IBM’s Health Analytics and Clinical Decision Support

M. S. Kohn; J. Sun; S. Knoop; A. Shabo; B. Carmeli; D. Sow; T. Syed-Mahmood; W. Rapp

doi:10.15265/IY-2014-0002

Yearbook of Medical Informatics, Table of Contents

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

¹Jointly Health (formerly IBM Research)

,

J. Sun

²College of Computing, Georgia Institute of Technology, Atlanta, Georgia (formerly IBM Research)

,

S. Knoop

³IBM Almaden Research Center, San Jose, CA, USA

,

A. Shabo

⁴Records of Health (formerly IBM Research), Haifa, Israel

,

B. Carmeli

⁵IBM Haifa Research Lab, Haifa, Israel

,

D. Sow

⁶IBM Watson Research Center, Yorktown Heights NY, USA

,

T. Syed-Mahmood

³IBM Almaden Research Center, San Jose, CA, USA

,

W. Rapp

⁷IBM Watson Solutions Development, Rochester MN, USA

› Author Affiliations

Abstract

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.

Keywords

Big Data - healthcare transformation - evidence-supported decision making - healthcare analytics

Full Text

References

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