用大模型加速心脏病患儿家长访谈的分析，提升效率与可扩展性。
LLM-TA: An LLM-Enhanced Thematic Analysis Pipeline for Transcripts from Parents of Children with Congenital Heart Disease
- 结合GPT-4o mini与分块提示工程，构建自动化主题分析流程。
- 在9份访谈中生成主题，与人工结果相似度达0.82以上。
- 适合医疗研究者快速处理复杂质性数据，需专家协作验证。
主题分析（TA）是医疗研究中分析访谈数据的基础方法，但耗时耗力，难以扩展至大规模复杂数据集。本研究探讨大语言模型（LLMs）在高风险医疗场景下增强归纳式主题分析的潜力。聚焦于先天性心脏病患者——异常主动脉起源冠状动脉（AAOCA）患儿家长的访谈记录，提出基于大模型的主题分析（LLM-TA）流程。该流程融合低成本先进模型GPT-4o mini、LangChain及提示工程与分块技术，依据归纳式分析框架处理9份详细访谈文本。通过主题相似性指标、大模型辅助评估和专家评审，对比模型生成与人工结果。结果显示，该流程显著优于现有大模型辅助主题分析方法。尽管目前尚无法完全达到人类水平，但在协同专家使用时，展现出显著提升分析效率、准确率与可扩展性的潜力，并大幅降低分析师工作量。研究提供实用建议，强调领域专家深度参与对解决现实应用与数据复杂性挑战的重要性。
原文摘要 · Abstract (English)
Thematic Analysis (TA) is a fundamental method in healthcare research for analyzing transcript data, but it is resource-intensive and difficult to scale for large, complex datasets. This study investigates the potential of large language models (LLMs) to augment the inductive TA process in high-stakes healthcare settings. Focusing on interview transcripts from parents of children with Anomalous Aortic Origin of a Coronary Artery (AAOCA), a rare congenital heart disease, we propose an LLM-Enhanced Thematic Analysis (LLM-TA) pipeline. Our pipeline integrates an affordable state-of-the-art LLM (GPT-4o mini), LangChain, and prompt engineering with chunking techniques to analyze nine detailed transcripts following the inductive TA framework. We evaluate the LLM-generated themes against human-generated results using thematic similarity metrics, LLM-assisted assessments, and expert reviews. Results demonstrate that our pipeline outperforms existing LLM-assisted TA methods significantly. While the pipeline alone has not yet reached human-level quality in inductive TA, it shows great potential to improve scalability, efficiency, and accuracy while reducing analyst workload when working collaboratively with domain experts. We provide practical recommendations for incorporating LLMs into high-stakes TA workflows and emphasize the importance of close collaboration with domain experts to address challenges related to real-world applicability and dataset complexity. https://github.com/jiaweixu98/LLM-TA
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