Health Technology Assessment (HTA) involves a range of processes and mechanisms that use scientific evidence to assess the quality, safety, efficacy, effectiveness and cost effectiveness of health services. The purpose of HTA is to provide information to understand the benefits and comparative value of health technologies and procedures. This information can be used by a range of stakeholders to inform policy, funding and clinical decisions, as well as assist consumers to make decisions.

However, the application of genomics to health care presents particular challenges to HTA processes, including: 

  • potential predictive nature of the risk of disease

  • the familial implications

  • clinical trial structures

  • the links between testing for genetic variations, which indicate the effectiveness of specific pharmaceuticals (co-dependent technology)

A number of entries in the catalogue of initiatives indicate a lack of resources to undertake the necessary data collection and evaluation of tests in individual countries and are looking for centralised evaluation and health technology assessment of genomic tests as well as dissemination of evidence of effectiveness and cost/benefit. 

There are a number of bodies that have been actively pursuing health technology assessment approaches specifically for genetic and genomic medicine and making their evidence publically available. The gene dossier process was used by the UK Genetic Testing Network’s (UKGTN) Genetic Test Evaluation Working Group for a number of years to provide an evidence base for the introduction of more than 600 tests into the NHS. 

Similarly, Clinical Utility Gene Cards (CUGCs) were used by the EuroGenTest initiative. CUGCs were disease-specific guidelines regarding the clinical utility of genetic testing. Clinical utility referred to the ability of a genetic test to significantly affect the clinical setting and patient outcome. Over 100 CUGCs are publically available with all finalised CUGCs published in the European Journal of Human Genetics.

Although CUGCs covered all elements relevant to assessing risks and benefits of genetic test applications, the approach did not include health economics studies or measures of the budget impacts of testing. The Medical Services Advisory Committee (MSAC) in Australia has developed a Clinical Utility Card (CUC) proforma for applications related to genetic testing for heritable mutations and is piloting these arrangements to assess the utility of germline genetic testing for broad disease areas, such as cancer, cardiovascular or mental illness. The approach is modelled on the CUGCs however, it is constructed from a clinical perspective of disease management rather than a single gene by gene approach. Further, a completed CUC provides an economic evaluation of testing clinically affected individuals and the marginal cost effectiveness of also testing family members (cascade testing) where appropriate. It also assesses the budgetary implications of testing. An example of a completed CUC is available from the assessment of genetic testing for hereditary mutations predisposing to cancer (breast and/or ovarian).

Despite the efforts of these entities to date, it seems there is still a lack of health technology assessments that demonstrate the value of genomic medicine. A structured review of comparative effectiveness research in genomic medicine by Phillips et al(2017) found very limited evidence of the effect of using genomic tests on health outcomes. This review identified, selected, and appraised systematic reviews conducted by technology assessment groups during the period from 2011 – 2015. This resulted in 21 reviews being analysed with cancer-related tests predominating and each of the 21 reviews found little or no evidence about the impact of genomic medicine on patient outcomes. Twelve reviews discussed the importance of patient-centered outcomes, although most noted the limited direct evidence about these outcomes.

Similarly, a systematic literature review by Schwarze et al (2018) summarised the current health economic evidence for whole exome sequencing (WES) and whole genome sequencing (WGS). This study found twenty-one economic evaluations between 2005 and 2016 of which 8 were full economic and 13 were partial economic evaluations. Of the eight full economic evaluations, two were cost utility analyses and six were cost-effectiveness analyses. The authors concluded that the current health economic evidence base to support the widespread use of WES and WGS in clinical practice is very limited with a key consequence being that it is difficult for health technology assessment agencies to efficiently allocate scarce health-care resources.

This current lack of evidence may reflect a lag period with many of the global initiatives included in the catalogue likely to generate more evidence of the efficacy and efficiency of genomic testing in a range of settings over the coming years. To keep track of the increasing number of qualitative and quantitative research papers in health economics and genomics the Health Economics Research Centre at the Nuffield Department of Population Health, University of Oxford has set up a database which is regularly updated.


The evaluation of genomic testing is raising a range of challenges for the current approaches to health technology assessment. As an example, traditionally, utility has been a clinical assessment of measures and endpoints related to medical care. However, individuals having genomic testing have been shown to be interested in results for a wider range of reasons (Kohler et al 2017). These reasons are being termed personal utility and include measures such as planning for the future and feelings of altruism. In order to fully assess the benefits and harms of genomic testing the existing methodology will need to be adapted to take account of these additional outcomes. 

Similarly, work is needed to define appropriate ways to measure the full range of outcomes including clinical and personal utility. Measures such as the quality-adjusted life year (QALY) which has been used to measure disease burden in economic evaluations to date may not be appropriate for some outcomes such as reproductive confidence and avoidance of other non-genetic tests as well as outcomes that accrue much later in the individuals, or their family’s life. 

There is also a challenge around dis-investing in non-sequencing tests and even some sequencing tests as new tests are shown to be more cost-effective than existing tests. 

The review by Love-Koh et al 2018 provides an overview of the challenges that precision medicine more broadly, including greater use of ‘omics’ data, will bring to the field of health technology assessment. The challenges include increases to the volumes of evaluations and within those evaluations, increases in the number of relevant interventions, comparators and populations encompassed by a single assessment. 

Resources on evaluation and health technology assessment:

Fahr P, Buchanan J, Wordsworth S. A Review of the Challenges of Using Biomedical Big Data for Economic Evaluations of Precision Medicine. Applied Health Economics and Health Policy. 2019.

Buchanan, J. & Wordsworth, S. Evaluating the Outcomes Associated with Genomic Sequencing: A Roadmap for Future Research. PharmacoEconomics Open (2018). https://doi.org/10.1007/s41669-018-0101-4

Phillips KA et al. Making genomic medicine evidence-based and patient-centered: a structured review and landscape analysis of comparative effectiveness research. Genet Med. 2017:Oct;19(10):1081-1091. https://doi.org/10.1038/gim.2017.21

Schwarze K, Buchanan J, Taylor JC, Wordsworth S. Are whole-exome and whole-genome sequencing approaches cost-effective? A systematic review of the literature. Genet Med. [Epub ahead of print, 15 Feb 2018]. http://doi.org/10.1038/gim.2017.247

Pitini E, De Vito C, Marzuillo C, D’Andrea E, Rosso A, Federici A, et al. How is genetic testing evaluated? A systematic review of the literature. EJHG. 2018;26(5):605-15. http://doi.org/10.1038/s41431-018-0095-5

Payne K, Gavan SP, Wright SJ, Thompson AJ. Cost-effectiveness analyses of genetic and genomic diagnostic tests. Nat Rev Genet. 2018;19(4):235-46. https://doi.org/10.1038/nrg.2017.108

Kohler JN, Turbitt E, Biesecker BB. Personal utility in genomic testing: a systematic literature review. European Journal of Human Genetics 25, 662-668 (2017). https://doi.org/10.1038/ejhg.2017.10

J. Love-Koh et al., The Future of Precision Medicine: Potential Impacts for Health Technology Assessment. PharmacoEconomics (2018). [Epub ahead of print, 13 July 2018]. https://doi.org/10.1007/s40273-018-0686-6

M. J. Khoury et al., A collaborative translational research framework for evaluating and implementing the appropriate use of human genome sequencing to improve health. PLoS Med 15(8), e1002631 (2018). https://doi.org/10.1371/journal.pmed.1002631