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Faster Insights: Qualitative Analysis in Physical Therapy

Evidano7 min read

Qualitative analysis in physical therapy helps researchers explain why a measurement tool succeeds in practice, not only whether the tool produces a score. The 2026 PLOS One study on Dutch-Flemish PROMIS computerized adaptive tests (CATs) shows how interviews can uncover workflow barriers, interpretation problems, and differences between clinician and patient perspectives. The study combined survey data from physical therapists with semi-structured interviews and thematic analysis, creating a useful model for AI-enabled qualitative research in healthcare. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and clinical research documents while preserving a transparent path from source data to themes. This article translates the study's findings into practical guidance for physical therapy researchers, implementation teams, and healthcare analysts.

Key Takeaways

According to the 2026 PLOS One study, PROMIS CATs were feasible and generally acceptable in primary care physical therapy, but wider implementation depended on reducing duplicate questionnaires, improving score interpretation, and fitting the tool into clinical workflows.

  • The study published on November 13, 2026, included 25 surveyed physical therapists, 10 interviewed physical therapists, and 9 interviewed patients across 14 Dutch physical therapy practices.
  • According to the study, 80% of surveyed physical therapists expressed interest in using PROMIS CATs, while only 28% reported regular daily use.
  • According to the study, 100% of surveyed physical therapists found PROMIS CATs easy to administer, but only 40% considered them time-efficient.
  • The study found that patients generally described the CATs as quick and relevant when results were used during consultations.
  • AI-enabled thematic analysis can help research teams compare survey patterns with interview themes, identify divergent perspectives, and organize evidence for implementation decisions.

What the PROMIS CAT Study Found

The 2026 PLOS One study evaluated Dutch-Flemish PROMIS CATs for adults with musculoskeletal spine and upper-extremity conditions receiving primary care physical therapy. Researchers integrated the CATs into electronic health record workflows across 14 practices and assessed acceptability, demand, practicality, and implementation.

The study used a mixed-methods design: 25 physical therapists completed a survey, while 10 physical therapists and 9 patients participated in semi-structured interviews. Two researchers independently performed open coding, compared codes through consensus meetings, and mapped inductively developed themes onto Bowen's feasibility domains.

The study reported that physical therapists generally viewed PROMIS CATs as useful for treatment evaluation and focused discussions. However, the study also identified barriers involving legacy PROMs, patient portal problems, uncertainty about T-scores and cut-offs, and concerns that some items were repetitive or insufficiently specific.

One interviewed patient said, "It zoomed in on what mattered to me, instead of asking irrelevant things" (Patient #4). One physical therapist described a central implementation problem: "If the CAT can replace NPRS, PSFS, and Quebec while giving the same information, that’s a win-win" (PT #6).

The study's findings are research evidence about feasibility and user experience, not clinical guidance or a diagnostic assessment.

PROMIS CAT Findings Snapshot

Date or study measureMetricValueImplication for qualitative research
November 13, 2026Study publicationPLOS One, 2026Provides the primary evidence base for the findings
Study periodPhysical therapy practices14 practices in the NetherlandsSupports analysis of implementation across multiple real-world settings
Survey samplePhysical therapists surveyed25 therapistsQuantifies clinician perceptions of feasibility
Interview samplePhysical therapists and patients interviewed10 therapists and 9 patientsAdds participant explanations and lived experience
Survey resultPhysical therapists interested in PROMIS CAT use80%Shows demand despite limited routine adoption
Survey resultPhysical therapists reporting regular daily use28%Shows the gap between interest and implementation
Survey resultPhysical therapists finding CATs easy to administer100%Indicates strong perceived usability at the administration level
Survey resultPhysical therapists viewing CATs as time-efficient40%Suggests that wider workflow burden affects perceived efficiency

Implications for Physical Therapy Researchers

Why should researchers analyze clinician and patient perspectives separately?

Researchers should analyze clinician and patient perspectives separately because the 2026 PLOS One study found a divergence between survey-based clinician perceptions and interviewed patient accounts.

Only 24% of surveyed physical therapists believed patients were satisfied with PROMIS CATs, while interviewed patients generally described the CATs as quick, simple, and relevant. Separate coding preserves this difference instead of averaging it away.

How can qualitative analysis explain the gap between interest and adoption?

Qualitative analysis can explain adoption gaps by connecting numeric findings with participants' descriptions of workflow, training, and organizational barriers.

