Close cooperation with Health Technology Assessment expertise is crucial for implementation and ultimately reimbursement of innovations in oncology

The Organisation of European Cancer Institutes OECI working group on Health Economics and Cost Benefit in Oncology suggests four actions that are needed to improve alignment and integration between clinicians, researchers, and Health Technology Assessment (HTA) experts and agencies: 1) HTA expertise is necessary close to or within the comprehensive cancer centres (CCC); 2) HTA expertise should be physically present throughout the translational research process; 3) Appropriate knowledge is necessary within the research staff; 4) Close cooperation between translational researchers, clinicians, and health economists guarantees clinical ownership. Fulfilling these conditions may help the translational research field in oncology to interact with agencies and efficiently move innovative technologies through the translational research stages into that of implementation and diffusion. This brings innovative treatments faster to the patient with a greater chance of reimbursement

The Top One Percent and Exploitation Measures

With the Occupy Movement that recently took place on Wall Street and in other parts of the globe, a lot of attention has recently been given to growing income inequality. The 2008 United States financial crisis, the Great Recession, and the subsequent weak recovery have brought about a more serious focus on income inequality and the widening income gap between the top one percent and other groups. These events have brought about some social unrest and instability in American society perhaps not seen since the Great Depression. How much has the top 1 percent of households gained in terms of income versus the other 99 percent in the United States over the last 30 years or so? Mainstream economists and other social scientists point to various causes which have been mentioned in many scholarly and popular writings. All of these mainstream factors affecting inequality have been found to be statistically significant in one scholarly study or another. This research paper explores other major concepts to explain income inequality rather than to dispute the findings of other existing research efforts. The empirical findings of this paper support radical arguments that income accumulation of those at the top is not connected to the productivity of capital investment, but rather instead is connected to the declining incomes and exploitation of the rest of the US population despite the rising output per worker of the US workforce over the last 30 to 40 years

PCN214 Expert Elicitation Used for Early Technology Assessment to Inform on Cost-Effectiveness of Next Generation Sequencing

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