The SOUND-BITES pilot (published 14 July 2026) embeds hearing checks into Meals on Wheels deliveries and will collect surveys and optional interviews from a planned n≈600 clients and 60 volunteers. Researchers running mixed-methods pilots need fast, reproducible thematic synthesis: that is where AI-enabled qualitative analysis helps. This post shows researchers and UX teams how to convert SOUND-BITES–style transcripts, otoscopy notes and pre/post surveys into rigorous themes, segment comparisons, and stakeholder-ready visualizations using Evidano. You will get a short workflow, precise time-savers, and a security note for handling sensitive transcripts in research-only contexts. For platform details see www.evidano.com.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that ingests transcripts, surveys, and clinical notes to automate coding, synthesis, and stakeholder-ready outputs.
This post explains how the SOUND-BITES pilot dataset (clinical outputs, surveys, and interviews) can be converted into rapid, evidence-ready themes and visual exports using automated transcription, deductive coding to the Theoretical Domains Framework, and mixed-methods synthesis.
- The SOUND-BITES protocol was published 14 July 2026 and targets about 60 volunteers and approximately 600 clients/household members across 8 Meals on Wheels NSW sites.
- Saturation for the qualitative component is expected at approximately 9–17 interviews, and the planned coding approach uses two coders with consensus meetings plus a third coder for arbitration.
- A compact two-week workflow moves from raw audio to preliminary themes in 10 business days, including transcription, PII redaction, deductive coding, and export of stakeholder briefs.
Fast take: why this study matters for qualitative teams
This pilot protocol measures feasibility and acceptability of home-based otoscopy, tablet hearing screens, and optional audio interviews across eight Sydney Meals on Wheels sites.
The SOUND-BITES pilot protocol proposes home-based otoscopy, tablet hearing screens and optional audio interviews to evaluate acceptability and feasibility across eight Sydney sites, full protocol: PLOS One.
- Primary payoffs for analysts: mixed-methods dataset (clinical outputs + surveys + ~9–17 interviews for saturation) and a pre-specified coding frame (Theoretical Domains Framework).
- If you manage transcript workflows, this is a classic use case for AI-assisted transcription, coding, and cross-segment synthesis.
Study snapshot
| Date / Metric | Value | Note / Implication |
|---|---|---|
| Protocol published | 14 July 2026 | PLOS One, full methods and timelines |
| Recruitment started | 01/03/2026 | Audiology student recruitment underway |
| Target sample | 60 volunteers; 600 clients/household members | Feasible pilot sized for acceptability + preliminary outcomes |
| Sites | 8 Meals on Wheels NSW locations | Metropolitan Sydney, diverse community contexts |
| Data timeline | Data collection → 30/04/2027; preliminary results → 01/09/2027 | Plan allows staged analysis and early synthesis |
What happened (methods in brief), why qualitative matters
The SOUND-BITES methods combine clinical screening, baseline and 6-month surveys, and optional audio interviews that are transcribed and coded to the Theoretical Domains Framework in NVivo.
SOUND-BITES pairs clinical screening (Arclight otoscopy, Sound Scouts app) with baseline and 6-month surveys and optional audio interviews. Interviews are transcribed, checked, participant-reviewed, and then coded deductively to the Theoretical Domains Framework (TDF) in NVivo.
- Qualitative endpoints: acceptability and feasibility of the program, volunteer experience, client reflections on education and help-seeking.
- Planned coding: two coders, coding guideline, consensus meetings, third coder arbitration; saturation expected ~9–17 interviews.
- Quantitative measures (HHIE-S, SSQ-12, EQ-5D-5L) enable crosswalks between reported experience and measured handicap, a rich triangulation opportunity.
So what for researchers & UX teams: translate mixed methods into decisions
For qualitative researchers
Qualitative researchers can use the deductive TDF setup while iterating the codebook and measuring inter-coder reliability.
SOUND-BITES is set up for deductive coding (TDF) but will surface emergent themes, plan for iterative codebook updates and inter-coder reliability metrics.
Key analytics to run: theme frequency by site, co-occurrence networks for barriers vs. enablers, and timeline narrative of help-seeking after referral.
For UX / service designers
UX and service designers can compare client-reported usability across devices and household contexts to prioritize fixes and training.
Compare client-reported usability of Sound Scouts and Arclight across household contexts to prioritize training or UI tweaks.
Use sentiment and quote frequency to build stakeholder-facing design recommendations (e.g., simplify instructions where miscomprehension clusters).
For policy & health analysts
Health policy analysts can feed acceptability and help-seeking rates into cost-utility models using EQ-5D-5L data.
Acceptability and help-seeking rates can feed cost-benefit models (EQ-5D-5L will be used for cost-utility).
Qual findings identify operational barriers (student availability, volunteer turnover) that affect scale-up assumptions.
