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AI Qualitative Analysis: Lessons from ELLY
Researchers evaluating community health interventions need to understand more than whether an outcome changed. Researchers also need to explain how participants experienced an intervention, which mechanisms supported change, and where local barriers limited engagement. AI qualitative analysis can help public health, UX, and policy research teams organize those complex interview findings while preserving the evidence behind each theme. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and research documents. The November 5, 2026 PLOS One study of Enjoy Life LocallY, or ELLY, provides a useful example: researchers interviewed 20 participants from two disadvantaged Scottish communities after a 12-week intervention delivered to 75 residents. The study used framework analysis to identify pathways involving social connection, behavior change, diet, wellbeing, and new skills. This article explains what the ELLY findings show, how researchers can use AI-assisted thematic analysis responsibly, and how Evidano can accelerate synthesis without replacing methodological judgment.
In this article
- Key Takeaways
- How AI Qualitative Analysis Clarifies the ELLY Findings
- ELLY Findings Snapshot for Health Researchers
- Implications of AI Qualitative Analysis for Public Health Researchers
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