Benefits of diagnostics tests generally centre on test accuracy measures. However, additional benefits of diagnostics may include: reduced laboratory time, reduced time to results and increases in the capacity of a laboratory to deliver more tests. New technological developments, such as high throughput sequencing (HTS) are challenging the current methods used in establishing the case for the introduction into clinical practice terms of economic impact.  This is evident in the case study developed here examining BRCA1/2 genetic testing in providing information on the risk of development of breast cancer. Current BRCA1/2 testing technologies are limited by long (up to one-year) turnaround times, which together with limited resources to increase the volume of tests and associated genetic counselling, has driven the use of a ‘risk threshold’ to target women eligible for testing. HTS offers the opportunity of decreased turnaround time and increased volume of BRCA1/2 tests, which will impact on the benefits and costs associated with the diagnostic service. Systematic reviews have identified Markov-type models as the dominant modeling methodology for the assessment of genetic testing. We propose that discrete event simulation (DES) is the appropriate model type to quantify the economic impact of HTS BRCA1/2 testing as it allows evaluation of the impact of capacity constraints and increased turnaround time on the costs and benefits of this new diagnostic technology. Importantly, DES also allows for the assessment of structural uncertainty by considering changes in patient pathways when using a new diagnostic technology. While DES may be the most appropriate modeling methodology in assessing the economic impact of novel genetic tests; typically the type of data and information required to popuate these models in lacking. We conclude by highlighting the type of data required to both population appropriate models and to adequately assess the economic impact of these novel genetic tests.