Presentation (CTI)

OBJECTIVES: Delphi and structured expert consensus methods are widely used to address uncertainty in healthcare decision-making. While often applied at later stages (e.g., health technology assessment [HTA]) to resolve evidence gaps under time constraints, their broader role in shaping evidence generation across the product development and market access lifecycle remains underexplored. This study aimed to characterise applications of Delphi methods and develop a framework to support more systematic, lifecycle-wide use.
METHODS: A targeted review of PubMed and grey literature (2021-2026) identified applied Delphi and structured consensus studies across clinical research, health economics and outcomes research (HEOR), and HTA contexts. Studies were screened for relevance to evidence generation or decision-making. Included studies were categorised by timing and function across the product lifecycle including: study design (early development and evidence planning), data generation (late development and pre-launch evidence generation), access decision-making (e.g. uncertainty analysis for HTA /reimbursement), and post-launch evidence. A qualitative synthesis informed development of a conceptual framework mapping Delphi applications to decision points across lifecycle stages.
RESULTS: Delphi is often applied at the analysis stage in HTA, informing model inputs and addressing residual uncertainty, indicating a predominantly reactive role. However, earlier applications demonstrated value. In study design, Delphi supported alignment on populations, comparators, outcomes, and pathways; in data generation, these insights informed registry design, dataset structures, and variable selection, ensuring relevance to clinical practice and anticipated evidence needs. However, use was inconsistently reported and rarely linked to lifecycle stages, limiting visibility of how early consensus informs downstream decision-making. The framework illustrates more systematic use across stages, supported by use cases including rare disease, oncology real-world evidence, and comparator selection.
CONCLUSIONS: Delphi methods are widely used but often applied late. A structured, transparent lifecycle approach can support earlier identification and mitigation of evidence gaps, enabling proactive, HTA-aligned evidence planning and reducing downstream uncertainty.