Evidano is an AI-powered qualitative data analysis platform that reduces manual synthesis time while preserving auditability and data security. Researchers and clinicians often struggle to convert hundreds of stakeholder questions into a focused research agenda. The PLoS study published on July 13, 2026 identified the top 10 back pain research priorities by consolidating 230 submitted questions into 37 core items and surveying n=284 respondents, a process ripe for faster, more reproducible qualitative methods. This post shows how to run a reproducible qualitative analysis of back pain research priorities and how AI tools like Evidano (www.evidano.com) cut manual work from transcript ingestion to theme and cross-segment analysis while preserving auditability and data security.
Key Takeaways
A concise answer: an AI-enabled PSP-style workflow can reproduce and scale the PLoS One priority-setting process, cutting synthesis time while keeping an auditable trail.
Practical summary: the PLoS study (Aug 2024–Jan 2025) condensed 230 submitted questions to 37 core questions, surveyed 284 respondents, and produced a consensus top-10 list on July 13, 2026, and the AI-enabled two-week runbook below reproduces that process more quickly.
- The PLoS One process used three focus groups (12 people with lived experience, 7 healthcare professionals) to generate 230 questions, later consolidated to 37 core questions.
- The interim survey reached 284 respondents and the consensus workshop produced a top 10 list on July 13, 2026.
- An AI-enabled two-week workflow ingests transcripts, auto-clusters suggestions into candidate core questions, runs literature checks, and prepares a ranked shortlist and workshop packet.
- Evidano supports secure ingestion, transcription with custom dictionaries, evidence-mapping, cross-segment comparison, and reproducible exports.
Fast take: what the PLoS study did (source)
The PLoS study used an adapted James Lind Alliance process in Norway (Aug 2024–Jan 2025) to identify back pain research priorities.
The PLoS study ran three focus groups (19 participants: 12 with lived experience and 7 healthcare professionals), generated 230 questions, condensed those to 37 core questions, surveyed 284 respondents, and produced a top-10 list.
Full paper: PLoS One.
- Why it matters: the top priority was ensuring implementation of evidence-based back pain recommendations in the Norwegian system.
- Practical payoff for researchers: the PLoS One process produced a consensus-driven, end-user-aligned agenda to guide funding and trials.
Findings snapshot
| Date / Phase | Metric | Value | Source / Note |
|---|---|---|---|
| Aug 2024–Jan 2025 | Focus groups | 3 groups; 19 participants (12 lived experience; 7 HCPs) | PLoS One methods |
| Focus group output | Raw questions | 230 (175 from lived experience; 55 from HCPs) | 8 out-of-scope → 222 consolidated |
| Consolidation | Core questions | 37 | Reviewed against PubMed; none fully answered |
| Interim survey | Responses | 284 (263 lived experience; 40 HCPs) | Top 25 forwarded to workshop |
| Workshop | Final participants | 9 (5 HCPs, 4 lived experience) | Top 10 priorities agreed by consensus |
What happened (methods, succinct)
This section explains that the team followed an adapted James Lind Alliance priority-setting process, using focus groups, consolidation, evidence checks, an interim survey, and a workshop to finalize priorities.
The team followed an adapted James Lind Alliance PSP: steering committee, focused scope, focus groups to gather uncertainties, thematic consolidation into core questions, literature checks, online interim prioritisation, and an in-person workshop to finalize the top 10.
- Consolidation favored broad, inclusive core questions to avoid diluting themes.
- Evidence checks prioritized high-quality syntheses (systematic reviews) on PubMed.
- Deviations from JLA included use of focus groups to maximise inclusion, no formal JLA advisor, and carers were not included.
How to run qualitative analysis of back pain research priorities (with AI)
This section answers how to replicate or scale a PSP-style process: follow the same core steps and add AI to accelerate and improve reproducibility.
If you need to replicate or scale this PSP-style process, the core steps are the same, but AI can accelerate them and improve reproducibility.
- Ingest: import audio, transcripts, survey spreadsheets and focus-group notes into Evidano.
- Normalize: run automated transcription (with custom dictionary) and translation where needed, and apply PII redaction.
- Consolidate: use thematic clustering to merge overlapping questions into candidate core questions and surface duplicates.
- Evidence check: auto-suggest existing systematic reviews and flag questions likely answered by literature.
- Prioritise: generate weighted scoring by segment (patients vs clinicians) and produce shortlists for a workshop.
- Audit & export: produce reproducible codebooks, quote lists, co-occurrence maps, and slide-ready visuals.
Implications for researchers, funders and UX teams
For clinical researchers
The recommendation for clinical researchers is to use AI-assisted consolidation to reduce bias and document merges.
Use AI-assisted consolidation to reduce researcher bias in framing core questions and to document how questions were merged.
Evidano evidence-mapping features speed the 'has this been answered? ' check that the PLoS team ran manually.
