用大模型提升医疗研究定性分析效率，兼顾速度与严谨。
Large Language Models for Large-Scale, Rigorous Qualitative Analysis in Applied Health Services Research
- 设计通用框架，让人类与大模型协作完成定性分析。
- 在167份访谈中实现快速编码，支持临床干预优化。
- 适合医疗政策与健康服务研究者参考应用。
大型语言模型（LLMs）在提升多中心医疗服务研究中定性分析效率方面展现出潜力，但其在实际研究中的方法论指导和对研究流程与成果的影响仍缺乏证据。本文提出一种模型与任务无关的框架，用于设计人机协同的定性分析方法，以支持多样化的分析目标。在一项针对联邦合格健康中心（FQHCs）糖尿病护理的多中心研究中，我们利用该框架实施了两种人机协作任务：（1）对研究人员生成的摘要进行定性综合，产出对比反馈报告；（2）对167份访谈转录稿进行演绎编码，以优化实践转型干预措施。大模型辅助实现了对临床人员的及时反馈，并整合大规模定性数据以支持理论与实践改进。本研究证明，大模型可有效融入应用型医疗服务研究，在保障严谨性的前提下显著提升效率，为未来在定性研究中持续创新提供指导。
原文摘要 · Abstract (English)
Large language models (LLMs) show promise for improving the efficiency of qualitative analysis in large, multi-site health-services research. Yet methodological guidance for LLM integration into qualitative analysis and evidence of their impact on real-world research methods and outcomes remain limited. We developed a model- and task-agnostic framework for designing human-LLM qualitative analysis methods to support diverse analytic aims. Within a multi-site study of diabetes care at Federally Qualified Health Centers (FQHCs), we leveraged the framework to implement human-LLM methods for (1) qualitative synthesis of researcher-generated summaries to produce comparative feedback reports and (2) deductive coding of 167 interview transcripts to refine a practice-transformation intervention. LLM assistance enabled timely feedback to practitioners and the incorporation of large-scale qualitative data to inform theory and practice changes. This work demonstrates how LLMs can be integrated into applied health-services research to enhance efficiency while preserving rigor, offering guidance for continued innovation with LLMs in qualitative research.
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