Large Language Models for Large-Scale, Rigorous Qualitative Analysis in Applied Health Services Research
This work addresses the need for methodological guidance in using LLMs to improve efficiency in applied health services research, offering incremental advancements for researchers in this domain.
The researchers tackled the problem of integrating large language models (LLMs) into qualitative analysis for health services research by developing a model- and task-agnostic framework, which they applied in a multi-site diabetes care study to enable timely feedback and incorporate large-scale data from 167 interview transcripts.
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.