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increased incidence of colon adenocarcinoma, a research program could be
launched with the multiple aims of:
1. Collecting and analyzing both bio-specimens and clinical phenotype data
from patients with either diagnosed colon cancer or who have family mem-
bers with such a diagnosis, in order to determine if rare genetic variants or
other clinical factors may be responsible for the higher than normal number
of cancer cases;
2. Simultaneously working with community members and organizations to
determine if additional socio-demographic or environmental factors that may
have been observed outside of the clinical setting may correlate with inci-
dence of colon adenocarcinoma; and
3. Leveraging Biomedical Informatics theories and methods to “package” the
information and knowledge gained from the data sets associated with items
(1) and (2) as well as that available in the public domain (e.g., literature,
public data sets, etc.) in order to deliver highly tailored clinical decision sup-
port to regional healthcare providers that would promote early screening
activities as well as personalized treatments for individuals at risk of or hav-
ing colon cancer that can be characterized using genomic, clinical, socio-
demographic and environmental factors. Such approaches are concerned with
capitalizing on the role of Biomedical Informatics as a conduit for translating
a variety of raw data sources into contextualized information and ultimately
actionable clinical knowledge.
Realizing the promise of translational science
: Achieving the vision articu-
lated above relative to the conduct and delivery of wide-ranging research activi-
ties that have immediate and demonstrable clinical actionability, is an example of
translational science in practice. However, such activities will require the coordi-
nation and collaboration of community members, clinicians, basic science
researchers (e.g., geneticists), biomedical informaticians, clinical researchers,
public health researchers and practioners, policy-makers, and funders. This type
of multidisciplinary team must be assembled and operated in a manner that over-
comes traditional “translational blocks” in order to rapidly move data, informa-
tion and knowledge between and among such individuals. Further, this type of
activity requires the design of clinical studies, care delivery guidelines and pub-
lic health interventions that are based on the best possible and integrated scien-
tifi c knowledge base. Findings and data should be rapidly translated between
parties involved in the establishment and tracking of such initiatives.
Employing systems thinking
: Finally, as opposed to a traditional view of clini-
cal care, research and public health in which those activities occur in at best a
loosely coordinated manner, the scenario described here requires a systems level
approach that bridges such “silos”. By quickly engaging the community, clini-
cians, researchers, public health professionals and policy makers in a team based
set of initiatives that combine large amounts of distributed and heterogeneous
data, information and knowledge, we are ultimately enabling the type of hypoth-
esis discovery and testing called for by a systems thinking model. Further, by
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