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OBJECTIVES: Whole genome sequencing (WGS) is a promising but complex technology. WGS is not yet widely used as a clinical diagnostic in oncology due to several barriers, such as the required infrastructure and expertise, costs, and unknown clinical utility. This study aimed to investigate possible future developments facilitating or impeding the implementation and adoption of WGS in this context. Exploring WGS development scenarios can inform strategic choices by assessing their likelihood of occurring and their consequences for the healthcare system. METHODS: Scenario drafting is an iterative process. First, a literature review was performed on potential barriers and facilitators related to the implementation of disruptive health technologies and WGS in particular. Second, the knowledge and opinions of national and international experts were used to prioritize these barriers and facilitators. Third, several of barriers or facilitators were combined into a set of coherent scenarios that each describe a possible future development. Fourth, these scenarios were validated internally and checked for plausibility by our research consortium. Fifth, expert opinion was elicited from a group of international experts. They were asked with which likelihood each scenario would take place in the next five years. Sixth, the data were pooled to create probabilities distributions for the likelihood of each scenario. RESULTS: Five domains were identified that are relevant to the implementation of WGS: technical, market access, clinical utility, and evidence generation, social, and reimbursement. Preliminary results indicate that experts consider the price of WGS, its clinical utility, and the turnaround time of WGS important aspects that can affect the implementation of WGS. Scenarios that describe a future development related to these aspects were created and probabilities were elicited. CONCLUSION: Possible future developments have been assessed on likelihood of occurring and these assessments can be used in strategic decision-making regarding the the implementation of WGS.

Objectives: More information is often thought to improve medical decision making, which may lead to overuse of relatively cheap tests. For diagnosing the underlying cause of aneamia in general practice, up to 14 different laboratory tests may be ordered by general practitioners (GPs), according to the Dutch guideline. This study assessed which of these 14 tests actually contribute to diagnosing an underlying cause of anaemia, and which subset of tests is optimal for establishing a correct diagnosis. Methods: Data from a previous questionnaire presenting GPs (n=139) with three varying real-world anaemia cases, and all 14 corresponding test results, was used. GPs selected the expected underlying cause (anaemia of chronic disease, iron deficiency anaemia, renal anaemia, 'unknown or other'). The correct underlying cause was established by an expert panel (i.e. GP, internist, clinical chemist). In this study, multinomial regression was used to determine the value of each test for establishing a particular underlying cause. Logistic regression was used to determine the value of each test for establishing the correct underlying cause. Stepwise backward selection using the Akaike Information Criterion was applied to determine the optimal subset of tests. Results: Only 9 (60%) of the laboratory tests, and patient age, contributed to diagnosing an underlying cause of anaemia (CRP, ESR, ferritin, folic acid, haemoglobin, leukocytes, MDRD, reticulocytes and serum iron). Diagnosing the correct underlying cause required just five (33%) tests (CRP, ferritin, folic acid, MCV and transferrin), and patient age. Conclusions: Of the full set of 14 laboratory tests recommended by the Dutch guideline, only a subset enhances the ability of the GP to diagnose an underlying cause of anaemia, from a statistical perspective. A subset of five tests has most added value. Extending this set with more tests may lead to lead to a decrease rather than increase in correct diagnoses.