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data, information, and knowledge management methods) are not well aligned with
the P4 paradigm, thus impeding the implementation of the model [
3
-
5
,
15
,
16
].
As can be readily ascertained, the continuum of data, information, and knowl-
edge management that is central to the premises underlying P4 medicine is directly
aligned with the emerging and “working” central dogma for Biomedical Informatics
that we have previously described. Unfortunately, current approaches to basic sci-
ence research, clinical care, and biomedical informatics are often poorly integrated,
yielding clinical decision-making processes that do not take advantage of up-to-date
scientifi c knowledge and capabilities afforded by Biomedical Informatics theories
and method [
5
,
12
,
15
]. There are an increasing number of systems modelling and
in-silico knowledge synthesis techniques that can provide investigators with the
tools to address such information needs, but their adoption and evaluation remains
an area of early and open research [
3
,
4
,
8
,
12
,
16
]. Given increasing concerns over
barriers to translating discoveries from the laboratory to the clinic or community,
such high-throughput informatics methods are highly desirable, and in our opinion,
central to the P4 paradigm [
4
,
6
-
8
,
12
]. As such, the on-going evolution in precision
or “P4” medicine has been and continues to be focused on overcoming fundamental
barriers that serve to prevent or impede rapid and systematic translation between
research and clinical care. Such barriers include a lack of unifi cation between data
generation environments, as regularly occurs in the laboratory, clinical, and com-
munity settings, and knowledge generation, which is the fundamental pursuit of
research. The lack of unifi cation is attributable to a number of factors as introduced
previously, including innate technical limitations to current clinical decisions sup-
port systems, socio-technical and regulatory barriers, as well as a lack of suffi ciently
robust and widely adopted informatics platforms intended to “shorten the distance”
between data and knowledge generation [
3
,
4
]. Such a state of affairs is both prom-
ising, in terms of the potential benefi ts of precision or “P4” medicine that can be
enabled through the use of Biomedical Informatics theories and methods, and also
challenging, given a landscape that remains somewhat misaligned with this vision
for the future of knowledge-driven healthcare.
1.3.3
The Role of “Big Data” in Biomedicine
Finally, and of note, there is an expanding focus in a variety of technical and
information-intensive domains on what has been called “Big Data.” In contemporary
discussions of trends in “Big Data”, it has been argued that the defi nitional character-
istics of a data set that is “Big” can be summarized via the three “Vs” [
17
,
18
], namely:
Volume
: the data set is of suffi ciently large in scale or volume that it requires
specialized collection, storage, transaction, and/or analysis methods
;
Velocity
: the speed with which the data is generated is such that it requires spe-
cialized collection, storage, or transaction technologies
;
and
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