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translational informatics [
4
]. Nevertheless, the frequently cited 17-year lag for bio-
medical discoveries to make their way into widespread practice likely persists, and
there remains a great need for further progress in translational informatics to drive
improvements in science and resultant knowledge-driven healthcare [
5
].
10.1.1.1
Capitalizing on the Promise of Translation
The past several years have seen substantial ongoing investments and policy inter-
ventions designed to encourage the adoption of healthcare information technolo-
gies, establish translational science infrastructure, and accelerate both healthcare
and research. The goal of such efforts is to capitalize on scientifi c advances, com-
pare the effectiveness of diagnostic and therapeutic interventions, and ultimately
improve the quality and cost-effectiveness of healthcare [
6
,
7
]
.
Even with existing
investments and policies, the success of such initiatives will ultimately rely on addi-
tional and fundamental changes to the ways in which healthcare and biomedical
research are practiced. Indeed, the very relationship between healthcare delivery
and biomedical science still requires change before the ever-increasing amounts of
biomedical information can be leveraged to accelerate both science and practice.
10.1.1.2
Creating Learning Healthcare Systems
Fundamental to such advances is the creation of what the Institute of Medicine has
called a “learning health system” [
8
,
9
]. Indeed, simultaneous advances in the wide-
spread adoption of interoperable, standards-based EHRs, the infrastructure for con-
ducting translational science, and the movement toward healthcare reform in order to
improve quality and contain costs are helping to support the need for systematic
“learning” (or science) as part of routine healthcare [
10
,
11
]. While the increased col-
lection and availability of healthcare data facilitated by EHR adoption and so-called
“meaningful use” are necessary for creating such a learning health system, they are not
suffi cient. Many additional components, ranging from further technological advances,
to regulatory and policy changes at the governmental level, to fi scal and administrative
changes at the organizational level, and cultural shifts among the public will likely be
needed [
12
]. Once created and functional at local, regional and national levels, such a
learning health system will be essential to enabling translational informatics.
10.1.1.3
Addressing the Challenges and Opportunities of “Big Data”
and Precision Medicine
As the adoption and use of EHRs continues and as that use is coupled with the
simultaneous acceleration in the availability of vast amounts of non-EHR-based
data about patients (e.g. internet-based social information, information about
Internet usage including social media, and increasingly inexpensive and available
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