Surodina, SvitlanaBorgo, RitaSheng, YunSlingsby, Aidan2025-09-092025-09-092025978-3-03868-293-6https://doi.org/10.2312/cgvc.20251216https://diglib.eg.org/handle/10.2312/cgvc20251216Design studies are a core methodology in visualisation for solving real-world problems, but applying them in complex domains, such as clinical visual analytics, encounters well-recognised challenges. Existing frameworks provide rigour but often fall short in guiding systematic long-term, cross-disciplinary collaborations and sustainable tool adoption in high-stakes settings. This paper introduces a two-phase framework combining extended domain-characterisation methods and grounded in the established design study methodologies to frame the industry-level precondition analysis from project-specific design. Validated through AI-Enabled Clinical Decision Support Systems (AI-CDSS) case studies, our approach standardises domain constraints upfront, accelerates project onboarding, and lays the groundwork for cross-project comparison for sustainable, scalable visualisation research.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visualization design and evaluation methods; Software creation and management → Requirements analysis; Applied computing → Decision analysis; Health care information systemsHuman centered computing → Visualization design and evaluation methodsSoftware creation and management → Requirements analysisApplied computing → Decision analysisHealth care information systems10.2312/cgvc.2025121610 pages