Better Global Health Roundtables: AI Qualitative Analysis

A global health roundtable is a structured, facilitated discussion that connects researchers, practitioners, policymakers, community representatives, and people with lived experience to exchange knowledge and address complex health challenges. According to the November 5, 2026 Ten simple rules for a successful global health roundtable, roundtables work best when organisers define a focused purpose, include diverse voices, manage power differences, and plan how dialogue will become useful outputs.

• The 2026 PLOS Computational Biology article presents 10 rules covering roundtable planning, participation, facilitation, ethics, outputs, and follow-up.
• The authors state that roundtables can combine experimental, observational, contextual, applied, and experiential knowledge to address complex global health challenges.
• The authors caution that roundtable outputs may provide less individual or localised depth than interviews and focus groups, making methodological transparency essential.
• AI qualitative analysis can help researchers organise transcripts, compare themes across stakeholder groups, and trace findings back to source passages, but AI cannot replace inclusive facilitation or ethical consent.
• The authors describe roundtables as “not scripted performances but dynamic, evolving conversations, ” so analysis workflows should preserve unexpected themes rather than force every contribution into predetermined codes.

The 2026 guidance argues that a successful global health roundtable depends on deliberate design before, during, and after the discussion. The authors developed the 10 rules by synthesising existing literature with their collective experience designing, convening, facilitating, and evaluating roundtables across global health research, policy, and practice.

The source defines a global health roundtable as a structured and facilitated discussion involving stakeholders from diverse geographical and institutional contexts. According to the authors, the format is intended to support knowledge exchange, collaborative relationships, and joint responses to complex global health problems.

The first four rules focus on analytical purpose: choose a specific knowledge gap, define the objective and discussion questions, invite diverse and experienced panellists, and decide early what outputs the discussion should produce. The authors list possible outputs including policy briefs, health reports, guidelines, white papers, peer-reviewed articles, memorandums of understanding, and advocacy initiatives.

The remaining six rules focus on participation and continuity: build commitment, use an inclusive and participatory approach, manage power dynamics, allow unexpected directions, plan future engagement, and acknowledge contributions from communities, NGOs, academics, and services.

The authors emphasise that ethical recognition should reflect intellectual contribution and may include co-authorship, co-presentation, dissemination support, or other meaningful forms of acknowledgement. The authors also warn that participatory formats can still reproduce power imbalances when organisers impose methods without allowing communities to shape the process.

Global health research teams should treat roundtable analysis as a structured comparison of perspectives, not merely as a summary of what participants said. The source authors argue that roundtables can support co-production, policy development, practice change, and longer-term collaboration, but the value depends on how well organisers connect the discussion to a defined objective.

Researchers should preserve stakeholder identity and context during analysis where consent and governance allow it. A theme such as “access” can mean something different to a patient, frontline worker, policymaker, or health-system administrator, and cross-segment comparisons can reveal those differences without collapsing them into one generalised finding.

AI qualitative analysis is most useful after the research team has established a defensible coding plan. Researchers can use AI to surface recurring themes, identify co-occurring concepts, compare stakeholder segments, retrieve supporting excerpts, and locate unexpected directions in a long transcript. Researchers should then review evidence, check interpretations against the original wording, and document analytical decisions.

The source authors' warning about power dynamics has a direct analytical consequence: frequency is not the same as importance. A dominant speaker may generate more transcript content than a community representative, while a less frequent contribution may identify a critical barrier or ethical concern. Qualitative teams should therefore combine thematic patterns with speaker context, facilitation notes, and reflexive review.

The source authors also describe roundtables as potential foundations for sustained engagement. A practical analysis workflow should therefore produce more than a meeting summary. The workflow should create an auditable set of themes, stakeholder comparisons, representative quotations, unresolved questions, proposed actions, and follow-up topics.

The 2026 global health roundtable guidance presents 10 practical rules for turning structured dialogue into inclusive, useful, and sustained knowledge exchange. AI qualitative analysis can help researchers manage the resulting transcripts, compare stakeholder perspectives, identify emerging themes, and create traceable outputs without removing human responsibility from the research process.

The strongest workflow combines focused objectives, ethical participation, careful facilitation, transparent coding, cross-segment analysis, and human validation of AI-supported findings. Evidano helps research teams move from roundtable documents to organised qualitative insights while keeping interpretation and governance with the people responsible for the study.

Explore the workflow and try Evidano for free to analyse global health roundtable transcripts, reports, and related qualitative data.