Category: Commentary on News; Subcategory: Commentary on News. Fast payoff: this post shows how researchers and program teams can turn the PLOS One pilot of an Electronic Immunization Registry (EIR) in Rajshahi City (published 2 July 2026) into rigorous, reproducible qualitative findings. The study collected survey items and open-ended responses from 305 caregivers and 16 health workers between July–September 2024; reported facility-based registration at 87% and broadly positive satisfaction (caregiver ‘satisfied’ 59–78%, ‘highly satisfied’ 16–22%; HCPs ‘highly satisfied’ 75–88%). Read the original paper at PLOS One article and use Evidano to map these mixed responses into thematic, frequency, and cross-segment evidence you can present to funders and implementers.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that ingests mixed surveys and open text to produce reproducible thematic, frequency, and cross-segment evidence. This post shows how to convert the July–September 2024 Rajshahi City EIR pilot (n=321) into actionable, secure qualitative deliverables that link perceptions to registry metrics. Major quantitative signals to preserve are facility-based registration at 87% and caregiver satisfaction clustered between 59–78% satisfied and 16–22% highly satisfied.
- Evidano can automate initial coding and PII redaction so teams reproduce thematic and frequency analyses from the Rajshahi pilot in days, not weeks.
- Report low-n provider segments (n=16 HCPs) with caution, supplement with bootstrapped summaries or targeted follow-ups, and flag uncertainty clearly.
- Pair perception themes with objective registry audits (timeliness, registration mode) before making scale-up recommendations; the pilot reported 88% registered within 45 days.
Findings snapshot
| Metric | Value | Source / Note |
|---|---|---|
| Published | 2 July 2026 | PLOS One article |
| Study period | July–September 2024 | Cross-sectional survey |
| Sample (total) | n = 321 (305 caregivers; 16 HCPs) | Caregivers = recipients; HCPs = direct users |
| Primary registration mode | 87% at EPI centers | Facility-based dominant; outreach also used |
| Caregiver satisfaction | 59–78% 'satisfied'; 16–22% 'highly satisfied' | Across assessed service items |
| HCP satisfaction | 75–88% 'highly satisfied' (varies by item) | n = 16; interpret cautiously |
| Timeliness | 88% registered within 45 days | Reported in discussion; aligns with other LMIC pilots |
What the study did (plain English)
This section summarizes the Rajshahi pilot design and data collection methods. The Rajshahi pilot used a mixed survey with 5-point Likert items and open-ended questions to capture perceptions of an EIR introduced in 2019 but assessed in 2024 operations. Data were collected from caregivers attending EPI sessions and HCPs using tablet-based entry and automated SMS reminders.
- Design: Cross-sectional, descriptive (July–Sep 2024); not designed for causal inference.
- Analysis: SPSS v24 for quantitative summaries; open-ended responses were reviewed and summarized descriptively.
- Limitations: purposive sampling of EPI attendees, small HCP sample (n=16), potential social desirability bias, and open-ends withheld publicly for confidentiality.
So what for UX researchers & program teams (implications)
For UX / Service designers
UX and service designers should note that caregiver satisfaction clusters around reminders and service convenience rather than direct interaction with the registry. Actionable insight: caregiver satisfaction clusters around reminders and service convenience rather than direct interaction with the registry, design evaluations should separate service-experience feedback from usability of backend tools.
Recommendation: capture end-user journeys (caregiver vs HCP) as separate transcripts and codebooks so themes do not conflate.
For program & policy analysts
Program and policy analysts should prioritize infrastructure constraints when interpreting positive perception signals. Actionable insight: positive perceptions (high reported satisfaction) support scaling pilots, but infrastructure constraints (intermittent internet, device limits) are key scaling risks.
Recommendation: pair perception data with objective registry audits (timeliness, duplicate IDs) before large-scale investment.
For qualitative researchers
Qualitative researchers should plan for trade-offs between confidentiality and analytic granularity. Actionable insight: open-ended responses exist but are withheld for confidentiality, that is a common trade-off, coded summaries in the paper miss cross-segment nuance.
