Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning

Seungjun Yi, Joakim Nguyen, Huimin Xu, Terence Lim, Andrew Well, Mia Markey, Ying Ding

Keywords: thematic analysis, large language models, reinforcement learning, qualitative research, multi-agent systems, clinical narratives, automated coding

TL;DR: We propose Auto-TA, a fully automated thematic analysis framework using multi-agent LLMs and reinforcement learning to extract themes from clinical narratives without manual coding.

Abstract: Congenital heart disease (CHD) presents complex, lifelong challenges often underrepresented in traditional clinical metrics. While unstructured narratives offer rich insights into patient and caregiver experiences, manual thematic analysis (TA) remains labor-intensive and unscalable. We propose a fully automated large language model (LLM) pipeline that performs end-to-end TA on clinical narratives which eliminates the need for manual coding or full transcript review. Our system employs a novel multi-agent framework, where specialized LLM agents assume roles to enhance theme quality and alignment with human analysis. To further improve thematic relevance, we optionally integrate reinforcement learning from human feedback (RLHF). This supports scalable, patient-centered analysis of large qualitative datasets and allows LLMs to be fine-tuned for specific clinical contexts.

Archival Status: Non-archival

Paper Length: Long Paper (up to 8 pages of content)

Submission Number: 292