TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews
Journal:
arXiv
Published Date:
Mar 26, 2025
Abstract
Thematic analysis (TA) is a widely used qualitative approach for uncovering
latent meanings in unstructured text data. TA provides valuable insights in
healthcare but is resource-intensive. Large Language Models (LLMs) have been
introduced to perform TA, yet their applications in healthcare remain
unexplored. Here, we propose TAMA: A Human-AI Collaborative Thematic Analysis
framework using Multi-Agent LLMs for clinical interviews. We leverage the
scalability and coherence of multi-agent systems through structured
conversations between agents and coordinate the expertise of cardiac experts in
TA. Using interview transcripts from parents of children with Anomalous Aortic
Origin of a Coronary Artery (AAOCA), a rare congenital heart disease, we
demonstrate that TAMA outperforms existing LLM-assisted TA approaches,
achieving higher thematic hit rate, coverage, and distinctiveness. TAMA
demonstrates strong potential for automated TA in clinical settings by
leveraging multi-agent LLM systems with human-in-the-loop integration by
enhancing quality while significantly reducing manual workload.
