Patient Voices: Qualitative analysis of Achilles rupture

Evidano is an AI-powered qualitative data analysis platform that helps teams turn Achilles tendon rupture interview transcripts into reproducible themes identifying early-care gaps, boot self-management issues, and fear of re-rupture.

• Study snapshot: 15 NHS patients (mean age 54.5, 80% male) interviewed once, mean 8.9 months since injury; published in PLOS One on June 29, 2026.
• Core finding: three themes emerged: entry to care, boot immobilisation experience, and a rehabilitation journey dominated by fear of re-rupture.
• Operational levers: clearer early communication, reliable boot support, and psychological scaffolding to improve adherence and outcomes.

Researchers interviewed 15 NHS patients (mean age 54.5) managed non-surgically for Achilles tendon rupture and used reflexive thematic analysis to surface lived-experience pain points and behavioural adaptations.

• Study published: June 29, 2026 (received Oct 6, 2025; accepted Jun 15, 2026). Source: PLOS One.
• Core finding: three themes, injury & entry to care; non-surgical immobilisation experiences; and the rehabilitation journey dominated by fear of re-rupture.
• Why it matters: early communication, reliable boot support, and psychological scaffolding are clear levers to improve adherence and outcomes.

The study recruited 15 patients from a specialist NHS ATR clinic between Aug–Nov 2023, interviewed each once, transcribed audio verbatim and applied reflexive thematic analysis.

• Method strength: timely interviews (mean 8.9 months post-injury) reduced recall bias relative to long-term retrospective studies.
• Limitations: single-site recruitment and only non-surgical cases; qualitative data not publicly shared for confidentiality.

The following runbook turns ATR interviews into decisions using a reproducible pipeline.

• 1) Import audio and transcripts to Evidano; apply a custom dictionary for clinical terms (for example, Vacoped, LAMP).
• 2) Auto-transcribe and PII-redact where needed.
• 3) Auto-generate initial codes and a frequency table of top terms/phrases.
• 4) Run co-occurrence analysis to tie 'fear' to 'boot removal' or 'return to sport'.
• 5) Manually review and refine themes with clinicians; lock a codebook for reproducibility.
• 6) Produce segment comparisons (age, sport vs non-sport mechanism) and export visuals for MDTs.
• 7) Deploy AI avatar follow-ups to validate findings or test interventions (boot instruction video, imaging reassurance).

The PLOS One qualitative analysis (published Jun 29, 2026) demonstrates that reducing ambiguity in early care, improving boot support, and treating fear as a measurable outcome are practical steps for better ATR rehab.

Ready to operationalise these steps? Run a reproducible thematic pipeline over your transcripts and surveys in minutes with Evidano, ingest, code, compare segments, and produce stakeholder-ready visuals with secure, research-grade controls.

• Next move for research teams: pilot a 2-week Evidano workflow on 20 interviews to map fear → adherence and prioritize a targeted intervention.
• For clinicians and service leads: produce a short report (top 5 quotes + co-occurrence map) for your MDT to redesign boot education and follow-up touchpoints.
• Call to action: Try Evidano for free.