A Multi-Agent Large Language Model Framework for Automated Qualitative Analysis
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909968556359680 |
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| author | Xu, Qidi Amjad, Nuzha Giles, Grace Cumming, Alexa Hermesky, De'angelo Wen, Alexander Kwak, Min Ji Kim, Yejin |
| author_facet | Xu, Qidi Amjad, Nuzha Giles, Grace Cumming, Alexa Hermesky, De'angelo Wen, Alexander Kwak, Min Ji Kim, Yejin |
| contents | Understanding patients experiences is essential for advancing patient centered care, especially in chronic diseases that require ongoing communication. However, qualitative thematic analysis, the primary approach for exploring these experiences, remains labor intensive, subjective, and difficult to scale. In this study, we developed a multi agent large language model framework that automates qualitative thematic analysis through three agents (Instructor, Thematizer, CodebookGenerator), named Collaborative Theme Identification Agent (CoTI). We applied CoTI to 12 heart failure patient interviews to analyze their perceptions of medication intensity. CoTI identified key phrases, themes, and codebook that were more similar to those of the senior investigator than both junior investigators and baseline NLP models. We also implemented CoTI into a user-facing application to enable AI human interaction in qualitative analysis. However, collaboration between CoTI and junior investigators provided only marginal gains, suggesting they may overrely on CoTI and limit their independent critical thinking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16063 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Agent Large Language Model Framework for Automated Qualitative Analysis Xu, Qidi Amjad, Nuzha Giles, Grace Cumming, Alexa Hermesky, De'angelo Wen, Alexander Kwak, Min Ji Kim, Yejin Human-Computer Interaction Artificial Intelligence Understanding patients experiences is essential for advancing patient centered care, especially in chronic diseases that require ongoing communication. However, qualitative thematic analysis, the primary approach for exploring these experiences, remains labor intensive, subjective, and difficult to scale. In this study, we developed a multi agent large language model framework that automates qualitative thematic analysis through three agents (Instructor, Thematizer, CodebookGenerator), named Collaborative Theme Identification Agent (CoTI). We applied CoTI to 12 heart failure patient interviews to analyze their perceptions of medication intensity. CoTI identified key phrases, themes, and codebook that were more similar to those of the senior investigator than both junior investigators and baseline NLP models. We also implemented CoTI into a user-facing application to enable AI human interaction in qualitative analysis. However, collaboration between CoTI and junior investigators provided only marginal gains, suggesting they may overrely on CoTI and limit their independent critical thinking. |
| title | A Multi-Agent Large Language Model Framework for Automated Qualitative Analysis |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2512.16063 |