Wickramasinghe Nilmini, Bali Rajeev K, Gibbons M Chris, Schaffer Jonathan
Center for the Management of Medical Technology, Stuart Graduate School of Business, Illinois Institute of Technology, Chicago, USA.
Stud Health Technol Inform. 2008;137:147-62.
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.
知识管理(KM)是一种新兴的商业方法,旨在解决企业当前面临的诸如竞争力和创新需求等问题。知识管理需求的前提基于商业环境中的范式转变,在这种环境中,知识是组织绩效的核心。试图采用知识管理的组织有许多工具、技术和策略可供使用。知识管理中的一项关键技术是数据挖掘,它能够通过对大量数据和信息的分析获得关键知识。医疗行业是一个信息非常丰富的行业。数据和信息的收集渗透到该行业的大部分(如果不是全部)领域;然而,医疗行业尚未充分采用知识管理,更不用说新出现的数据挖掘技术了。在本文中,我们通过强调数据挖掘和知识管理在实现卓越临床实践和行政管理方面的潜力,展示了它们对医疗保健的普遍益处。具体而言,我们展示了数据挖掘如何通过实现野中郁次郎所确定的四个关键转变来实现知识螺旋:(1)将现有显性知识转化为新的显性知识,(2)将现有显性知识转化为新的隐性知识,(3)将现有隐性知识转化为新的显性知识,以及(4)将现有隐性知识转化为新的隐性知识。这是通过建立理论模型来完成的,这些模型分别确定了知识螺旋的功能以及数据挖掘在知识发现过程中的探索性和预测性力量。然后,我们将我们的模型应用于一个医疗数据集,以展示这种方法的潜力以及这种方法对医疗保健临床和行政方面的影响。此外,我们展示了这些技术如何促进医院解决医疗质量委员会确定的六个医疗质量维度的问题。