Session (CTI)

PURPOSE: Session objectives are to (1) demonstrate how generative AI–assisted development can rapidly translate a published cost-effectiveness analysis (CEA) into a stakeholder-facing interactive dashboard; (2) introduce a theory-grounded framework for designing decision-maker–facing CEA dashboards that reduces stakeholder cognitive burden; (3) examine, potential adoption and its policy implications of AI-augmented HEOR communication for HTA practice in Asia Pacific; and (4) engage the audience in multi-perspective discussion bridging HEOR-methods, economics-policy, and public-sector decision-maker viewpoints. DESCRIPTION: CEAs are typically disseminated as static manuscripts, leaving non-technical stakeholders to imagine how findings apply to their context. This gap has been constrained by the development cost of interactive tools, which require Shiny or web-programming expertise rarely concentrated in HEOR teams, particularly in APAC HTA settings. Generative AI–assisted code development has lowered this barrier: a working interactive dashboard can now be built from a validated economic model in a day, and theoretical design principles can be operationalized into code with the large language model serving as a translation layer. The session will open by framing the CEA-to-stakeholder communication gap and the APAC HTA context (15 minutes, Nathorn). A live demonstration of building a Shiny dashboard for a US workplace influenza vaccination return-on-investment model will follow, walking through an employer scenario and reconstructing the AI-assisted development workflow (25 minutes, Jeong-Yeon). The original economic modeler will discuss methodologic rigor and validation under AI-assisted workflows (10 minutes, Richard). The session will close with moderated audience discussion on validation standards, perception of the audience on the value of LLM-generated artefacts, and the case for HTA-agency requirements for interactive supplements alongside static dossiers (10 minutes, Nathorn). This session may offer value to HEOR researchers, HTA staff, employers, payers, policymakers, and anyone who are interested in AI-augmented dissemination of HEOR works.