Development of a microcosting protocol to determine the economic cost of diagnostic genomic testing for rare diseases in Australia

Abstract

AbstractIntroductionGenomic testing is a relatively new, disruptive and complex health technology with multiple clinical applications in rare diseases, cancer and infection control. Genomic testing is increasingly being implemented into clinical practice, following regulatory approval, funding and adoption in models of care, particularly in the area of rare disease diagnosis. A significant barrier to the adoption and implementation of genomic testing is funding. What remains unclear is what the cost of genomic testing is, what the underlying drivers of cost are and whether policy differences contribute to cost variance in different jurisdictions, such as the requirement to have staff with a medical license involved in testing. This costing study will be useful in future economic evaluations and health technology assessments to inform optimal levels of reimbursement and to support comprehensive and comparable assessment of healthcare resource utilisation in the delivery of genomic testing globally.MethodsA framework is presented that focuses on uncovering the process of genomic testing for any given laboratory, evaluating its utilization and unit costs, and modelling the cost drivers and overall expenses associated with delivering genomic testing. The goal is to aid in refining and implementing policies regarding both the regulation and funding of genomic testing. A process-focused (activity-based) methodology is outlined, which encompasses resources, assesses individual cost components through a combination of bottom-up and top-down microcosting techniques and allows disaggregation of resource type and process step.Ethics and disseminationThe outputs of the study will be reported a relevant regional genetics and health economics conferences, as well as submitted to a peer-reviewed journal focusing on genomics.Article SummaryThis method uses key stakeholder interviews, health care resource utilisation and unit cost data collection for estimating the economic cost of diagnostic genomic testing in Australia.Advancements may include time in motion studies or greater reporting detail but with a trade-off in feasibility.Probabilistic modelling addresses uncertainty inherent in input estimates.The study does not incorporate variations in testing pathways, such as resequencing stored DNA costs or reanalysis of existing sequencing data.Automating ongoing monitoring of changes in genomic testing workflows, resource utilisation and costs would be beneficial.