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Healthcare System” [ 8 ] in which an “Evidence Generating Medicine” [ 9 ] paradigm
is adhered to. In this model:
￿
Clinicians, patients, family members and community entities populate a
healthcare “ecosystem” ;
￿
All of the interactions between the individuals and entities that exist in the
“ecosystem” are characterized using Biomedical Informatics theories,
methods and technologies , resulting in the population of numerous accessible
and reusable information resources such as EHRs, Personalized Health Records
(PHRs), emergent data generators such as the sensors and ubiquitous computing
devices described earlier, and a variety of public data and information
repositories;
￿
These foundational resources in-turn catalyze evidence-generation via the
conduct of clinical and translational science programs that involve
multi-disciplinary teams of researchers, educators and policy makers, all of whom
can use the knowledge gained from such research to quickly inform their respec-
tive roles in terms of advancing science, educating the healthcare workforce or
population-at-large, and informing large-scale policies and best practices; and
￿
Such evidence generation supports and enables a systems-level approach to
analytics, the creation and delivery of actionable knowledge that can be delivered
based on patient-specifi c characteristics, and the instantiation of decision support
tools that bring the best possible knowledge to the point-of-care and wellness
promotion, such as in the patients home or in community settings. This type of
systems thinking ultimately delivers on the promise of personalized medicine
[ 10 ], and informs the healthcare “ecosystem” introduced earlier.
Finally, and perhaps most importantly, all of these activities occur within a virtu-
ous and rapid cycle (Fig. 2.3 ), via which every encounter that occurs in the health-
care “ecosystem” is an opportunity to learn and improve the care and wellness
promotion delivered to patients, their families and their communities, building upon
all of the preceding components of the “Learning Healthcare System”.
2.4
Conclusions
The fundamental vision for Translational Informatics (TI), which has been intro-
duced and elaborated upon in the fi rst two chapters of this topic, is predicated on the
interaction of three critical and synergistic dimensions, namely:
1. The promise of clinical and translational research in the biomedical and health-
care domains;
2. The adoption and utilization of systems thinking approaches; and
3. The application of an emergent central dogma for the broad domain of Biomedical
Informatics concerned with bridging the gaps between data, information and
knowledge.
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