Generative AI Patient Education Module for Breast Oncology

Summary

This study evaluates the safety, accuracy, and impact of an artificial intelligence (AI) tool designed to support patient education in breast cancer care for breast oncology patients under selected physicians care within the University of California, San Francisco breast cancer center and affiliate sites.

Detailed Description

PRIMARY OBJECTIVES: I. Lead-in period: To evaluate the safety of the OpenEvidence CareConnect a Retrieval-Augmented Generation (RAG) Large Language Model (LLM) tool during the initial lead-in phase in patients with metastatic breast cancer. II. To evaluate the clinical accuracy and appropriateness of the OpenEvidence CareConnect RAG LLM tool in delivering general health and breast cancer-specific information. SECONDARY OBJECTIVES: I. To assess patient perceptions of the AI tool's helpfulness, alignment with other information sources, and its impact on communication with their oncology care team. II. To evaluate provider perceptions of the tool's usefulness and impact on communication and the quality of patient education. III. To compare patient experiences and perceptions between immediate access and delayed access to the AI educational tool. EXPLORATORY OBJECTIVES: I. To evaluate the changes in inter-visit communication between patient and care team with and without access to the AI tool. II. To understand patient concerns and educational needs related to breast cancer diagnosis and treatment. OUTLINE: The first 5 participants will be assigned to the Lead-In Phase. After which, the next 30 participants will be randomized to either an Intervention arm or a Waitlist Control arm. Participants on the Waitlist Control arm will cross over after 3 months to the Intervention arm. Surveys and provider assessments occur throughout, and adherence and safety are monitored during the lead-in. Each participant will be on study for approximately 6 months.

Eligibility Criteria

Interventions