This introduction summarizes a June 2026 proof-of-concept co-design study that tested a mobile app (RehaLink) unifying conventional and technology-based assessments for neurorehabilitation with 17 inpatients and 15 health care practitioners. Read the original study at rehab.jmir.org/2026/1/e85072. If your team analyzes transcripts, UX feedback, or mixed clinical metrics, Evidano can ingest interview notes, app comments, and assessment exports to produce thematic, frequency, and cross-segment analyses that map directly to actionable interface changes and clinical workflows.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that ingests transcripts and assessment spreadsheets, aligns text and numeric signals to shared codes, and produces thematic and cross-segment analyses and exportable visuals. A June 7, 2026 co-design study with 17 patients and 15 health care practitioners found consistent UI preferences for progress bars, color coding, and combined absolute and normative values.
- Study snapshot: the co-design process had three steps and included prototype usability testing with patient SUS mean 93.6 (SD 6.4) and HCP SUS mean 80.9 (SD 8.1).
- Design tradeoff: participants wanted quick visual signals (progress bars, color codes) plus access to absolute numbers and normative context (VPIT normalized 0-100).
- Operational playbook: a compact two-week sprint can reproduce these insights by combining auto-transcription, AI-assisted coding, and one-click theme-to-metric reports.
Study snapshot
| Item | Value | Source / Note |
|---|---|---|
| Publication date | June 7, 2026 | rehab.jmir.org/2026/1/e85072 |
| Participants | 17 patients (MS), 15 HCPs (26 HCP participations) | Co-design feedback sessions 1–3 |
| Prototype usability (SUS) | Patients mean 93.6 (SD 6.4); HCPs mean 80.9 (SD 8.1) | Session 3 usability test |
| Technology metric reference | VPIT normative database n=120 | Used for 0-100 normalization |
| Key UI preferences | Progress bars, color codes, absolute values + norms | Iterative ranking & Likert ratings |
What happened (in plain English)
The study ran a three-step user-centered co-design process at Kliniken Valens to identify information needs, test representations of VPIT metrics, and validate a tablet app prototype. The authors collected feedback using Likert scales, rankings, and open responses and found consistent preferences for progress bars, color coding, and combined absolute and normative values, with health care practitioners initially favoring simpler visuals and patients wanting more numeric detail.
- Methods: structured feedback sessions (30–45 minutes), mockups → Figma prototype, SUS, directed content analysis.
- Data types: conventional assessments (grouped into six domains) plus ten VPIT metrics grouped into five lay domains and normalized to 0-100.
- Design tradeoff: participants wanted both quick signals and on-demand access to absolute and normative numbers.
Implications for researchers, UX teams, and clinicians
For qualitative researchers
Qualitative researchers should combine quantitative metrics and open feedback to produce richer design decisions. The study shows that directed content analysis on session transcripts can surface tensions, for example health care practitioners underrating patient appetite for numbers, and that preference rankings and pre/post attitude shifts can be captured as quantifiable outcomes.
For UX/Design teams
UX and design teams should prioritize progress bars with on-demand detail as a default pattern. Prototype testing with small iterative samples (approximately 5–9 per round) produced stable preference signals in this study, and including toggles for absolute versus normalized views plus expandable explanatory text reconciles clinician and patient needs.
For clinical teams & implementation leads
Clinical teams should pair visual summaries with interpretable thresholds and printable tables to drive adoption. Structured feedback in the study increased perceived value (patients median 4→5; HCPs 4→4.5), and using MDC/MCID thresholds alongside visuals supports consultations and decision making.
Do more, faster with Evidano
Problem: Fragmented mixed data (transcripts + metrics)
Evidano ingests interview transcripts, open feedback, and assessment spreadsheets and aligns them to shared codes so teams can analyze themes across text and numeric signals without manual triangulation.
Problem: Inconsistent coding & missed cross-segment patterns
Evidano provides AI-assisted thematic coding with hierarchical codes and subcodes, automatic frequency counts, and cross-segment analysis (patients vs HCPs; pre/post exposure) to reveal over- and under-represented themes.
Problem: Need localized terms and clinical accuracy
Evidano supports custom dictionaries for transcription and translation (clinical terms and instruments like VPIT), PII redaction, and encrypted storage, and the platform does not use customer data to train third-party models.
Problem: Slow synthesis for design sprints
Evidano generates one-click reports: theme summaries, exemplar quotes mapped to metrics (for example, 'progress bar preferred' clustered by age or role), and exportable visuals for stakeholder meetings.
Quick workflow: Reproduce these insights in a sprint (2 weeks)
This quick workflow shows how to reproduce the study insights in a two-week sprint using AI-enabled qualitative analysis.
- Day 1: Import transcripts, survey spreadsheets, and assessment CSVs into Evidano; set a custom dictionary (VPIT, MDC/MCID, clinical terms).
- Day 2–3: Auto-transcribe (with PII redaction) and translate non-English responses; run initial topic extraction.
- Day 4–6: Apply or refine a codebook (UX preferences, emotional tone, data needs) using AI-assisted coding; review with a clinician coder.
- Day 7–9: Generate frequency tables, co-occurrence networks, and segment comparisons (patients vs HCPs; age; role).
- Day 10: Produce one-page executive brief and visualization pack (progress bar mockups, normative vs absolute views) for design decisions.
- Day 11–14: Iterate prototype language and UI with stakeholders and re-test a small sample.
FAQ: AI-enabled qualitative analysis
How do you compare segments reliably?
Comparing segments reliably requires normalized counts and clear visualizations, and Evidano computes thematic frequencies, normalizes counts by segment size, and produces cross-tabs and visuals so teams can see which themes are over- or under-represented (for example, patients wanting absolute values versus HCPs).
Can I include clinical metrics like VPIT?
Yes, you can include clinical metrics like VPIT by importing metric CSVs and mapping them to text themes, and Evidano can join metrics to quotes (for example, participants with worsening VPIT scores mentioning 'motivation') to prioritize intervention design.
Is this research or diagnostic?
The outputs are research-focused and non-diagnostic, and teams should always pair tool outputs with clinical judgment and consented data governance.
Wrapping up & next steps
Use AI-enabled qualitative analysis to convert co-designed neurorehab feedback into prioritized product changes and measurable implementation steps. If you study or build patient-facing feedback like RehaLink (study details at rehab.jmir.org/2026/1/e85072), apply a two-week sprint of transcript ingestion, AI-assisted coding, and theme-to-metric reporting to surface tradeoffs and quantify stakeholder divergences.
Start a pilot: Try Evidano for free to upload a sample session and see a thematic report mapped to your metrics.