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From Interviews to Insight: AI Qualitative Data Analysis
Community health researchers often need to turn interviews into credible findings about behaviour, wellbeing, participation, and intervention outcomes. The challenge is not only finding repeated topics, but also tracing how participants describe connections between an intervention and its perceived effects. AI qualitative data analysis can help researchers organize large volumes of interview text, compare themes across communities, and surface evidence for a human-led interpretation. The 2026 ELLY study in Scotland provides a useful example: researchers analysed 20 participant interviews to understand how a 12-week community intervention was perceived to influence healthy weight and wellbeing. This article explains what the study found, how qualitative analysis supported its conclusions, and how AI-assisted workflows can help research teams move from transcripts to transparent thematic insight without replacing methodological judgement.
In this article
- Key Takeaways
- What the ELLY Study Shows About Qualitative Health Research
- ELLY Findings Snapshot for AI Qualitative Data Analysis
- Implications for Researchers Using AI Qualitative Data Analysis
Evidano8 min read Read More