Faculty, Staff and Student Publications
Language
English
Publication Date
1-1-2026
Journal
Computational and Structural Biotechnology Journal
DOI
10.1016/j.csbj.2025.12.007
PMID
41551040
PMCID
PMC12809031
PubMedCentral® Posted Date
12-18-2025
PubMedCentral® Full Text Version
Post-print
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.
Keywords
Digital twins, Large language models, Artificial intelligence, Provider-patient communication, Conversational agents
Published Open-Access
yes
Recommended Citation
Li, Pengze; Hu, Yutong; Li, Jianfu; et al., "Developing Provider Digital Twins for Personalized Provider-Patient Communication via a Rag-Based Conversational Framework" (2026). Faculty, Staff and Student Publications. 705.
https://digitalcommons.library.tmc.edu/uthshis_docs/705
Graphical Abstract