aceEfficiency vs Depth in AI-Generated and Human-Synthesized Thematic Analyses of Ugandan Women's Experiences of Obstetric Fistula: Comparative Qualitative Study
Background Limited recent literature has evaluated the use of large language models (LLMs) in the qualitative analysis of health data; more research is needed to expand the generalizability of LLM use and to evaluate potential ethical considerations. Objective Our research sought to (1) describe the process of using AI to analyze qualitative in-depth interview data, (2) identify similarities and differences between the human and AI-generated analyses to compare the quality and rigor of the two techniques and des…
Using the Versa LLM to analyze interviews with women recovering from fistula surgery cut analysis time from about 15 hours to about 2.5 hours.
Versa's analysis lacked the narrative depth, compelling excerpts and range of the human synthesis, so it could not capture lived experience without substantial human involvement.
The study is a small comparison nested within a larger mixed methods study, and the authors note that more research is needed to expand the generalizability of LLM use and to evaluate ethical considerations.
Evidence
- Peer-reviewedJournal of Medical Internet Research2026-10-09
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Truvace Impact Record TRV-2026-1653, v1: “Efficiency vs Depth in AI-Generated and Human-Synthesized Thematic Analyses of Ugandan Women's Experiences of Obstetric Fistula: Comparative Qualitative Study.” Truvace, 2026-10-17. /record/TRV-2026-1653 (accessed at citation time). sha256 0580b4822351ee89…
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