When investigator meets large language models: a qualitative analysis of cancer patient decision-making journeys

Abstract

Large language models (LLMs) are transforming the landscape of healthcare research, yet their role in qualitative analysis remains underexplored. This study compares human-led and LLM-assisted approaches to analyzing cancer patient narratives, using 33 semi-structured interviews. We conducted three parallel analyses: investigator-led thematic analysis, ChatGPT-4o, and Gemini Advance Pro 1.5. The investigator-led approach identified psychosocial and emotional themes, while the LLMs highlighted structural, temporal, and logistical aspects. LLMs demonstrated efficiency in identifying recurring patterns but struggled with emotional nuance and contextual depth. Investigator-led analysis, while time-intensive, captured the complexities of identity disruption and emotional processing. Our findings suggest that LLMs can serve as complementary tools in qualitative research, enhancing analytical breadth when paired with human interpretation. This study proposes a hybrid model integrating AI-assisted and human-led methods and offers practical recommendations for responsibly incorporating LLMs into qualitative health research.