Qualitative analysis of EIR perceptions

Core answer: The PLOS assessment (published 2 July 2026) of an EIR pilot in Rajshahi (data collected July–September 2024, n=321) shows strong acceptability among caregivers and health-care providers, but recurring operational barriers such as intermittent internet and device limitations that risk blocking scale.

Brief summary: The study combined Likert items and short open-text responses, producing measurable satisfaction signals (caregivers and HCPs) plus qualitative reports of infrastructure and workflow pain points that program teams should prioritize.

• Primary evidence source: PLOS One, published 2 July 2026, data collection July–September 2024.
• Caregiver satisfaction reported: satisfied 59–78%, highly satisfied 16–22%; HCP highly satisfied 75–88% (provider sample n=16).
• Main operational barriers reported: intermittent internet and device limitations, flagged in open-ended responses.
• Practical path: AI-enabled qualitative methods (thematic extraction, cross-segment analysis, prioritized fixes) convert open-ended feedback into prioritized, testable recommendations.

Fast take answer: The PLOS study (published 2 July 2026) surveyed 305 caregivers and 16 health-care providers about an EIR pilot introduced in Rajshahi in 2019 and found high satisfaction alongside recurring operational problems such as intermittent internet and device limits.

Study snapshot: Data collection for this assessment occurred July–September 2024, and caregivers reported high satisfaction with EIR-supported services while HCPs reported strong utility for record management but recurring operational issues.

• Primary evidence source: PLOS One (PLOS One, 2 July 2026).
• Why it matters: Perception data identify practical blockers to scaling EIRs (infrastructure, offline workflows, training), exactly the inputs program planners need to prioritize.
• Payoff: AI-enabled qualitative methods convert short open-ended responses into prioritized fixes and measurable recommendations.

This section answers what the study measured: The study measured perceptions of feasibility and acceptability among direct users (health-care providers) and service recipients (caregivers) using a cross-sectional, descriptive design.

Design and scope: The study used a cross-sectional, descriptive assessment of perceptions, not an effectiveness trial, combining quantitative Likert items (5-point) with open-ended questions analyzed descriptively.

• Sampling: purposive recruitment of caregivers attending EPI sessions; the HCP sample was small (n=16), which limits generalizability.
• Outcomes measured: satisfaction, perceived usefulness of SMS reminders, preferences for EIR versus paper, and reported operational challenges.
• Limitations to note: self-report bias, lack of a comparison group, single urban setting (RCC), and open-ended responses withheld publicly due to identifiability concerns.

This checklist shows the steps to convert the study's approach into an actionable pilot evaluation using AI-enabled qualitative methods.

Step-by-step plan to convert the study's approach into an actionable pilot evaluation:

• 1) Import instruments and CSV survey exports into Evidano, and include metadata (role, date, ward).
• 2) Run automated transcription and cleanup for any audio, using a custom dictionary for Bengali vaccine terms when needed.
• 3) Generate an initial thematic model (auto-suggest codes), then review and finalize the codebook with stakeholders.
• 4) Produce cross-segment frequency tables (caregiver versus HCP; facility versus outreach) and co-occurrence networks.
• 5) Export an executive brief that maps the top three operational fixes to estimated implementation effort and impact.
• Deliverable in 10–14 days: coded dataset, visualization pack, and prioritized recommendations ready for microplanning.

Direct answer: The PLOS study appropriately anonymized quantitative data and restricted open-ended text to protect confidentiality, and re-analysis should preserve those constraints.

Guidance: When re-analyzing qualitative health data, preserve consent constraints, apply PII redaction, and treat outputs as research-only (non-diagnostic).

Platform note: Evidano supports PII redaction and encrypted storage to align with these requirements.

Bottom line: The July 2, 2026 PLOS assessment of the Rajshahi EIR (n=321) shows strong acceptability but clear operational barriers that can be translated into prioritized, testable fixes.

• Ready to convert open-ended responses into prioritized rollout decisions? Start a secure project and demo the workflows cited above on Evidano.
• If you plan a scale-up, measure and monitor registration timeliness, device sync failures, and SMS delivery success as your core rollout KPIs.

Ethics reminder: Use anonymization and respect participant consent when reusing qualitative data.

Strong CTA: Try Evidano for free to book a walkthrough of an EIR-focused analysis pipeline and see how thematic, frequency, and cross-segment analyses cut synthesis time.