Qualitative analysis of Achilles tendon rupture (NHS)

Clinical researchers should use targeted qualitative analysis to link patient narratives to measurable outcomes. The PLOS dataset shows fear is a recurring code, so quantify co-occurrence with pain and activity avoidance to prioritise interventions.

Triangulate patient themes with objective rehab metrics such as heel-rise deficits and re-rupture proxies to test whether psychological support improves functional return.

Physiotherapists and orthopaedics should standardise boot adjustment guidance and offer early touchpoints because patients reported feeling abandoned between fitting and the 8-week review. The study reports patient demand for written and video guidance and for visual reassurance after non-surgical care.

Consider brief imaging or progress checks where operationally possible, as many participants wanted visual reassurance following non-surgical treatment.

UX and service teams should map patient journey pain points and prioritise fixes that reduce cognitive load at the first visit. The pathway pain points include ED wait, incorrect immobilisation, and self-adjustment anxiety.

Measure downstream effects such as weight gain and secondary musculoskeletal complaints as service KPIs to capture the broader impact of initial pathway failures.

Evidano is an AI-powered qualitative data analysis platform that ingests audio, transcripts, PDFs and spreadsheets, auto-organises them into a single corpus, and accelerates reflexive thematic workflows. Those capabilities let teams compare interviews by age, injury mechanism, or time since injury without file-scatter overhead.

To address scattered files and inconsistent coding, Evidano ingests audio, transcripts, PDFs and spreadsheets and auto-organises them into a single corpus so you can compare interviews by age, mechanism (sporting vs non-sporting), or time since injury.

To extract themes at scale, Evidano offers AI-assisted thematic analysis that mirrors reflexive workflows, auto-suggested codes, hierarchical codebooks, and frequency counts that highlight how often 'fear', 'boot discomfort' or 'hygiene' appear and in which subgroups.

To surface differences in understanding by patient segment, Evidano runs cross-segment analysis that shows which clinician phrases align with fear or confidence using co-occurrence networks. This helps clarify whether inconsistent messaging maps to worse patient-reported outcomes.

To collect structured follow-up narratives, Evidano supports AI avatar interviews with consent workflows, then compares longitudinal themes automatically to fill missing subgroup data.

Evidano encrypts data end-to-end and uses proprietary large language models tuned for qualitative research; customer data is never used to train third-party models, which is critical when handling clinical interviews.

To compare fear across subgroups, tag narratives by subgroup such as age and sport history and then run cross-segment frequency and statistical comparisons to identify whether fear codes are more common in a subgroup. Export representative quotes to illustrate the quantitative signal.

AI-assisted themes should be treated as research aids that surface patterns for human review, Evidano is designed to augment, not replace, researcher judgment. For clinical pathway changes, combine qualitative signals with objective measures before altering care.

To protect sensitive clinical data, use PII redaction and encrypted storage plus governance workflows for ethical approvals and restricted exports; these controls are essential for interview datasets like the PLOS study.