision Making and Informed Intentions

Overview

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?