The VALUE of AI-Guided Communication: Enhancing Shared Decision-Making in Metabolic Bariatric Surgery Consultations...

Effective communication in metabolic bariatric surgery (MBS) is essential for patient engagement and adherence, yet surgical residents often lack structured training. Artificial intelligence offers a novel approach to scaffold communication skills.

To evaluate the impact of an AI-Guided metacognitive framework VALUE (Validate, Align & Reframe, Link & Educate, Unite in a plan) on shared decision-making (SDM) and communication outcomes in MBS consultations compared to self-directed learning.

Forty surgical residents were randomized into two groups: AI-Guided (using the VALUE framework) and self-learning. The AI-Guided group used a structured prompt to interact with a large language model (DeepSeek-V3.2) to generate personalized consultation plans. Each conducted simulated consultations with standardized patients from a case library. Outcomes were measured using the Shared Decision-Making Questionnaire-9 (SDM-Q-9), Decision Conflict Scale (DCS), Four Habits Coding Scheme (4HCS), Surgeon Self-Efficacy scale (SSI-BS), Communication Outline Quality Scale (CQS), and AI Interaction Quality (AIIQ). The trial was registered on the Open Science Framework (Registration DOI: https://doi.org/10.17605/OSF.IO/BAQH6 ).

The AI-Guided group scored significantly higher on SDM-Q-9 (84.7 vs. 71.3, p < 0.01) and 4HCS (17.5 vs. 14.8, p < 0.01), and lower on DCS (19.5 vs. 32.1, p < 0.01). Communication outlines were also of higher quality (13.8 vs. 7.5, p < 0.01). Residents reported greater self-efficacy gains in information provision, values integration, decision facilitation, and emotional support. All secondary analyses remained significant after Benjamini-Hochberg correction for multiple comparisons.

An AI-Guided metacognitive communication framework significantly improves shared decision-making, reduces decisional conflict, and enhances communication quality and self-efficacy in MBS consultations, suggesting a promising approach that requires further validation in larger, multi-site trials.