What is Test Adoption?
This case study provides an overview of the core concepts of diagnostic adoption and its main drivers. It will also give you an understanding of how ...
Jordan Clark of Diaceutics comments on the American Journal of Managed Care article ‘Economics of Genomic Testing for Women With Breast Cancer’ and highlights that personalized medicine is being held back by the unwillingness of payers to reimburse genomic testing.
Robert D Lieberthal, PhD, from the Jefferson Population Health Continuing Professional Education Collaborative, describes in the American Journal of Managed Care (http://www.ajmc.com/publications/issue/2013/2013-1-vol19-n12/economics-of-genomic-testing-for-women-with-breast-cancer/4) what he believes to be the first structured review of the economics of breast cancer care. The NIH estimates that the annual financial burden of breast cancer is around $14 billion. Lieberthal’s excellent review highlights what is generally agreed, namely that genomic testing and personalized medicine can reduce costs but that there is still inadequate reimbursement for genomic studies.
We wholeheartedly agree with the recommendation from Dr Lieberthal that more studies into the health economics of personalized medicine are urgently needed. In addition, the move towards inclusion of direct clinical trial data as the backbone of such studies would be an improvement on the current modeling algorithm approach. He eloquently visualizes the direct and indirect cost of breast cancer care and it is a stark reminder that genomic testing is only a small, but essential, gear in the patient care pathway.
Elsewhere in the Expert Insight titled [php snippet=34 param=”title=PM2.0&id=77&useTitle=true”] we discuss that in order for personalized medicine to fulfil its potential, we must start taking the long-term holistic view of health economics, rather than a short-term detrimental view, that will not fund genomic testing in the order of $100s. The cost of genomic testing pales into insignificance when compared to the potential direct and indirect cost savings of treating cancer using precision medicine.
Of course, we recognize that the fragmentation of stakeholders and inefficiencies of reimbursement systems currently supporting personalized medicine, and companion diagnostics in particular, are holding back a shift to PM2.0. However, it all starts with the data and this paper should ring alarm bells for us all given that even in breast cancer, where we now have some of the most advanced biomarker and risk assessment tools, we do not have the business model sorted out. Pity.