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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.14478 |
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| _version_ | 1866909996533415936 |
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| author | Ronaghi, Sasha Aveling, Emma-Louise Levis, Maria Ross, Rachel Lauren Alsentzer, Emily Singer, Sara |
| author_facet | Ronaghi, Sasha Aveling, Emma-Louise Levis, Maria Ross, Rachel Lauren Alsentzer, Emily Singer, Sara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14478 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large Language Models for Large-Scale, Rigorous Qualitative Analysis in Applied Health Services Research Ronaghi, Sasha Aveling, Emma-Louise Levis, Maria Ross, Rachel Lauren Alsentzer, Emily Singer, Sara Computation and Language 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. |
| title | Large Language Models for Large-Scale, Rigorous Qualitative Analysis in Applied Health Services Research |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2601.14478 |