OBJECTIVES: Next Generation Sequencing (NGS) promises to find mutations (targets) in individual cancer patients, to subsequently assign targeted therapy. Currently, the technology itself is still in development and the effects on disease development, prognosis, or choice of therapy are still unclear. Besides information for the current patient, additional (secondary) information for future patients can also become available. To accelerate the reimbursement process and have a NGS-panel available for patients in the earliest possible stage, early Technology Assessment (TA) is ongoing. In this project, we report on expert elicitation by means of scenario drafting, to provide qualitative and quantitative data to fill the evidence gaps in early cost-effectiveness modeling. METHODS: The following steps in a multi-parameter framework can be distinguished: 1) Identifying (dynamic) aspects having impact on adoption; 2) Brainstorm on possible scenarios (informal interviews with NGS experts); 3) Scenario construction; 4) Validation of scenarios (semi-structured questionnaires to European NGS-experts); 5) Quantification into parameters for cost-effectiveness modeling. RESULTS: Based on the interviews and questionnaires (n=29), the most likely scenarios as patients interest in NGS (likelihood 66,5% ±28,1); organizational readiness (84,4 ±18,5); advantage of including RNA, and the demand from the clinic (needs of medical staff) were identified as drivers for NGS development. Possible barrier scenarios were: number of actionable targets (55,2 ±23,7); demonstrating clinical utility (50,4 ±31,4); current evidence generation  (65,5 ±27,9); consensus on panel for reimbursement (40,3% ±24,1); and competition within the field (64,2 ±21,9). Clustering into parameters for cost-effectiveness modeling resulted in: “failures”, “compliance”, “uptake”, and “future information”.  CONCLUSIONS: Although there are many issues to overcome to adopt NGS, the likelihood of NGS incorporation in clinical practice is very high. The “additional future information” is the most interesting but complex variable to identify and to incorporate in cost-effectiveness modeling.