用多智能体AI+专家协作，提升临床访谈主题分析效率与质量
TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews
- 构建多智能体系统协同分析临床访谈，引入心脏专家指导
- 在先天性心脏病访谈中，主题识别率、覆盖率和独特性均优于单模型
- 适合医疗研究者、临床分析师，显著降低人工标注负担
主题分析（TA）是挖掘非结构化文本潜在含义的常用定性方法，在医疗领域具有重要价值，但耗时耗力。尽管大语言模型（LLMs）被用于主题分析，但在高风险医疗场景下的临床访谈分析应用仍有限。本文提出TAMA：一种基于多智能体LLM的人机协同主题分析框架，用于临床访谈分析。通过智能体间结构化对话并整合心脏专科专家的知识，我们以先天性异常主动脉起源冠状动脉（AAOCA）患儿家长访谈数据为案例，验证了TAMA在主题命中率、覆盖范围和独特性上均优于单智能体方法。该框架通过人机协同显著提升了分析质量，同时大幅减少人工工作量，具备在临床环境中自动化主题分析的潜力。完整代码已开源：https://github.com/Charlie-Yi-SJ/TAMA。
原文摘要 · Abstract (English)
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 high-stakes healthcare settings, particularly for qualitative clinical interview analysis, remain limited. 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 single-agent LLM 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. The full implementation is publicly available at https://github.com/Charlie-Yi-SJ/TAMA.
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