The study reported 80% interest in PROMIS CATs but only 28% regular daily use. Interview themes linked that gap to duplicate legacy PROMs, portal difficulties, limited score interpretation training, and the absence of structural support.

What should implementation teams code for?

Implementation teams should code for acceptability, demand, practicality, and organizational embedding because the study used these four domains to assess feasibility.

The 2026 study identified recurring topics including questionnaire burden, result discussion, graphical feedback, item relevance, digital literacy, patient portals, reimbursement, professional guidelines, and access to individual item responses.

How Evidano Helps With AI Qualitative Research

Problem: Interview transcripts take too long to synthesize

Evidano is an AI-powered qualitative data analysis platform that helps researchers organize interview transcripts, open-ended survey responses, and research documents into evidence-backed themes.

Researchers can use thematic analysis to review comments about questionnaire burden, score interpretation, digital access, and clinical usefulness, then compare those themes across physical therapists and patients. Teams can explore the workflow in Evidano's features.

Problem: Survey percentages and interview themes are difficult to connect

Evidano supports cross-segment analysis so research teams can compare quantitative response groups with qualitative themes from different participant segments.

For a PROMIS implementation study, researchers could compare therapists who report regular use with therapists who report low use, then examine whether training, portal access, or legacy PROM burden appears in each segment.

Problem: Sensitive healthcare interviews require careful handling

Evidano provides transcription with PII redaction and encrypted data handling for research workflows involving sensitive interview material.

Evidano states that customer data is never used to train third-party models, which is relevant when researchers process patient or clinician interviews. Teams should still follow their institutional ethics, consent, retention, and access requirements.

Problem: Findings need to remain inspectable

Evidano provides AI chat over documents and analyses, along with visualizations such as word clouds, co-occurrence networks, and hierarchical codes with subcodes.

These tools can help a team move from recurring phrases to a code structure, inspect relationships among barriers, and ask follow-up questions without losing the connection to the underlying research corpus.

FAQ: Qualitative Analysis in Physical Therapy

What is qualitative analysis in physical therapy?

Qualitative analysis in physical therapy is the systematic interpretation of interviews, open-ended responses, observations, or documents to identify experiences, barriers, facilitators, and decision-relevant themes.

The 2026 PLOS One study used hybrid inductive-deductive thematic analysis to understand how physical therapists and patients experienced PROMIS CATs.

How many participants were included in the PROMIS CAT feasibility study?

The study included 25 physical therapists in a survey, 10 physical therapists in interviews, and 9 patients in interviews.

The research took place across 14 physical therapy practices in the Netherlands, according to the study published on November 13, 2026.

What were the main barriers to PROMIS CAT implementation?

The main barriers were simultaneous use of legacy PROMs, patient portal difficulties, uncertainty about interpreting CAT scores, limited structural embedding, and concerns about the relevance of some items.

The study also reported that only 28% of surveyed physical therapists used PROMIS CATs regularly in daily practice, despite 80% expressing interest.

How can AI support thematic analysis of healthcare interviews?

AI can support thematic analysis by accelerating transcription review, suggesting codes, grouping recurring concepts, comparing participant segments, and helping researchers query a large qualitative corpus.

Human researchers should validate interpretations, review source passages, document analytical decisions, and protect participant confidentiality because AI output does not replace methodological judgment.

Can AI qualitative analysis replace human researchers?

AI qualitative analysis should support rather than replace human researchers in healthcare studies.

The PROMIS CAT study used independent coding, consensus meetings, a disagreement log, and peer debriefing, demonstrating the importance of researcher oversight and documented analytic rigor.

Conclusion and Next Steps

The 2026 PLOS One study shows that PROMIS CATs can be acceptable and practical in physical therapy, but implementation depends on reducing duplicate measurement, improving interpretation, and aligning tools with real workflows.

The study also demonstrates why qualitative analysis matters: interviews explained the difference between clinician perceptions and patient experiences, while thematic coding revealed barriers that percentages alone could not clarify.

AI-enabled qualitative research can help teams process transcripts and open-ended survey data faster while preserving segment comparisons and traceable themes. Researchers should combine AI assistance with human review, ethical safeguards, and transparent documentation.

Physical therapy researchers and healthcare teams can try Evidano for free to accelerate qualitative synthesis across interviews, surveys, and research documents.

Topics

  • qualitative analysis in physical therapy
  • AI qualitative research healthcare
  • PROMIS CAT qualitative analysis
  • thematic analysis physical therapy
  • AI thematic analysis

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