Open Access
Yearb Med Inform 2014; 23(01): 154-162
DOI: 10.15265/IY-2014-0002
Original Article
Georg Thieme Verlag KG Stuttgart

IBM’s Health Analytics and Clinical Decision Support

M. S. Kohn
1   Jointly Health (formerly IBM Research)
,
J. Sun
2   College of Computing, Georgia Institute of Technology, Atlanta, Georgia (formerly IBM Research)
,
S. Knoop
3   IBM Almaden Research Center, San Jose, CA, USA
,
A. Shabo
4   Records of Health (formerly IBM Research), Haifa, Israel
,
B. Carmeli
5   IBM Haifa Research Lab, Haifa, Israel
,
D. Sow
6   IBM Watson Research Center, Yorktown Heights NY, USA
,
T. Syed-Mahmood
3   IBM Almaden Research Center, San Jose, CA, USA
,
W. Rapp
7   IBM Watson Solutions Development, Rochester MN, USA
› Institutsangaben
Weitere Informationen

Publikationsverlauf

15. August 2014

Publikationsdatum:
05. März 2018 (online)

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Summary

Objectives: This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making.

Methods: Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given.

Results: There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data.

Conclusion: Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation.