Manchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, The University of Manchester, Manchester, M13 9PL, UK.
Nat Rev Genet. 2018 Apr;19(4):235-246. doi: 10.1038/nrg.2017.108. Epub 2018 Jan 22.
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.
下一代测序技术的发展推动了诊断检测的临床应用，这些检测可以全面检测基因组，从而有机会诊断罕见的遗传性疾病或为治疗提供靶向信息。新的基因组诊断测试与传统的诊断方法（包括单基因遗传测试和其他临床策略）竞争有限的医疗保健预算。在这种情况下，基于决策分析模型的成本效益分析是一种有用的方法，可以帮助评估引入新的医疗保健干预措施的成本与后果。本观点提出了决策者在分配医疗资源时必须考虑的关键方法学、技术、实际和组织方面的挑战，以获取关于不断涌现的基因组检测相对成本效益的可靠和及时信息。
