This post explains how patient psychological safety shapes whether people discuss medications, and how AI-enabled qualitative research can make those insights actionable. The primary keyword is qualitative analysis of patient psychological safety. The intended audience is qualitative researchers, health services teams, and UX or implementation leads who analyze interviews or focus groups about medication use. The payoff: concrete coding priorities, reproducible analytic steps, and tools you can use to speed synthesis while preserving participant nuance.
Key Takeaways
According to the PLOS One study by El-Kotob et al., psychological safety determined whether patients raised medication concerns during clinical encounters, with interviews collected between May and August 2024 and the article published on August 20, 2026.
- The PLOS One study interviewed 21 participants (n = 21) between May and August 2024, with most participants aged 40 or older (n = 19) and identifying as White (n = 14) and women (n = 14), according to El-Kotob et al.
- El-Kotob et al. report three core constructs from the Patient Psychological Safety (PPS) framework: Patient Belonging, Patient Learning, and Patient Participating, which together shaped willingness to speak up about medications.
- A direct participant quote in the PLOS One paper summarized the risk of silence: "I just don’t want to take up people’s time, " (El-Kotob et al., PLOS One, 2026).
- The study found that clear, contextualized communication and explicit invitations to participate reduced distress and increased engagement in medication decisions, a pattern reported across interviews conducted May–August 2024.
What happened: qualitative analysis of patient psychological safety in medication conversations
The PLOS One study conducted a secondary deductive qualitative analysis of an existing interview dataset to examine psychological safety during medication-related encounters.
According to El-Kotob et al., researchers used the Patient Psychological Safety (PPS) framework to code for three domains: Patient Belonging, Patient Learning, and Patient Participating, applying a data-display matrix and consensus coding in Microsoft Excel.
According to El-Kotob et al., sessions were audio-recorded, transcribed, and collected via one-on-one interviews and focus groups between 01/05/2024 and 31/08/2024, and the team continued sampling until informational power was reached.
Findings Snapshot
| Date / Timeline | Metric | Value | Implication |
|---|---|---|---|
| May–Aug 2024 | Interview window | n = 21 participants | Sufficient depth for PPS-focused secondary analysis, per El-Kotob et al. |
| Aug 20, 2026 | Publication | PLOS One article (El-Kotob et al.) | Peer-reviewed, open access framing for implementation work |
| Participant demographics (reported in study) | Age, race, gender | ≥40 years (n = 19), White (n = 14), women (n = 14) | Findings may underrepresent younger adults and some racial groups |
| Analytic framework | PPS framework domains | Belonging, Learning, Participating | Provides explicit code categories for future qualitative coding |
Implications for qualitative researchers analyzing patient psychological safety
Researchers should prioritize coding for belonging, learning, and participation when analyzing medication conversations, because El-Kotob et al. found these constructs consistently determined whether patients spoke up.
- Code for emotional response: according to the PLOS One study, feelings of dismissal or being heard mapped directly to willingness to disclose medication issues.
- Capture context signals: El-Kotob et al. report that brief, fragmented primary care encounters increased the risk that patients would withhold concerns.
- Stratify by identity: the PLOS One authors noted that race, gender, and disability shaped dismissal experiences, so researchers should include demographic cross-segmentation in analyses.
How Evidano Helps: apply AI-enabled qualitative research to psychological safety
Problem: Large interview sets are time-consuming to synthesize → Solution: Rapid thematic + frequency analysis
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano’s thematic coding and frequency analysis can accelerate reproducing the PPS framework across interviews while preserving verbatim excerpts for audit trails.
Use case: import the 21 transcripts cited in the PLOS One study to generate deductive codes (Belonging, Learning, Participating) and surface the most frequent co-occurring subthemes.
Problem: Missing demographic cross-segmentation → Solution: Cross-segment and co-occurrence analysis
According to El-Kotob et al., demographic factors shaped experiences of dismissal; Evidano supports cross-segment analysis so you can compare themes by age, gender, or race.
Evidano’s co-occurrence network and hierarchical subcodes help you detect whether "dismissal" co-occurs more often with specific identity markers in your dataset.
Problem: Replicability and researcher consensus are hard → Solution: reproducible matrices and AI-assisted memos
El-Kotob et al. used a data-display matrix and consensus coding in Excel; Evidano automates matrix creation and stores analyst notes to speed reconciliation.
Evidano’s AI chat over your documents helps generate coder memos and suggested quotations that maintain in-sentence attribution for quotes and statistics.
Problem: Audio transcription and PII concerns → Solution: encrypted transcription with custom dictionaries
If you follow the PLOS One approach of audio-recorded interviews, Evidano’s transcription (with custom dictionary and PII redaction) reduces manual cleaning time while protecting participant privacy.
See Evidano features for details on transcription, encryption, and analytic exports.
FAQ: qualitative analysis of patient psychological safety
What is patient psychological safety in the context of medication conversations?
Patient psychological safety is the extent to which a patient feels accepted, able to learn, and safe to participate in discussions about medications.
According to the PLOS One study, the Patient Psychological Safety (PPS) framework defines three domains (Belonging, Learning, Participating) and links them to willingness to disclose medication concerns.
How did the PLOS One study measure psychological safety?
The PLOS One study used a secondary deductive qualitative analysis of interviews coded to the PPS framework.
El-Kotob et al. collected interviews between May and August 2024 (n = 21), transcribed audio-recordings, and populated a data-display matrix to reach consensus coding.
How can AI speed qualitative analysis without losing nuance?
AI can accelerate coding, surface co-occurrence patterns, and generate initial memos while preserving verbatim quotes for human validation.
Evidano applies proprietary LLMs tuned for qualitative research to produce thematic, frequency, and cross-segment analyses, and retains original passages for auditability so human analysts can confirm interpretive claims.
Are there ethical limits to using participant data with AI tools?
AI-assisted analysis should follow consent, de-identification, and ethics approvals and avoid using identifiable data in external models without permission.
El-Kotob et al. flagged ethics constraints on sharing identifiable raw data; Evidano supports PII redaction and encrypted storage to align with such requirements, see Evidano data security.
Conclusion & Next Steps
The PLOS One qualitative analysis by El-Kotob et al. shows that fostering belonging, clear learning, and participation changes whether patients raise medication concerns during clinical encounters.
Researchers and care teams should code for the three PPS domains, stratify results by demographic groups, and preserve verbatim participant quotes when reporting, as recommended in the PLOS One paper.
If you want to apply these methods to your own transcripts and accelerate synthesis with AI while keeping human oversight, Try Evidano for free.
Topics
- qualitative analysis of patient psychological safety
- patient psychological safety qualitative analysis
- psychological safety medication conversations
- AI qualitative research
- qualitative medication communication analysis
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