Driving Clinical and Translational Research Using Biomedical Informatics - Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare - page 110

6.7 Conclusions

The timely and effi cient pursue of CTR is central to creating an evidence base that
can in turn enable the delivery of knowledge-driven healthcare, wherein the combi-
nation of quality, safety, and cost are all optimized. As can be seen in this chapter
and the referenced works, the application of BMI theories and methods has signifi -
cant and wide-ranging potential in terms of achieving such benefi t. As such, the
tight integration BMI and CTS is of the utmost importance in terms of achieving the
TI vision set forth in this topic.

Discussion Points

￿ What types of information needs characterize the CTR environment? How are
these requirements different that information needs encountered in the discrete
sub-disciplines that make up the overall CTR spectrum?
￿ How do major BMI method and theories map to the aforementioned information
needs? In what cases does such a mapping elucidate gaps in knowledge or prac-
tice that represent open areas of research and innovation?
￿ What barriers prevent the effective and timely application of BMI theories and
methods to address the needs of CTR teams?
￿ How does the increasing use of “big data” serve to infl uence or effect the preced-
ing factors?
￿ How do socio-cultural factors play a role in the effective integration of BMI
theories and methods with CTR related activities?

References

1. Sung NS, Crowley Jr WF, Genel M, Salber P, Sandy L, Sherwood LM, et al. Central challenges
facing the national clinical research enterprise. JAMA. 2003;289(10):1278-87. PubMed PMID:
12633190, Epub 2003/03/14. eng.
2. Payne PR, Embi PJ, Sen CK. Translational informatics: enabling high-throughput research
paradigms. Physiol Genomics. 2009;39(3):131-40. PubMed PMID: 19737991, Pubmed
Central PMCID: 2789669, Epub 2009/09/10. eng.
3. Embi PJ, Payne PR. Clinical research informatics: challenges, opportunities and defi nition for
an emerging domain. J Am Med Inform Assoc. 2009;16(3):316-27. PubMed PMID: 19261934.
Epub 2009/03/06. eng.
4. Chung TK, Kukafka R, Johnson SB. Reengineering clinical research with informatics.
J Investig Med. 2006;54(6):327-33. PubMed PMID: 17134616. eng.
5. Ash JS, Anderson NR, Tarczy-Hornoch P. People and organizational issues in research systems
implementation. J Am Med Inform Assoc. 2008;15(3):283-9. PubMed PMID: 18308986. ENG.
6. NIGMS. Defi nition of a clinical trial. Bethesda: National Institute of General Medical
Sciences; 2006 [cited 2006 2/25/2006].
7. Payne PR, Johnson SB, Starren JB, Tilson HH, Dowdy D. Breaking the translational barriers:
the value of integrating biomedical informatics and translational research. J Investig Med.
2005;53(4):192-200. PubMed PMID: 15974245.
8. Spilker B. Guide to clinical trials. New York: Raven; 1991. xxv, 1156 p.
9. Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the
hierarchy of research designs. N Engl J Med. 2000;342(25):1887-92. PubMed PMID: 10861325.