Developing provider digital twins for personalized provider-patient communication via a RAG-based conversational framework

Abstract

Digital twins have emerged as a paradigm in precision and personalized medicine, enabling data-driven modeling of individuals to support tailored interventions. While most existing work focuses on patient-oriented twins, little attention has been given to modeling the provider's role, particularly in clinical communication. In this study, we present GRACE (Generalized RAG-Enhanced Conversation Framework), a framework for constructing a provider digital twin (ProDT) that emulates key aspects of clinicians' communicative and cognitive behavior. GRACE integrates three modules: a physician-informed dialog script generation and optimization module for provider-patterned conversation, a Retrieval-Augmented Generation (RAG) pipeline for factual grounding and timely knowledge updating, and an LLM-based conversational interface that enables interactive, context-aware exchanges. Using HPV vaccination counseling as a representative use case, GRACE was evaluated with HealthBench and a structured user study involving clinician feedback. The results demonstrate its feasibility, trustworthiness, and adaptability for proactive provider-patient communication, marking a conceptual step toward safe, scalable, and cognitively grounded digital twins in healthcare.

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