Since the White House launched its Precision Medicine initiative in January 2015 there has been a great deal of buzz about personalized or precision medicine and the future of healthcare. “Personalized medicine” is an older term and is gradually falling by the wayside as critics think that it denotes a focus on the individual whereas precision medicine is more focused on which treatments work best for patients with a specific genetic, lifestyle or environmental context. The latter is more appropriate in the context of digital health where the growth in wearables, mHealth and even Population Health Management have become part of precision medicine initiatives. But once we settle upon a definition, what are the real challenges to making precision medicine a reality? Given the significant challenges associated with EHR implementations as well as interoperability challenges across a given community, how will healthcare begin to address an additional stakeholder whose datasets are much larger and bring these insights into the clinic in an actionable way? There are very few, if any, EHR examples today that have the capability of integrating, in a systematic way, genetic data in a format that can be readily used for treatment and therapeutic practice. This raises a number of important questions on when precision medicine can become reality in the clinic and what kind of strategic roadmap can be put into place to address the health IT issues.