A common vision is that large genotype datasets (up to whole genomes) will be routinely acquired in large numbers of patients and embedded in EHRs to be used by knowledgeable clinicians, supported by sophisticated clinical decision support systems.52 As described below, this idea is now being tested across multiple EHR systems for pharmacogenomic implementation. However, there are multiple major obstacles that need to be addressed as these systems are developed.53,54 Some are operational and may interface with policy issues such as storing and retrieving these large datasets, maximizing privacy, and ensuring that genetic data are not used in a discriminatory fashion in individuals (e.g. in non-health insurance) and across populations definable by genetic variation. Another is the general problem of developing and evaluating evidence that specific variants are “actionable”, a term taken to mean that their presence would in some way alter routine care. In pharmacogenetic implementation, this could be to avoid a drug or to change a drug dose, while genomic information could be used to intensify screening, undertake specific therapies, or to acquire long-term care insurance. Once a
