TY - GEN
T1 - Realising the knowledge spiral in healthcare
T2 - 5th Annual ICMCC event on Medical Care and Compunetics: Patient Empowerment - The Power of Information, ICMCC 2008
AU - Wickramasinghe, Nilmini
AU - Bali, Rajeev K.
AU - Gibbons, M. Chris
AU - Schaffer, Jonathan
PY - 2008
Y1 - 2008
N2 - Knowledge Management (KM) is an emerging business approach aimed at solving current problems such as competitiveness and the need to innovate which are faced by businesses today. The premise for the need for KM is based on a paradigm shift in the business environment where knowledge is central to organizational performance [1]. Organizations trying to embrace KM have many tools, techniques and strategies at their disposal. A vital technique in KM is data mining which enables critical knowledge to be gained from the analysis of large amounts of data and information. The healthcare industry is a very information rich industry. The collecting of data and information permeate most, if not all areas of this industry; however, the healthcare industry has yet to fully embrace KM, let alone the new evolving techniques of data mining. In this paper, we demonstrate the ubiquitous benefits of data mining and KM to healthcare by highlighting their potential to enable and facilitate superior clinical practice and administrative management to ensue. Specifically, we show how data mining can realize the knowledge spiral by effecting the four key transformations identified by Nonaka [2] of turning: (1) existing explicit knowledge to new explicit knowledge, (2) existing explicit knowledge to new tacit knowledge, (3) existing tacit knowledge to new explicit knowledge and (4) existing tacit knowledge to new tacit knowledge. This is done through the establishment of theoretical models that respectively identify the function of the knowledge spiral and the powers of data mining, both exploratory and predictive, in the knowledge discovery process. Our models are then applied to a healthcare data set to demonstrate the potential of this approach as well as the implications of such an approach to the clinical and administrative aspects of healthcare. Further, we demonstrate how these techniques can facilitate hospitals to address the six healthcare quality dimensions identified by the Committee for Quality Healthcare [3].
AB - Knowledge Management (KM) is an emerging business approach aimed at solving current problems such as competitiveness and the need to innovate which are faced by businesses today. The premise for the need for KM is based on a paradigm shift in the business environment where knowledge is central to organizational performance [1]. Organizations trying to embrace KM have many tools, techniques and strategies at their disposal. A vital technique in KM is data mining which enables critical knowledge to be gained from the analysis of large amounts of data and information. The healthcare industry is a very information rich industry. The collecting of data and information permeate most, if not all areas of this industry; however, the healthcare industry has yet to fully embrace KM, let alone the new evolving techniques of data mining. In this paper, we demonstrate the ubiquitous benefits of data mining and KM to healthcare by highlighting their potential to enable and facilitate superior clinical practice and administrative management to ensue. Specifically, we show how data mining can realize the knowledge spiral by effecting the four key transformations identified by Nonaka [2] of turning: (1) existing explicit knowledge to new explicit knowledge, (2) existing explicit knowledge to new tacit knowledge, (3) existing tacit knowledge to new explicit knowledge and (4) existing tacit knowledge to new tacit knowledge. This is done through the establishment of theoretical models that respectively identify the function of the knowledge spiral and the powers of data mining, both exploratory and predictive, in the knowledge discovery process. Our models are then applied to a healthcare data set to demonstrate the potential of this approach as well as the implications of such an approach to the clinical and administrative aspects of healthcare. Further, we demonstrate how these techniques can facilitate hospitals to address the six healthcare quality dimensions identified by the Committee for Quality Healthcare [3].
KW - Data mining
KW - Explicit knowledge
KW - Healthcare
KW - Knowledge management
KW - Knowledge spiral
KW - Tacit knowledge
UR - http://www.scopus.com/inward/record.url?scp=52649107613&partnerID=8YFLogxK
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M3 - Conference contribution
C2 - 18560077
AN - SCOPUS:52649107613
SN - 9781586038687
T3 - Studies in Health Technology and Informatics
SP - 147
EP - 162
BT - Medical and Care Compunetics 5
PB - IOS Press
Y2 - 9 June 2008 through 11 June 2008
ER -