Presentation (CTI)

OBJECTIVES: Many innovative medical technologies span multiple clinical indications, yet traditional evaluations focus on single‑context assessments. This limits insight into how such technologies should be used optimally in practice. Consequently, decision makers struggle to determine which patient groups benefit most and under what circumstances multi‑indication technologies add value. This study examines if and how HEOR methods can be applied to evaluate multi-indication technologies.
METHODS: We developed principles to guide the evaluation of multi-indication technologies. We applied multiple complementary methods to learn if and how they could support decision making about optimal use of the MR-Linac, an illustrative case to learn from. Group model building was used to map the system in which the technology operates, semi‑structured interviews explored stakeholder definitions of value and risk, and quantitative exploratory analyses were conducted to assess conditions under which the MR‑Linac could deliver benefit across indications. Based on the findings we derived underlying principles relevant for multi‑indication evaluation within HEOR.
RESULTS: We propose five principles for evaluating multi-indication technologies: 1) Understand context by mapping the system in which the technology operates, 2) Identify value and risks to clarify what optimal use means for stakeholders, 3) Quantify potential value to explore under which circumstances the technology delivers benefits, 4) Prioritize across uncertainty by estimating the maximum potential benefit per indication and 5) Monitor and Adapt by predefining when reconsideration is needed and monitoring whether expected benefits hold. These principles can be adapted to varying evidence contexts and decision timelines.
CONCLUSIONS: The principles provide a structured and flexible approach for evaluation of multi-indication technologies, supporting explicit deliberation about patient prioritization and technology value. They offer a foundation for methodological development within the HEOR community and help innovators design evidence generation strategies that identify where their technology adds the most value and what evidence is needed to demonstrate it.