An Agentic AI System for Multi-Framework Communication Coding

• URL: http://arxiv.org/abs/2512.08659v1
• Date: Tue, 09 Dec 2025 14:46:16 GMT
• Title: An Agentic AI System for Multi-Framework Communication Coding
• Authors: Bohao Yang, Rui Yang, Joshua M. Biro, Haoyuan Wang, Jessica L. Handley, Brianna Richardson, Sophia Bessias, Nicoleta Economou-Zavlanos, Armando D. Bedoya, Monica Agrawal, Michael M. Zavlanos, Anand Chowdhury, Raj M. Ratwani, Kai Sun, Kathryn I. Pollak, Michael J. Pencina, Chuan Hong,
• Abstract summary: We developed a Multi-framework Structured Agentic AI system for Clinical Communication (MOSAIC)<n>MOSAIC is built on a LangGraph-based architecture that orchestrates four core agents, including a Plan Agent for codebook selection and workflow planning.<n>To evaluate performance, we compared MOSAIC outputs against gold-standard annotations created by trained human coders.
• Score: 17.846847341760675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical communication is central to patient outcomes, yet large-scale human annotation of patient-provider conversation remains labor-intensive, inconsistent, and difficult to scale. Existing approaches based on large language models typically rely on single-task models that lack adaptability, interpretability, and reliability, especially when applied across various communication frameworks and clinical domains. In this study, we developed a Multi-framework Structured Agentic AI system for Clinical Communication (MOSAIC), built on a LangGraph-based architecture that orchestrates four core agents, including a Plan Agent for codebook selection and workflow planning, an Update Agent for maintaining up-to-date retrieval databases, a set of Annotation Agents that applies codebook-guided retrieval-augmented generation (RAG) with dynamic few-shot prompting, and a Verification Agent that provides consistency checks and feedback. To evaluate performance, we compared MOSAIC outputs against gold-standard annotations created by trained human coders. We developed and evaluated MOSAIC using 26 gold standard annotated transcripts for training and 50 transcripts for testing, spanning rheumatology and OB/GYN domains. On the test set, MOSAIC achieved an overall F1 score of 0.928. Performance was highest in the Rheumatology subset (F1 = 0.962) and strongest for Patient Behavior (e.g., patients asking questions, expressing preferences, or showing assertiveness). Ablations revealed that MOSAIC outperforms baseline benchmarking.

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