Fast, reproducible synthesis is the bottleneck in turning SDM research into clinical change. A new qualitative study (published 30 June 2026) examined shared decision-making in pediatric physical therapy via six focus groups (adolescents n=11, parents n=9, pediatric physical therapists n=6) and a qualitative survey of 46 PPTs. The authors adapted a goal-based SDM model and mapped implementation strategies (training, SDM tools, team culture). Read the original study at PLOS ONE. If you run qualitative research, UX studies, or clinical implementation pilots, this post shows how to convert transcripts and surveys into actionable implementation artifacts using AI-enabled qualitative analysis platforms. You will get a compact workflow: what to extract, which themes to prioritize (intake and goal-setting, child involvement, therapy plan, recurring SDM), and how to operationalize implementation strategies with reproducible outputs for training and stakeholder buy-in.
Run this analysis on your own data
Start freeTopics
- qualitative analysis shared decision-making
- pediatric physical therapy
- goal-based SDM model
- CFIR implementation strategies
- AI qualitative analysis
Run this analysis on your own data
Evidano codes documents, interviews, survey responses and spreadsheets with AI, with your judgment in control at every step.
Keep reading
- Commentary on NewsFrom Interviews to Insight: AI Qualitative AnalysisLearn how AI qualitative analysis can organize mental health interviews, compare stakeholder views, and support culturally aware research. Try Evidano.
- Commentary on NewsWhat ELLY Reveals About AI Qualitative AnalysisSee how AI qualitative analysis can turn community-health interviews into defensible themes, pathways, and decisions, using findings from the 2026 ELLY study.
- Commentary on NewsWhat ELLY Shows About AI Qualitative AnalysisLearn how AI qualitative analysis can reveal intervention pathways, community needs, and participant outcomes from the ELLY health study. Try Evidano.