Do more, faster with Evidano (mapped to the SOUND-BITES use case)
Ingest and clean, transcripts, surveys, clinical notes
Evidano ingests audio files, cleaned Word transcripts, REDCap CSVs and Arclight/Sound Scouts outputs to create an analysis-ready corpus.
Import audio files, cleaned Word transcripts, REDCap CSVs and Arclight/Sound Scouts outputs into Evidano.
Use PII redaction during transcription and a custom dictionary (device and clinical terms) to keep transcripts analysis-ready.
Automated thematic + frequency analysis
Evidano automates deductive coding to a TDF codebook and produces theme counts, representative quotes, and inter-coder alignment reports quickly.
Run deductive coding to a TDF codebook (uploadable) and let Evidano produce theme counts, representative quotes, and inter-coder alignment reports in minutes.
Quickly surface which TDF domains dominate by site or participant subgroup (e.g., clients vs volunteers).
Cross-segment and mixed-methods synthesis
Evidano merges survey variables with qualitative themes to run cross-segment comparisons and flag patterns across sites and subgroups.
Merge survey variables (HHIE-S, SSQ-12, help-seeking yes/no) with qualitative themes to run cross-segment comparisons and flag patterns (e.g., low help-seeking + high stigma themes).
Generate visualizations: co-occurrence networks, word clouds, hierarchical code→subcode trees for inclusion in reports and presentations.
Stakeholder-ready outputs & iterative QA
Evidano produces clickable quote packs, slide-ready exports, and an audit trail from transcript to theme for ethics and reproducibility.
Produce clickable quote packs, slide-ready export, and an audit trail showing transcript→theme mapping for ethics and reproducibility.
Use the AI chat over your corpus to ask ad-hoc questions like “show me volunteer quotes about training challenges” and export results.
Security & governance
Evidano encrypts data, does not use project data to train third-party models, and supports research-only workflows with consent management.
Data is encrypted and never used to train third-party models, suitable for research-sensitive clinical projects.
Non-diagnostic research note: Evidano supports research-only workflows and consent management; clinical follow-up decisions remain with qualified clinicians.
Two-week workflow (practical checklist)
This two-week workflow condenses a run-book to produce preliminary themes from raw audio in 10 business days.
A compact run-book to go from raw audio to preliminary themes in 10 business days:
- Day 1–2: Import audio and surveys into Evidano; set project structure and upload TDF codebook.
- Day 3–5: Auto-transcribe with custom dictionary + PII redaction; reviewer checks and participant verbatim confirmations.
- Day 6–7: Run deductive coding, generate theme frequencies and co-occurrence maps.
- Day 8–9: Merge HHIE-S and help-seeking survey columns, run cross-segment contrasts (site, volunteer vs client).
- Day 10: Export stakeholder brief and representative quote pack for nutrition/Meals on Wheels and audiology partners.
FAQ: AI qualitative analysis
What is the SOUND-BITES pilot?
The SOUND-BITES pilot embeds hearing checks into Meals on Wheels deliveries and collects surveys and optional audio interviews from clients and volunteers.
The SOUND-BITES pilot (published 14 July 2026) embeds hearing checks into Meals on Wheels deliveries and will collect surveys and optional interviews from a planned n≈600 clients and 60 volunteers across eight Sydney sites.
How does Evidano handle transcript PII and data governance?
Evidano supports PII redaction during transcription, encrypts data, and does not use project data to train third-party models, supporting research-only workflows.
Use PII redaction during transcription and a custom dictionary to keep transcripts analysis-ready; Evidano also provides an audit trail and consent management for research projects.
How many interviews are needed for saturation and what coding approach is planned?
Saturation is expected at approximately 9–17 interviews and the planned approach uses deductive coding to the Theoretical Domains Framework with two coders and arbitration by a third coder as needed.
Planned coding: two coders, coding guideline, consensus meetings, third coder arbitration; saturation expected ~9–17 interviews.
What outputs and timings can teams expect from this workflow?
Teams can expect theme frequencies, representative quotes, inter-coder alignment reports, co-occurrence visualizations, and stakeholder-ready slide exports, achievable in a 10 business day run-book.
A compact two-week workflow moves from raw audio to preliminary themes in 10 business days, including export of stakeholder briefs and quote packs.
Wrapping up & next steps
The SOUND-BITES pilot shows how AI-enabled qualitative analysis reduces manual bottlenecks and increases reproducibility for researchers, UX teams and program leads.
SOUND-BITES is a textbook case where AI-enabled qualitative analysis reduces manual bottlenecks and increases reproducibility: structured codebooks, rapid cross-segment queries, and visual exports accelerate decisions for researchers, UX teams and program leads.
Ready to pilot this workflow on your transcripts, surveys and clinic notes? See how Evidano handles mixed-methods pilots: Try Evidano for free.
Ethics reminder: these approaches support research-only inference. Clinical and diagnostic decisions should remain with licensed providers.