For funders & policy teams
The guidance for funders and policy teams is to produce transparent, auditable priority lists linked to participant quotes and demographics to justify funding calls.
Produce transparent, auditable priority lists linked to participant quotes and demographic segments to justify funding calls.
Compare priorities across regions or cohorts quickly with cross-segment analysis.
For UX / service design teams
The action for UX and service design teams is to translate priorities into user stories and experiments using exported theme hierarchies.
Translate priorities into user stories and service experiments; export theme → subtheme hierarchies and co-occurrence networks to spot pain points.
Two-week reproducible workflow (checklist)
This section provides a two-week runbook to replicate a PSP-style priority-setting process with AI in roughly two weeks of active work.
Runbook: replicate a PSP-style priority-setting process with AI in ~2 weeks of active work.
- Day 1–2: Import files (audio, transcripts, survey CSVs) into Evidano; set custom dictionary and PII rules.
- Day 3–4: Auto-transcribe and translate; quick QA pass on 10% of transcripts.
- Day 5–6: Auto-cluster 200+ suggestions into candidate core questions; review and merge manually (steering committee).
- Day 7–9: Run automated literature check (systematic review flags) and remove answered questions.
- Day 10–11: Launch weighted online interim prioritisation (segment by role, exposure duration, region).
- Day 12–13: Produce shortlist with cross-segment divergence report and visualizations (word clouds, co-occurrence).
- Day 14: Prepare workshop packet: ranked shortlist, supporting quotes, and reproducible audit trail for delegates.
Ethics note: this workflow is research-focused and non-diagnostic. Secure consent, anonymise data, and follow your institutional rules.
Do more, faster with Evidano
Problem: manual consolidation eats time
The problem is that manual consolidation consumes many researcher hours; Evidano automates clustering and candidate question generation.
Evidano automates clustering and generates candidate core questions, reducing hours of manual coding.
Problem: inconsistent segment comparisons
The issue of inconsistent segment comparisons is solved by cross-segment frequency and thematic comparison features.
Use cross-segment frequency and thematic comparison to quantify differences between patients and clinicians (or regions).
Problem: workshop prep is chaotic
The workshop preparation problem is solved by exports of ranked lists, supporting quotes, and visualizations that make packets slide-ready.
Evidano exports ranked lists, supporting quotes, and visualizations (co-occurrence networks, hierarchical codes → subcodes) into slide-ready formats.
Security & compliance
The security and compliance statement is that data is encrypted and never used to train third-party models, with versioned exports for audit trails.
Data is encrypted and never used to train third-party models. For reproducible audit trails, Evidano stores versioned exports and codebooks.
FAQ: Qualitative analysis of back pain research priorities
What did the PLoS One study do and find?
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The short answer is the PLoS One study used an adapted JLA PSP process to produce a top-10 list of back pain research priorities on July 13, 2026.
The PLoS One study ran three focus groups in Norway (Aug 2024–Jan 2025), generated 230 questions, consolidated them to 37 core questions, surveyed 284 respondents, and agreed a top 10 by workshop consensus.
Full paper: PLoS One.
How were the 230 raw questions consolidated into 37 core questions?
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The direct answer is the team used thematic consolidation and literature checks to merge overlapping submissions and remove out-of-scope items.
Consolidation favoured broad, inclusive core questions to avoid diluting themes and the team reviewed candidates against PubMed and systematic reviews to identify unanswered questions.
How can AI speed and improve this priority-setting process?
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The one-sentence answer is AI can automate transcription, clustering, evidence-mapping, cross-segment scoring, and reproducible exports to cut manual time and reduce bias.
AI speeds ingestion, normalization, consolidation, literature checks, prioritisation and audit exports, producing candidate core questions and visualisations ready for workshop use.
What does a two-week AI-enabled runbook look like?
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The brief answer is the two-week runbook runs ingestion, transcription, clustering, literature checks, interim prioritisation and workshop packet preparation across 14 days.
Day-by-day steps include import and dictionary setup (Days 1–2), transcription QA (Days 3–4), auto-clustering (Days 5–6), literature checks (Days 7–9), interim prioritisation (Days 10–11), shortlist and visuals (Days 12–13), and workshop packet preparation (Day 14).
Wrapping up, your next steps
The PLoS One PSP (Jul 13, 2026) shows that stakeholder-driven priorities cluster around implementation, system organisation, recognition and diagnostics, and instrumenting the process with AI preserves rigor while cutting synthesis time.
If you run similar priority-setting work, instrumenting the process with AI preserves rigor while cutting synthesis time.
- Try the two-week workflow above on a pilot cohort and compare time-to-insight versus a manual approach.
- Learn more and try Evidano at Evidano; we provide secure ingestion, thematic + cross-segment analysis, transcription with custom dictionaries, and reproducible exports to power stakeholder-driven research.
- Try Evidano for free.
Topics
- qualitative analysis
- back pain research priorities
- priority-setting partnership
- James Lind Alliance
- Evidano workflow
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