Evidano is an AI-powered qualitative data analysis platform that ingests de-identified notes and runs thematic and cross-segment analyses. The PLOS One study (published 16 July 2026) used the Nominal Group Technique with 22 stakeholders in Tbilisi to shortlist a disclosure decision aid for people living with HIV in Georgia. This post shows researchers and UX teams how to reproduce stakeholder-driven prioritization with AI-enabled qualitative analysis, from ingesting NGT notes to producing cross-segment theme maps and visuals in Evidano. You will learn which data to capture, which analyses reveal provider versus PLWH trade-offs, and a short workflow to turn field notes into an adaptation-ready brief.
Key Takeaways
Key Takeaways: Two separate NGT sessions (providers n=12; PLWH n=10) plus a joint session prioritized the Family disclosure decision aid for adaptation, with providers favoring digital formats and PLWH favoring session-based support.
Combine Nominal Group Technique ranking with AI-enabled thematic and cross-segment synthesis to accelerate adaptation planning and produce reproducible, funder-ready briefs.
- NGT with n=22 stakeholders in Tbilisi (Feb–Jul 2026) produced ranked formats and interventions, prioritizing the Family disclosure DA by consensus in Jul 2026.
- Providers ranked digital formats (READY) highest, while PLWH ranked session-based Family disclosure DA highest.
- Primary datasets are available in the paper’s Supporting Information, including de-identified NGT notes and ranking forms.
- AI-assisted workflows can map adaptation priorities (peer educators, legal content, confidentiality) and compare providers versus PLWH to surface tensions early.
Fast take + source
Fast take: Two separate NGT sessions (providers n=12; PLWH n=10) plus a joint session identified the Family disclosure decision aid as the priority for adaptation; providers favored digital formats while PLWH prioritized individual/session-based support.
Read the original paper at PLOS One.
- Why it matters: disclosure decisions are relational and safety-sensitive, adaptation must balance acceptability and feasibility.
- What you can do: reproduce the study’s prioritization and synthesize adaptation requests faster with AI-assisted thematic and cross-segment analysis.
Findings snapshot
| Date / Item | Metric | Value | Source / Note |
|---|---|---|---|
| Study timeline | Recruitment | Feb 9–Mar 3, 2026; sessions at Tbilisi AIDS Center | Methods section |
| Sample size | Participants | n=22 (providers 12; PLWH 10) | Results |
| Provider preference | Format ranked #1 | Digital (READY) | Provider NGT |
| PLWH preference | Format ranked #1 | Session-based (Family disclosure DA) | PLWH NGT |
| Final selection | Consensus | Family disclosure DA prioritized for adaptation | Joint session (Jul 2026) |
What happened: methods & key outputs
What happened: The team identified nine candidate decision-support interventions via desk review, grouped them into session-based, paper-based, and digital formats, and ran Nominal Group Technique sessions to shortlist priorities.
The team ran two 60-minute NGT sessions (providers; PLWH) and a 40-minute joint session using idea-generation, round-robin sharing, and independent ranking to produce de-identified discussion notes and ranking scores.
- The NGT outputs produced: (1) ranked formats per stakeholder group, (2) ranked interventions within preferred formats, and (3) adaptation priorities (peer educators, legal content, confidentiality, flexible timing).
- Primary datasets available in the paper’s Supporting Information: de-identified NGT notes and ranking forms.
- Limitations flagged by authors: underrepresentation of younger PLWH, field notes rather than verbatim transcripts, and single-site recruitment.
Qualitative analysis of HIV disclosure decision aids: implications for researchers
For UX & qualitative teams
For UX & qualitative teams: Separate stakeholder groups surface divergent priorities, here providers prioritized scalability and digital formats while PLWH prioritized trust and individual support.
Capture both ranking scores and verbatim or near-verbatim quotes to quantify preference strength and interpret rationale.
Use cross-segment comparison to flag adaptation tensions early, for example digital scalability versus one-to-one emotional safety.
For policy / implementation leads
For policy and implementation leads: Prioritize end-user preferences when selecting interventions for adaptation, this study weighted PLWH perspectives in the final decision.
