From Stories to Insight: AI Qualitative Analysis in Healthcare

According to the November 2, 2026 study, Owning the narrative: Exploring the impact of a creative storytelling intervention on stigma and empowerment among persons affected by leprosy in Pakistan, participatory storytelling was associated with less self-stigma, greater confidence, and stronger individual empowerment, although wider community stigma remained.

The study shows why AI qualitative analysis in healthcare must connect recurring themes with participant context, gender, disability, and structural barriers rather than treating frequency alone as evidence of impact.

• The PLOS study used qualitative data from 17 participants, including 16 people who took part in photovoice and 14 who completed in-depth interviews, with 13 participants taking part in both methods.
• The intervention pilot took place in Karachi during 2024, while recruitment occurred from May 30 to June 20, 2025, and interviews were conducted from June 2 to June 25, 2025.
• The researchers reported a shift from shame and concealment toward self-acceptance, openness, confidence, and pride, with the strongest effects at the individual level.
• The study found that gender norms, visible disability, community misconceptions, travel costs, and economic constraints limited broader social change.
• The study concluded that sustained community contact, peer leadership, inclusive outreach, and socioeconomic opportunities are needed to extend storytelling benefits beyond organizational settings.

According to the 2026 PLOS study, participatory storytelling helped participants move from shame and concealment toward greater self-acceptance, confidence, and openness. The intervention combined storytelling, peer support, health education, skills-building, contact-based activities, and photovoice through the Marie Adelaide Leprosy Centre in Karachi.

The researchers analyzed interviews and photovoice materials thematically using the Social Ecological Model, which considers individual, interpersonal, community, institutional, and structural influences. The analysis was supported by NVivo 14 and combined deductive codes from the research framework with inductive codes that emerged from participant accounts.

The study identified individual empowerment as the clearest area of change. Participants described improved confidence, communication skills, self-respect, and willingness to speak publicly. One participant said, "I was feeling very light in my heart because I have already shared everything, " describing the emotional effect of disclosure and narrative ownership.

Peer relationships were another important theme in the study. A participant explained, "When we were all together, we are doing these activities in a group. It was very good feeling for me that if we are together and unite, so we can do a better work." The researchers interpreted these accounts as evidence of solidarity, belonging, and collective resilience.

The study also documented limits. Community misconceptions and discrimination persisted, particularly for people with visible Grade 2 disability. The authors reported that all four women in the 17-person sample faced gender-related participation constraints, including needing permission or accompaniment from male relatives. The authors therefore cautioned that the findings indicate perceived impact and possible mechanisms, not definitive causal effects.

Healthcare researchers should treat storytelling data as both evidence of personal change and evidence about the systems surrounding participants. The PLOS study showed that individual confidence improved more clearly than community attitudes, so an analysis that reports only positive participant sentiment could overstate programme impact.

AI qualitative analysis can help researchers compare themes across levels of the Social Ecological Model. A research team could examine whether references to shame, confidence, disclosure, peer support, family acceptance, gender restrictions, visible disability, or economic barriers occur mainly in individual interviews, group discussions, or community accounts.

The PLOS study also demonstrates why cross-segment analysis matters. The researchers reported different experiences by gender, disability status, employment context, and social setting. Analysts should therefore compare themes across these segments while avoiding unsupported claims about groups with very small sample sizes.

Qualitative analysts should preserve quotations and surrounding context when summarizing sensitive health research. The PLOS authors used participant language such as "We are different from others, not less" to show how narrative work can support dignity, but such quotations should remain connected to the participant’s circumstances and the study’s limitations.

Ethics remain central in healthcare research. The PLOS study obtained informed consent, anonymized data, used translators, offered debriefing, and made counselling support available when needed. AI tools should support secure, controlled analysis and should not be used to make clinical diagnoses or reduce participants to automated labels.

The November 2, 2026 PLOS study shows that participatory storytelling can support self-acceptance, confidence, peer solidarity, and individual empowerment among people affected by leprosy, while also demonstrating that personal change does not automatically produce community-level change.

For healthcare researchers, AI qualitative analysis offers a practical way to examine interviews, photovoice discussions, and open-ended responses across themes and participant segments. The strongest workflow combines AI-assisted organization with human-led interpretation, ethical safeguards, and transparent reporting of sample limitations.

Researchers analyzing sensitive healthcare narratives can use Evidano to structure documents, compare qualitative themes, and ask focused questions across a secure analysis workspace. Try Evidano for free to begin analyzing qualitative research data with AI support.