Faster Insights: qualitative analysis of lumpy skin disease

Evidano is an AI-powered qualitative data analysis platform that turns KAP surveys, interviews, and clinic notes into reproducible, stakeholder-ready evidence quickly. The PLOS KAP study (n=398) shows strong reliance on clinical signs, widespread diagnostic confusion, and notable antibiotic use, all of which qualitative analysis can unpack into actionable policy recommendations.

Use thematic coding and cross-segment comparisons to link vets’ diagnostic language to antibiotic prescribing rationales and target AMR stewardship and lab-confirmation campaigns.

• n=398 veterinarians were surveyed across 157 upazilas (data collected 28 July 2022–30 June 2023).
• About 92% of respondents identify LSD by characteristic skin nodules, 69% treat on clinical grounds, and 73.62% reported diagnostic confusion with similar conditions.
• 62.06% of respondents perceived antibiotic use as an AMR risk, and 75.28% linked LSD to the hot-humid season.

The PLOS paper was a cluster-randomized cross-sectional KAP survey of early-career veterinarians across 157 upazilas in Bangladesh, with data collected 28 July 2022–30 June 2023. The questionnaire covered demographics, clinical recognition, differential diagnoses, treatment choices, and attitudes toward antimicrobial use, and the analysis used descriptive statistics and χ2/Fisher tests in R.

• n=398 respondents; the majority were aged 21–29 and recent graduates (65.82%).
• Diagnostic reliance: 92% identify LSD by nodules; 69% treat on clinical grounds though 73.62% report confusion with cowpox, papillomatosis, and similar conditions.
• Treatment heterogeneity: 60.05% prescribe antibiotics (often combined with antihistamines/NSAIDs); only 43.22% thought antibiotics were used rationally.
• Study limits: self-report bias, a skew toward fresh graduates, and no PCR-confirmed cases in the survey.

This two-week workflow reproduces and extends the PLOS study into policy-ready insights.

• Day 0–2: Collect and upload materials, survey spreadsheet, audio interviews, and clinic notes. Set custom dictionary entries for local disease names and drug brands.
• Day 3–4: Auto-transcribe and clean transcripts; run initial thematic extraction to surface top mentions such as 'nodule', 'cowpox', and 'antibiotic'.
• Day 5–7: Import the PLOS clinical-sign scoring model as a hierarchical codebook; run batch AI-assisted coding and validate 20% of documents manually.
• Day 8–10: Run cross-segment analyses (region, years of experience, telemedicine vs field). Generate co-occurrence networks to identify common diagnostic confusions.
• Day 11–13: Draft a stakeholder brief with top 10 quotes, frequency tables, and targeted recommendations such as a lab-confirmation drive and AMR stewardship training.
• Day 14: Export a reproducible report and deliver interactive visualizations for decision-makers.

AI-enabled qualitative analysis converts perception data from KAP interviews and clinic logs into policy-ready insights quickly. Start by uploading a small pilot (10–30 transcripts plus your survey) to validate codes and extract the top stewardship and diagnostic confusion themes. For a guided trial that maps PLOS-style KAP outputs to stakeholder-ready briefs, Try Evidano for free.

• Quick win: map where antibiotics are being justified, and produce a short training brief targeting those reasons.
• Policy win: use cross-segment comparisons to target lab-confirmation campaigns during hot-humid months (study: 75.28% linked seasonality).
• Operational win: standardize the clinical sign scoring model in Evidano to harmonize diagnosis language across districts.