A Multi-Modular Human-AI Workflow for LLM-Assisted Thematic Analysis: Application to COPD Telerehabilitation...

Large language models (LLMs) are increasingly explored for qualitative analysis, but the effect of workflow design on thematic fidelity remains unclear. This study evaluated a structured human-AI collaboration framework using Claude Opus 4.6 to analyze 16 interview transcripts from patients with chronic obstructive pulmonary disease participating in a pulmonary telerehabilitation program. The workflow included code extraction, code combination, and theme generation, and was tested using hierarchical and direct strategies. AI-generated themes were compared with human-derived themes using sentence-t5-xxl embeddings and cosine similarity, with theme alignment performed using Hungarian and greedy matching. Output volume varied substantially across strategies, ranging from 53 to 357 codes and 11 to 17 themes. Direct grouping (average cosine similarity 0.891) and L3 grouping (0.890) achieved the highest similarity to human-generated themes. These findings suggest that grouping-based workflows can preserve key information, reduce redundancy, and improve thematic generation in LLM-assisted qualitative analysis.