Developing new frameworks to value genomic information: accounting for complexity.

Current frameworks & their limitations Decisions have to be made about how to allocate the finite resources available to healthcare systems. Frameworks exist that can aid decisions about whether a new healthcare intervention should be approved for use in a healthcare system. These frameworks are typically underpinned by a formal approach to economic evaluation called cost– effectiveness analysis (CEA) and are primarily driven by the tenet that population health should be maximized. In a CEA, two or more interventions are assessed in terms of their costs and outcomes to determine the relative cost–effectiveness of an intervention compared with its alternatives. To enable comparisons between different types of healthcare interventions in a CEA, it is preferable to use a standard, common outcome measure: the qualityadjusted life-year (QALY). Mortality effects and quality of life attributable to ‘health’ are captured by the QALY with ‘health’ being narrowly defined by the scope of recommended survey tools such as the EQ-5D [1]. Generally speaking, the frameworks have proved useful for resource allocation decision making and have been successfully adopted in a number of jurisdictions across the world. There are, however, specific situations in which QALY-based frameworks, as currently applied, are inadequate. Notably, limitations have been identified where genomic information forms part of a complex intervention, for example, in precision medicine initiatives [2,3]. Interventions which utilize genomic information are complex, in that multiple elements combine to produce multifaceted outcomes. Importantly, these outcomes can extend beyond the confines of ‘health’ as conceptualized in the QALY.