AI Thematic Analysis for MS Research

The 2026 PLOS One study protocol describes a mixed-methods evaluation of online mindfulness-based cognitive therapy for people with multiple sclerosis and mild-to-moderate depression, using quantitative outcomes and qualitative interviews.

AI thematic analysis in healthcare can help research teams organize interview transcripts, identify recurring themes, compare stakeholder groups, and connect qualitative explanations with trial findings, while researchers retain responsibility for interpretation and ethics.

• The PLOS One protocol plans a waitlist randomized controlled trial with a minimum of 152 people with multiple sclerosis, with questionnaires collected at baseline, after 8 weeks, and at 21 weeks in the 2026 to 2028 study timeline.
• The PLOS One protocol plans 30 to 40 semi-structured interviews with participants, healthcare professionals, mindfulness-based cognitive therapy instructors, and multiple sclerosis decision makers.
• The PLOS One researchers will use inductive thematic analysis followed by deductive analysis informed by the Consolidated Framework for Implementation Research.
• The PLOS One protocol will merge qualitative and quantitative findings in a joint display to examine convergence, discordance, and explanation.
• The PLOS One protocol states that recruitment is expected to begin on November 1, 2026, data collection is expected to finish in March 2028, and results are expected in spring 2028.

The PLOS One protocol uses qualitative interviews to explain how an online mindfulness-based cognitive therapy intervention is experienced, accepted, and implemented, rather than measuring clinical effectiveness alone.

The study authors describe a type 1 hybrid effectiveness-implementation design conducted online through the University of Toronto. The quantitative component will assess changes in depression using the Hospital Anxiety and Depression Scale Depression subscale, while the qualitative component will explore motivations, accessibility, acceptability, perceived effects, and implementation barriers.

The intervention will consist of an 8-week online group course, with weekly 2.5-hour sessions, groups of up to 15 people, and home practices of up to 45 minutes per day, according to the PLOS One protocol.

The protocol uses inductive thematic analysis to allow themes to emerge from interview transcripts. The researchers will then use deductive analysis informed by the Consolidated Framework for Implementation Research to examine barriers and facilitators to implementation.

The study authors write that mixed methods will examine “whether it is effective but also how the intervention is perceived and should be implemented.” This distinction is important because a statistically promising intervention can still face adoption, accessibility, adherence, or scalability problems.

AI thematic analysis in healthcare is most useful when it accelerates evidence organization while preserving researcher-led interpretation, reflexivity, and participant protection.

• For qualitative researchers, AI can support first-pass coding, theme comparison, transcript search, and evidence retrieval across a large interview corpus, but researchers should review codes against the study question and analytic framework.
• For mixed-methods teams, AI can help align qualitative themes with quantitative time points, participant characteristics, adherence patterns, or outcome groups without treating correlation as explanation.
• For implementation researchers, cross-segment analysis can separate barriers reported by participants from barriers reported by healthcare professionals, instructors, and decision makers.
• For health research governance teams, transcript analysis should include de-identification, access controls, auditability, and a clear record of how AI-assisted outputs were reviewed.
• For clinical audiences, the protocol is not evidence that online mindfulness-based cognitive therapy reduces depression in people with multiple sclerosis. The protocol describes how that question will be studied.

The PLOS One protocol also illustrates why qualitative evidence needs contextual analysis. The researchers plan to use a joint display to assess fit between qualitative and quantitative findings, so a research team must preserve the connection between a theme, its supporting quotations, the participant group, and the relevant outcome data.

The 2026 PLOS One protocol shows how qualitative interviews can explain the acceptability, accessibility, and implementation of an online intervention alongside quantitative effectiveness measures.

AI thematic analysis in healthcare can make that mixed-methods workflow more manageable by helping teams organize transcripts, compare stakeholder segments, retrieve supporting evidence, and connect themes with trial data.

Researchers should use AI as a transparent analytical aid, not as a substitute for methodological expertise, participant protection, or clinical interpretation.

To explore AI-assisted qualitative analysis for interviews, documents, and survey responses, try Evidano for free.