EIR Qualitative Analysis: Insights from Bangladesh

The PLOS One pilot shows broadly positive user perceptions but also clear operational constraints, and it is a feasibility snapshot not a scalability evaluation.

Re-analyzing open-ended responses with privacy-preserving AI can surface actionable themes (connectivity, device reliability, training) to prioritize fixes. The study period was July–September 2024 and the article was published 2 July 2026.

• Sample and main numbers: n=321 (305 caregivers; 16 HCPs); facility-based registration 87%; caregiver satisfaction 59–78% satisfied (16–22% highly satisfied); HCP high satisfaction 75–88%.
• Design note: cross-sectional descriptive survey, open responses reviewed but not published due to identifiability; treat findings as feasibility/acceptability signals rather than scalability proof.
• Practical priority areas: offline-capable UI and lightweight sync, device maintenance workflows, connectivity investments, and targeted refresher training.

The study found that caregivers and health workers in an urban EIR pilot reported broadly positive perceptions alongside operational constraints. Caregiver satisfaction ranged 59–78% satisfied with 16–22% highly satisfied across items, health-care-provider high satisfaction ranged 75–88% across items, and electronic registration occurred mostly at EPI centers (87%). Operational constraints centered on intermittent internet and device limitations, and the authors frame the work as a feasibility/acceptability snapshot not an effectiveness or scalability evaluation (study period July–September 2024; published 2 July 2026). See the original article at PLOS One.

• Study design: cross-sectional descriptive survey (n=321; 305 caregivers, 16 HCPs).
• Key numbers: 87% facility-based registration; reported registration timeliness ≈88% within 45 days (mentioned in discussion).
• Qualitative hint: open-ended responses (not publicly released) referenced connectivity and device issues alongside perceived convenience of reminders and better record access.

The qualitative component used a pretested semi-structured questionnaire with closed Likert items and open-ended questions, and open responses were reviewed but withheld from publication for identifiability. Quantitative items were summarized with descriptive statistics (SPSS). Open responses informed the narrative on operational challenges but were not published because authors anonymized data to protect respondents.

• Sampling: purposive recruitment of caregivers attending EPI sessions, useful for experience-rich responses but can overestimate satisfaction.
• HCP data: small n (16) so qualitative quotes from providers could be high-leverage but are few.
• Ethics: data anonymized; open responses not publicly shared for confidentiality. Non-diagnostic; research-focused.

This two-week workflow lays out reproducible qualitative analysis steps for an EIR pilot: Week 1 covers ingest and preparation, and Week 2 covers coding, synthesis, and reporting.

• Day 1: Import transcripts, survey open responses, and metadata (site, role, date) and enable PII redaction and custom dictionary entries (local vaccine names, EPI terms).
• Day 2–3: Auto-transcribe audio (if present) and run translation review, tagging low-confidence segments for human quality control.

Week 2: Code, synthesize, report

• Day 4–6: Run AI-assisted thematic coding and accept or adjust the suggested codebook, using hierarchical codes to separate operational barriers into 'connectivity', 'device', and 'training'.
• Day 7–9: Run cross-segment frequency and co-occurrence analyses (caregiver vs. HCP; facility vs. outreach), and export top quotes per theme with provenance for stakeholder slides.
• Day 10: Generate a one-page executive brief and a slide-ready visualization pack for implementation leads with clear recommended actions (e.g., offline sync rollout, targeted refresher training).

The immediate next steps are to treat open-ended data as the explanatory signal for quantitative metrics and to use AI to accelerate coding while preserving privacy and reproducibility. Step 1: Re-import your open responses and enable PII redaction. Step 2: Run thematic and cross-segment analysis to prioritize fixes such as offline mode, device maintenance, and refresher training. Step 3: Share an executive brief with visuals and representative quotes for decision-makers.

• Step 1: Re-import your open responses and enable PII redaction.
• Step 2: Run thematic plus cross-segment analysis to prioritize fixes (offline mode, device maintenance, refresher training).
• Step 3: Share an executive brief with visuals and representative quotes for decision-makers.

Ready to try this on your own EIR dataset? Try Evidano for free.