Recommendation: use AI-assisted thematic analysis to preserve granularity while automating redaction and secure storage.
Do more, faster with Evidano (mapping problems → features)
Problem: Mixed inputs (surveys + open text) → Solution
This subsection maps the mixed-input problem to how Evidano handles it. Evidano ingests spreadsheets and transcripts, auto-detects columns (Likert items, open text) and produces thematic and frequency analyses in minutes so you can quantify themes across segments (ward, caregiver vs HCP).
Problem: Sensitive, non-public open-ends → Solution
This subsection maps confidentiality constraints to platform features. Use Evidano's PII redaction and encrypted storage to analyze participant quotes without exposing identifiers, supporting ethics constraints noted in the paper (IRB: IBMC, R/IRB/2024/04/01).
Problem: Small, uneven provider samples → Solution
This subsection explains how to treat low-n segments analytically. Evidano cross-segment analysis and bootstrapped frequency tables help you report stable patterns (e.g., HCP theme prevalence) while clearly flagging low-n segments.
Problem: Need reproducible deliverables → Solution
This subsection explains reproducible outputs you can produce. Exportable visualizations (co-occurrence networks, hierarchical codes→subcodes, word clouds) and an AI chat over your uploaded documents let stakeholders interrogate findings interactively during briefings.
Security note
This subsection answers the security question directly. Evidano uses end-to-end encryption and does not use customer data to train third-party models, suitable for health program data that require confidentiality.
Checklist: Reproduce this qualitative analysis in 7 steps
This checklist explains how to reproduce the Rajshahi pilot qualitative analysis in seven steps:
- 1) Gather inputs: upload the survey spreadsheet (Likert items + open text) and any de-identified interview transcripts to Evidano.
- 2) Apply PII redaction and (if needed) translation with a custom dictionary for local terms.
- 3) Auto-code: run AI-assisted initial coding to surface candidate themes; review and finalize a codebook.
- 4) Thematic + frequency analysis: generate theme prevalence by segment (caregiver vs HCP; ward; registration mode).
- 5) Cross-segment analysis: produce difference-in-proportion tables and co-occurrence networks to show which themes cluster with complaints (connectivity issues, device limits).
- 6) Visualize & export: create word clouds and hierarchical code maps for presentations and dashboards.
- 7) Produce reproducible deliverables: export results, notes, and an interactive Q&A of the corpus for stakeholders.
FAQ: Qualitative analysis of EIR perceptions
What counts as qualitative analysis of EIR perceptions?
Thematic coding and narrative synthesis of operational barriers constitute qualitative analysis of EIR perceptions. Thematic coding of open-ended caregiver/HCP responses, narrative synthesis of operational barriers, and coding that links perceptions to objective registry metrics (timeliness, registration mode).
How do I compare small groups (e.g., n=16 HCPs) reliably?
Use clear reporting and uncertainty flags when comparing small groups. Report raw counts, use effect-size-aware summaries, and flag low-n segments; supplement with targeted qualitative follow-ups or bootstrapped frequency estimates as implemented in Evidano.
How do I keep quotes usable but confidential?
Redaction and coded metadata keep quotes usable while protecting identity. Redact PII, replace nitty-gritty identifiers with coded tags, and store de-identified excerpts alongside metadata (role, ward) so you preserve analytic value while honoring ethics.
Wrapping up: What to do next
This section recommends next actions for follow-up evaluations and scaling decisions. If you are preparing a follow-up to the Rajshahi pilot (scaling, rural rollout, or longitudinal evaluation), start by converting the pilot’s semi-structured open-ends into a reproducible thematic dataset and linking those themes to registry metrics (timeliness, registration completeness).
Try these next moves: upload your survey and open-text files to Evidano; run an automated codebook draft; use PII redaction and cross-segment analyses to produce a stakeholder-ready brief in days, not weeks. For a hands-on trial, Try Evidano for free.
Ethics note: this guidance is research-focused and non-diagnostic. The original study had IRB approval (IBMC, R/IRB/2024/04/01) and anonymized data handling, replicate that practice when you work with health data.