An agentic AI system for multi-framework communication coding

Bohao Yang

doi:10.1038/s41746-026-03314-w

An agentic AI system for multi-framework communication coding

2026-10-11

0

OA

AI

Brianna N. Richardson

S

Sophia Bessias

N

Nicoleta Economou-Zavlanos

DOI：10.1038/s41746-026-03314-w

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Clinical communication is central to patient outcomes, yet large-scale annotation of patient–provider conversations remains labor-intensive, inconsistent, and difficult to scale. Existing large language model (LLM)–based approaches typically rely on single-task systems with limited robustness across communication frameworks and clinical domains. In this study, we developed MOSAIC, a Multi-framewOrk Structured Agentic AI system for scalable Clinical Communication coding. MOSAIC combines dynamically few-shot prompting, retrieval-augmented generation (RAG), and a LangGraph-based multi-agent workflow for planning, annotation, and verification. Performance was evaluated against gold-standard annotations produced by trained human coders. MOSAIC was developed on 54 annotated transcripts and evaluated on 120 transcripts spanning rheumatology and obstetrics/gynecology (OB/GYN). On the evaluation set, MOSAIC achieved an overall weighted F1 score of 95.6%. Performance was highest in rheumatology (F1 = 95.9%) and strongest for bias and patient behaviors categories (F1 = 96.4% and 96.1%, respectively). These results demonstrate the potential of multi-framework AI systems for reliable clinical communication analysis.

An agentic AI system for multi-framework communication coding

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