Plan hybrid delivery by adapting a family-focused decision aid to include initial one-to-one sessions with optional later group or peer elements and digital follow-ups to balance feasibility and acceptability.
Ethics & safeguards (research note)
Ethics and safeguards: This is research-focused guidance, not clinical advice, so maintain consent, confidentiality, and safety protocols for disclosure-sensitive topics.
The study used de-identified notes and IRB approval (IRB #2025-017).
Do more, faster with Evidano (operational mapping)
Ingest: field notes, rankings, and transcripts
Ingest: Upload de-identified NGT notes, ranking spreadsheets, and (if available) audio/video to Evidano for analysis.
Evidano supports custom dictionaries and PII redaction to match local names and terms before analysis.
Analyze: thematic + cross-segment comparison
Analyze: Run automated thematic extraction to identify adaptation priorities such as peer educators, legal content, and confidentiality, and produce frequency counts by stakeholder segment (providers versus PLWH).
Evidano produces hierarchical codes and subcodes so teams can map high-level themes to concrete implementation items.
Visualize & convince
Visualize and convince: Generate co-occurrence networks and segment heatmaps showing where themes cluster, for example trust co-occurring with one-to-one and peer support.
Exportable word clouds and tables support stakeholder briefs, and the AI chat over your documents can answer ad-hoc questions like: 'Which adaptations did PLWH mention most for family disclosure? '
Secure & repeatable
Secure and repeatable: Data is encrypted and never used to train third-party models, and analyses can be re-run as new pilot data arrive to track changing preferences without rebuilding the codebook.
Checklist: 7-step AI-enabled workflow to reproduce this study
Checklist: Use this 7-step AI-enabled workflow to reproduce the study’s prioritization and synthesize adaptation requests faster.
Step 1: Capture: collect de-identified ranking forms, flip-chart notes, and audio, and obtain IRB/consent for secondary analysis.
Step 2: Prep: clean files, apply a custom dictionary for local terms, and redact any PII.
Step 3: Ingest: upload transcripts, notes, and ranking spreadsheets into Evidano.
Step 4: Codebook: import the nine candidate interventions as labels and run automated thematic extraction, then review suggested codes.
Step 5: Cross-segment analysis: run frequency and co-occurrence analyses comparing providers versus PLWH, and produce ranked lists and illustrative quotes.
Step 6: Visuals & brief: export a one-page decision brief with rankings, heatmaps, and recommended adaptations for pilot design.
Step 7: Iterate: after pilot feedback, re-ingest new transcripts and run longitudinal change analysis to adapt delivery (digital versus session mix).
FAQ: HIV disclosure decision aids
What was the sample size and composition of the study?
Answer: The study included 22 participants, with providers n=12 and people living with HIV (PLWH) n=10.
The NGT sessions were held in Tbilisi and produced ranked preferences by stakeholder group.
Which decision aid was prioritized for adaptation?
Answer: The Family disclosure decision aid was prioritized for adaptation by consensus in the joint session (Jul 2026).
Both providers and PLWH contributed rankings that led to this final selection.
What methods did the researchers use to reach priorities?
Answer: The researchers used the Nominal Group Technique with two 60-minute stakeholder sessions and a 40-minute joint session, using idea-generation, round-robin sharing, and independent ranking.
The outputs included de-identified discussion notes and ranking scores that informed adaptation priorities.
What adaptation priorities emerged from the analysis?
Answer: The main adaptation priorities included peer educators, legal content, confidentiality, and flexible timing.
Cross-segment comparison highlighted tensions such as digital scalability versus one-to-one emotional safety.
Conclusion & next steps
Conclusion: The PLOS One NGT study (published 16 July 2026) demonstrates how structured stakeholder methods reveal divergent priorities when adapting disclosure decision aids.
If you are preparing an adaptation or pilot, combine NGT-style ranking with AI-enabled qualitative synthesis to cut synthesis time and produce reproducible, visual-ready briefs for funders and implementers.
Ready to run this on your own NGT notes or transcripts? See how Evidano ingests de-identified notes, runs thematic and cross-segment analyses, and exports stakeholder-ready visuals. Try Evidano for free.