AI for qualitative analysis of health literacy

For qualitative researchers, this study is a model for instrument adaptation: integrate cognitive interviews with frontline workers and patients before large-scale validation to preserve conceptual fidelity while improving comprehension for low-literacy groups.

Use mixed evidence: thematic findings should inform item wording and response options before psychometric testing, not after.

For TB program managers and policy teams, HELIA‑TB identifies patients at risk of non-adherence with a large effect size, so embedding a validated tool into routine monitoring can prioritize education, adherence support, and resource allocation.

Because the instrument performed across literacy strata, the instrument supports equity-focused screening in community programs.

For UX and product teams, map themes from cognitive interviews to microcopy and decision flows, and use tested term swaps (for example, 'physician' to 'doctor') as practical UX fixes validated by patients.

The study’s adaptation steps are reproducible UX actions that improve comprehension for low-literacy users.

Ingest and centralize all materials by uploading transcripts, PDFs, and spreadsheets into Evidano, which maintains one searchable corpus with access controls and encrypted storage.

Problem: interviews, pilot notes, field forms and scoring sheets are scattered. Evidence: centralization reduces manual file handling and supports reproducibility.

Translate and transcribe multilingual interviews using automated tools with custom dictionaries to preserve local terms such as ASHA and DOT, and apply PII redaction for ethics compliance.

Problem: multilingual interviews (Gujarati) and inconsistent terminology slow analysis. Evidano provides automated transcription and translation with custom dictionaries and PII redaction to speed coding and anonymize responses.

Link qualitative item changes to psychometrics by generating exportable item-level responses and codebooks that map to HELIA domains so teams can run CFA-ready analyses.

Problem: linking qualitative item changes to psychometrics is manual and error-prone. Evidano provides AI-assisted thematic coding, hierarchical code trees, co-occurrence networks, and exportable codebooks you can map to instrument items.

Compare literacy scores across adherence groups and literacy strata with cross-segment analysis that outputs frequency tables and effect-size metrics for reporting.

Problem: it is hard to compare literacy scores across adherence groups and literacy strata. Evidano supports cross-segment analyses (for example, adherent vs. non-adherent; literate vs. no formal education).

Produce short, actionable briefs with one-click visualizations and AI chat over your documents to generate executive summaries and policy briefs.

Problem: field teams need short, actionable briefs. Evidano provides one-click visualizations (word clouds, co-occurrence graphs, hierarchical themes) and AI chat to draft stakeholder deliverables.

Maintain secure and auditable workflows with end-to-end encryption, access logs, and a policy that customer data are not used to train third-party models to meet research ethics expectations.

Problem: sensitive health data and ethics approvals require secure handling. Evidano offers encryption, access logs, and explicit nonuse of customer data for third-party model training.

Adapt when a validated tool exists and requires contextual tweaks, because adaptation saves time and preserves comparability.

The HELIA to HELIA‑TB adaptation is an example where contextualizing items for TB preserved the original constructs while improving relevance.

Combine cognitive interviews with frontline workers and patients, simplify wording, pilot timing, and track comprehension flags in transcripts to ensure understanding.

The HELIA‑TB process included cognitive interviews, term swaps, and pilot testing to improve comprehension for low-literacy populations.

Use platforms with end-to-end encryption, access logs, and clear data usage policies to protect sensitive data.

The HELIA‑TB workflow expectations align with platforms that support PII redaction and explicit nonuse of customer data for third-party model training.