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be great, if the majority of the data are undecipherable then they are of limited
value. Thus, within the array of data sources that may be the source of knowledge
within a learning healthcare system it is important to consider the quality and reli-
ability of data.
The challenges with data should not preclude the pursuit of eliciting knowledge.
Indeed, the methodologies that have been or will be developed to transform indi-
vidual datum points into potentially new knowledge will undoubtedly enable a para-
digm shift both in knowledge discovery and knowledge sharing. To bolster this
paradigm shift, the identifi cation of evaluation methodologies (and accepting their
potential shortcomings) will be crucial. Within the context of a learning healthcare
system, the development of new knowledge (i.e., testable hypotheses that can lead
to actionable events) will require the constant evaluation in light of real-world
health contexts. By accepting the shortcomings of a given methodological approach
for eliciting new knowledge, but identifying the potential advantages, the healthcare
system can learn and thus advance towards a step of identifying new wisdom that
can be used to inform subsequent decisions.
5.3.3
Making Biomedical Data Consumable for Populations
Beyond its roles in identifying new knowledge through computantional inferencing,
the process of bibliome mining can have more general implications. For consumers,
the role of bibliome mining has two major facets: (1) to support their providers
through the identifi cation of known facts and potentially new knowledge that can
lead to new therapies; and, (2) to provide insights into the realm of known informa-
tion and offer a means to identify potential suggestions to providers. Together, these
two facets refl ect the essential role that bibliome mining plays in light of the vol-
umes of biomedical data that need to be deciphered. Bibliome mining is necessary
by those directly working in biomedicine - from researchers to clinicians - as well
as those who benefi t from biomedical innovations - most notably, consumers.
As ubiquitous monitoring (often as part of the overall “quantifi ed self” move-
ment) becomes increasingly the norm, the leveraging of bibliome mining techniques
will become more relevant for the general public. For example, for an individual
who uses a tracker device (e.g . , a Fitbit [ 47 ]) as well as had their genome profi led
(e.g . , using 23andMe [ 48 ]) may benefi t from an understanding of what their per-
sonal data mean in light of published reports. For individual genes or disease condi-
tions, services like 23andMe has offered some insights to the meaning of results in
light of available literature (which has been subjected to a combination of biobliome
mining techniques and manual curation; at the time of this writing - March 2014-
this service has been suspended in response to an FDA warning letter). Prospectively,
the need for bibliome mining that can address general queries that span multiple
resources (e.g . , activity and genomic data) may help unveil meaningful insights that
can promote more overall positive health. The ultimate utility of quantifi ed self
technologies or knowledge inferred from biomedical literature for the general public
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