Driving Clinical and Translational Research Using Biomedical Informatics - Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare - page 106

motivating questions one might encounter relative to using systematic and extensible
data-analytic pipelining platforms can include:

1 . How can I ensure that my data analysis plan is both effi cient and reproducible ?
2 . Are there ways to reduce the workload associated with the repetitive analysis of
large - scale and multi - dimensional data sets ?
3 . Can I capture intermediate data products associated with my data analysis plans
so that I can perform quality assurance regarding such evaluative processes ?

6.3.4 Dissemination and Exchange of Knowledge Generated

Via Research Activities

It is a well-known phenomenon that the time period required to move a basic science
discovery into clinical research, and ultimately clinical or population-level practice
can span in excess of one or two decades [ 1 , 4 , 14 , 27 ]. As noted previously in this
chapter, numerous studies have identifi ed the dissemination or exchange of informa-
tion between various research and operational settings as one of the most pressing
issues contributing to such long research, development and implementation lifecycles
[ 14 ]. A wide variety of BMI tools and methods have been developed that are intended
to overcome these barriers, such as web-based communication and collaboration tools,
knowledge representation standards and platforms, public data and literature regis-
tries/databases and associated query and reporting tools, and evidence-based practice
tools such as guideline delivery systems and clinical decision support systems [ 4 , 7 ,
16 , 28 ]. However, current research and development concerning the implementation
and utilization of these types of informatics platforms tends to be focused on distinct
domains or settings, rather than conceptualizing and integrating them across the full
CTR spectrum. Furthermore, there are a plethora of socio-cultural challenges, includ-
ing human factors, workfl ow limitations, and historical or cultural norms of both
research and clinical activities, which serve to impede the deployment and use of such
BMI approaches and technologies. As such, the area of data, information, and knowl-
edge exchange across traditional organizational and disciplinary boundaries remains
an open and vigorous area of BMI research and development. Finally, and again
repeating the assessments in the previously described problem areas, and when taken
as a whole, the types of motivating questions one might encounter relative to the dis-
semination and exchange of knowledge generated via research activities can include:

1 . How do I communicate my laboratory fi ndings in a way that will support or
enable the rapid design of pre - clinical and or clinical studies informed by the
knowledge associated with such fi ndings ?
2 . Are there optimal ways to encode the data , information , and knowledge gener-
ated during clinical studies so as to accelerate their dissemination to clinical
practioners ?
3 . What is the best way to ensure that new clinical evidence or guidelines are rap-
idly adopted across a broad clinical or population - level spectrum ?