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|Title:||Realising the knowledge spiral in healthcare: the role of data mining and knowledge management.|
|Epworth Authors:||Wickramasinghe, Nilmini|
|Other Authors:||Bali, Rajeev|
Information Storage and Retrieval
Quality of Healthcare
Chair of Health Informatics Management, Epworth HealthCare, Victoria, Australia
|Citation:||Stud Health Technol Inform. 2008;137:147-62|
|Abstract:||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 . 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 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.|
|Journal Title:||Studies in Health Technology and Informatics|
|Affiliated Organisations:||Center for the Management of Medical Technology, Stuart Graduate School of Business, Illinois Institute of Technology, Chicago, USA.|
|Appears in Collections:||Health Informatics|
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