In modern healthcare systems, the available resources may influence the morbidity, mortality, and-consequently-the level of healthcare provided in every country. This is of particular interest in developing countries where the resources are limited and must be spent wisely to address social justice and the right for equal access in healthcare services by all the citizens in economically viable terms. In this light, the current allocation is, in practice, inefficient and rests mostly on each country's individual political and historical context and, thus, does not always incorporate decision-making enabled by economic models. In this study, we present a new economic model, specifically for resource allocation for genomic medicine, based on performance ratio, with potential applications in diverse healthcare sectors, which are particularly appealing for developing countries and low-resource environments. The model proposes a new method for resource allocation taking into account (1) the size of innovation of a new technology, (2) the relative effectiveness in comparison with social preferences, and (3) the cost of the technology, which permits the measurement of effectiveness to be determined differently in the context of a specific disease and then to be expressed in a relative form using a common performance ratio. The present work expands on previous work for innovation in economic models pertaining to genomic medicine and supports translational science.

Additional Metadata
Keywords developing world OMICS, economics, personalized medicine, public health genomics
Persistent URL,
Journal Omics : a journal of integrative biology
Grant This work was funded by the European Commission 7th Framework Programme; grant id h2020/668353 - Ubiquitous Pharmacogenomics (U-PGx): Making actionable pharmacogenomic data and effective treatment optimization accessible to every European citizen (U-PGx)
Fragoulakis, V. (Vasilios), Mitropoulou, C, Katelidou, D. (Daphne), van Schaik, R.H.N, Maniadakis, N, & Patrinos, G.P. (2017). Performance Ratio Based Resource Allocation Decision-Making in Genomic Medicine. Omics : a journal of integrative biology, 21(2), 67–73. doi:10.1089/omi.2016.0161