Faster Insights: Qualitative Analysis of Post‑COVID Care

Evidano is an AI-powered qualitative data analysis platform that accelerates transcription, thematic coding, and integration across transcripts, surveys, and claims.

This post explains how the LCovB mixed-methods study (registered Aug 2025, protocol published 9 July 2026) maps post-COVID care trajectories from 44 SHIs and how researchers can reproduce its convergent analysis to produce guideline-ready recommendations by Nov 2026.

• LCovB combines claims from 44 statutory health insurers covering 7.8 million insured, expecting ~13, 500 PCS cases for cohort and cost analyses.
• LCovB integrates a hybrid survey (2, 854 contacted; target ≥200 responses), approximately 15 semi-structured interviews, and approximately 15 home assessments, triangulated via a Delphi panel.
• A reproducible 7-step pilot pipeline (2–4 weeks) can validate linkages: transcribe audio, map identifiers, auto-code, run joint displays, and prepare expert-ready outputs using Evidano.

Fast summary: LCovB is a convergent mixed-methods study registered Aug 2025 that maps care trajectories and gaps for post-COVID syndrome and aims to produce guideline-ready recommendations by Nov 2026.

• Primary source: PLoS One (Published 9 July 2026).
• Why qualitative analysis matters: interviews (n≈15) supply contextual barriers, coping strategies and patient-reported gaps that claims data alone cannot show.
• Evidano fit: use AI-assisted transcription, thematic coding, and cross-segment analysis to integrate transcripts, surveys and claims-derived variables (Evidano).

Plain-English summary: LCovB runs four parallel modules that together map prevalence, patient-reported outcomes and clinical validation to produce care recommendations.

• Module 1: complete claims extraction (01.01.2021–31.12.2023 / 2024 depending on submodule), focus on ICD codes U07.x and U09.9.
• Module 2: hybrid survey including validated scales (SF-36, PHQ, FUNCAP) and project-specific questions.
• Module 3: ~15 recorded and transcribed interviews analyzed with Kuckartz qualitative content analysis.
• Module 4: physician-led home assessments with monthly monitoring for 6 months.

Implication statement: Researchers, UX teams and policy stakeholders should prioritize actionable themes linked to care pathways and use qualitative excerpts to illustrate cost impacts.

• Prioritize themes that link to actionable pathways (e.g., 'GP first contact but no referral' → design referral triggers).
• Use qualitative excerpts to illustrate cost-impact findings in briefings for payers and policymakers.
• Segment analysis (age, gender, severity, region) uncovers where rehab or specialized clinics are most needed.

This checklist gives a 7-step run-book you can apply in 2–4 weeks for a small pilot (n≈200 survey + 10 interviews).

• 1) Collect and catalogue inputs (claims extract metadata, survey CSV, raw audio).
• 2) Transcribe audio with a custom clinical dictionary; run QA.
• 3) Import all sources into Evidano and map shared identifiers/segments.
• 4) Draft an initial codebook from literature + 2 pilot interviews; use AI-assisted auto-coding.
• 5) Run thematic frequency and cross-segment analyses; generate joint displays.
• 6) Prepare 10–15 top quotes and visualizations for an expert workshop.
• 7) Iterate codes based on expert feedback and finalize recommendations.

Wrapping up: Prioritize a reproducible pipeline that links transcripts, surveys and claims into joint displays for decision-makers when synthesizing mixed-methods post-COVID data like LCovB.

• Try a pilot: ingest one claims extract + 10 interviews + survey CSV to validate linkages and produce an expert brief.
• Get started with Evidano to accelerate transcription, thematic coding and cross-segment synthesis, or contact us to map this workflow to your data.
• Try Evidano for free to run a 2-week pilot and deliver a joint display and policy brief that mirrors LCovB’s integration approach.