Patient-Centered RAG for Oncology Visit Aid Following the Ottawa Decision Guide

TL;DR

This paper introduces an interactive system that uses retrieval-augmented generation to help prostate cancer patients prepare effectively for medical visits, improving communication and decision-making.

Contribution

It adapts the Ottawa Decision Guide into a dynamic AI-assisted workflow, enhancing patient preparation and clinician support in oncology care.

Findings

High system usability (UMUX=6.0/7)

Generated content is highly relevant (mean=6.7/7)

Clinicians find the system clinically faithful (mean=6.82/7)

Abstract

Effective communication is essential in cancer care, yet patients often face challenges in preparing for complex medical visits. We present an interactive, Retrieval-augmented Generation-assisted system that helps patients progress from uninformed to visit-ready. Our system adapts the Ottawa Personal Decision Guide into a dynamic retrieval-augmented generation workflow, helping users bridge knowledge gaps, clarify personal values and generate useful questions for their upcoming visits. Focusing on localized prostate cancer, we conduct a user study with patients and a clinical expert. Results show high system usability (UMUX Mean = 6.0 out of 7), strong relevance of generated content (Mean = 6.7 out of 7), minimal need for edits, and high clinical faithfulness (Mean = 6.82 out of 7). This work demonstrates the potential of combining patient-centered design with language models to enhance clinical preparation in oncology care.