Developments in next-generation sequencing technologies have driven the clinical application of diagnostic tests that interrogate the whole genome, which offer the chance to diagnose rare inherited diseases or inform the targeting of therapies. New genomic diagnostic tests compete with traditional approaches to diagnosis, including the genetic testing of single genes and other clinical strategies, for finite health-care budgets. In this context, decision analytic model-based cost-effectiveness analysis is a useful method to help evaluate the costs versus consequences of introducing new health-care interventions. This Perspective presents key methodological, technical, practical and organizational challenges that must be considered by decision-makers responsible for the allocation of health-care resources to obtain robust and timely information about the relative cost-effectiveness of the increasing numbers of emerging genomic tests.

Introduction: The advancement of precision medicine into routine clinical practice has been highlighted as an agenda for national and international health care policy. A principle barrier to this advancement is in meeting requirements of the payer or reimbursement agency for health care. This special report aims to explain the economic case for precision medicine, by accounting for the explicit objectives defined by decision-makers responsible for the allocation of limited health care resources. Areas covered: The framework of cost-effectiveness analysis, a method of economic evaluation, is used to describe how precision medicine can, in theory, exploit identifiable patient-level heterogeneity to improve population health outcomes and the relative cost-effectiveness of health care. Four case studies are used to illustrate potential challenges when demonstrating the economic case for a precision medicine in practice. Expert commentary: The economic case for a precision medicine should be considered at an early stage during its research and development phase. Clinical and economic evidence can be generated iteratively and should be in alignment with the objectives and requirements of decision-makers. Programmes of further research, to demonstrate the economic case of a precision medicine, can be prioritized by the extent that they reduce the uncertainty expressed by decision-makers.

Objectives. To identify and quantify resource required and associated costs for implementing TNF-α inhibitor (TNFi) drug level and anti-drug antibody (ADAb) tests in UK rheumatology practice. Methods. A microcosting study, assuming the UK National Health Service perspective, identified the direct medical costs associated with providing TNFi drug level and ADAb testing in clinical practice. Resource use and costs per patient were identified via four stages: identification of a patient pathway with resource implications; estimation of the resources required; identification of the cost per unit of resource (2015 prices); and calculation of the total costs per patient. Univariate and multiway sensitivity analyses were performed using the variation in resource use and unit costs.Results. Total costs for TNFi drug level and concurrent ADAb testing, assessed using ELISAs on trough serum levels, were £152.52/patient (range: £147.68–159.24) if 40 patient samples were tested simultaneously. For the base–case analysis, the pre-testing phase incurred the highest costs, which included booking an additional appointment to acquire trough blood samples. The additional appointment was the key driver of costs per patient (67% of the total cost), and labour accounted for 10% and consumables 23% of the total costs. Performing ELISAs once per patient (rather than in duplicate) reduced the total costs to £133.78/patient.Conclusion. This microcosting study is the first assessing the cost of TNFi drug level and ADAb testing. The results could be used in subsequent cost-effectiveness analyses of TNFi pharmacological tests to target treatments and inform future policy recommendations.