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ways to test for and store one's genomic information, etc.), great opportunities and
challenges will arise. Whatever the current capabilities of our computational infra-
structure, there comes a point where the volume, velocity, and/or variety of the data
present challenges to existing systems and users [ 13 ]. So-called big-data, while
thereby challenging to leverage, clearly present great opportunities for advancing
science and practice of healthcare.
A case in point involves the central promise of the genomics revolution - that of
precision (or genetically guided) medicine. While potentially quite powerful,
routine use of genomic information in the clinical setting has yet not come to pass.
A recent systematic review of precision medicine workfl ows cites three critical bar-
riers [ 14 ]. First, clinicians are poorly equipped to make sense of genomic data;
genomic competency represents a limited part of medical training and guidelines
change so rapidly they are often obsolete by the time a clinician has completed
training and enters practice. Second, genetic experts who can provide actionable
interpretations and who keep abreast of clinically relevant genomic discoveries are
a relative few and are often not available to assist. Finally, the growth of both
patient-specifi c genomic data via exome and genome sequencing as well as gener-
ated knowledge provides a unique challenge to existing electronic health record
(EHR) platforms that were not designed to manage such information. Clearly, it is
unreasonable to expect a single clinician to aggregate the needed information from
multiple sources, keep abreast of and integrate the knowledge-based information to
interpret the available data, and then render a cogent and appropriate diagnosis and
treatment plan. Yet, this is how our health system is currently designed to operate.
To address these issues and realize the benefi ts of the big data opportunities as
facilitated by translational informatics innovations, fundamental shifts in our health-
care paradigm may be needed.
10.1.2
Current State (Where Are We Today?)
10.1.2.1
Technical Capabilities
Today's technologies, while advanced relative to the past and improving on both
the clinical and research fronts [ 15 ], remain limited in certain ways related to the
capacity and functionality needed for translational informatics. For instance, a key
element to realizing a learning health system by enabling the kinds of team-science
and rapid translation needed involves data exchange. As the volume and velocity of
data expand, signifi cant advances in network capabilities will be needed across the
globe. Current efforts to create ubiquitous broadband communication capability is
a step in the right direction, though it will likely still not be suffi cient for wide-
spread “big data” sharing efforts. As a result, initiatives are now underway to
develop improved approaches to this problem [ 16 ]. Beyond the purely computa-
tional, other key technical capabilities including the development of services to
enable federated data sharing, secure transmission of data, and meta-data are
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