Qualitative analysis of community hearing screening

Evidano is an AI-powered qualitative data analysis platform that accelerates transcription, framework coding, and stakeholder-ready reporting for mixed-methods pilots like SOUND‑BITES.

The SOUND‑BITES pilot (published 14 July 2026) pairs audiology students with Meals on Wheels NSW volunteers to deliver home-based otoscopy and a short tablet hearing screen, with optional audio interviews coded to the Theoretical Domains Framework (TDF).

Applying an AI-enabled workflow can cut weeks from the manual transcription-to-report timeline while preserving auditability and the two-coder reliability workflow required by the protocol.

• SOUND‑BITES plans n=60 volunteers and ~600 clients/household members, with qualitative interviews typically ~4 weeks post-program.
• The protocol specifies deductive TDF coding in NVivo with two coders and a third for arbitration, and targets saturation ranges cited in the protocol.
• Common bottlenecks are manual transcription, transcript checking, inter-coder drift, segment comparisons, and stakeholder-ready reporting.
• Evidano automates verbatim transcription with custom dictionaries and PII redaction, supports AI-assisted framework coding, cross-segment comparisons, and one-click visuals for evidence packs.
• A practical step is to pilot AI-assisted workflows on the first 10 interviews to validate transcripts, coder agreement, and visual outputs.

The Study snapshot lists key protocol items, values, and sources for SOUND‑BITES.

The SOUND‑BITES protocol pairs Master of Clinical Audiology students with Meals on Wheels NSW volunteers to deliver home-based otoscopy and an 8–10 minute Sound Scouts tablet hearing screen.

Visits include education, referrals where needed, and a baseline plus 6-month follow-up survey. Optional interviews capture participant and volunteer perspectives; transcripts are checked manually and coded to the TDF in NVivo. The protocol explicitly aims to evaluate acceptability, feasibility, and preliminary help-seeking outcomes.

• Screening equipment: Arclight otoscope and Sound Scouts app on iPad with Sennheiser HD 400S headphones.
• Interview timing: approximately 4 weeks post program for qualitative semi-structured interviews.
• Qualitative plan: Deductive TDF coding, two coders plus a third for arbitration, with target saturation ranges cited in the protocol.

Qualitative researchers and evaluators should focus on transcript processes because the work done with interview transcripts determines whether findings are actionable for funders and partners.

The protocol uses standard rigor, including double coding, participant transcript checks, and TDF, but the manual steps described in the protocol add weeks of work. Common bottlenecks in the workflow include:

• Turnaround delays caused by manual transcription and transcript checking before coding.
• Inter-coder drift when maintaining consistency across coders using framework coding.
• Difficulty in quickly comparing segments across sites, volunteer roles, or device referral outcomes.
• Reporting burdens: extracting quotes, visualizing co-occurrence, and producing stakeholder-ready summaries.

This 7-step checklist shows how to go from audio to stakeholder report using the protocol methods and AI-enabled steps.

• 1) Collect audio with consent and metadata (site, role, date).
• 2) Upload recordings to Evidano; apply project-level PII redaction and a custom dictionary for domain terms.
• 3) Auto-transcribe; reviewer listens to flagged low-confidence segments and signs off.
• 4) Import codebook (TDF) and run AI-assisted initial coding; assign two human coders to review divergent items.
• 5) Generate thematic summaries, code frequency tables, and cross-segment comparisons by site, volunteer experience, and help-seeking outcome.
• 6) Create visuals (co-occurrence network, hierarchical codes to subcodes) and pull vetted quotes into an evidence pack.
• 7) Export a concise decision memo for Meals on Wheels and funders: acceptability rate, common barriers, recommended SOP changes, and cost-utility inputs linked to EQ‑5D‑5L and help-seeking data.

The SOUND‑BITES pilot has clear qualitative procedures but manual steps that slow time-to-insight, and an AI-enabled research platform can cut weeks from the timeline while preserving auditability.

• Start small: pilot Evidano on the first 10 interviews to validate transcripts, coder agreement, and visual outputs.
• Evidano integrates with REDCap/CSV exports for linking HHIE‑S and other scales to qualitative cases.
• For a walkthrough or demo of how this maps onto SOUND‑BITES methods, see Evidano or request a pilot project to try the 7-step workflow above; alternatively, Try Evidano for free.

Ethics note: Use AI tools only within approved protocols and with participant consent; outputs are for research and program evaluation, not clinical diagnosis.