Knowledge elicitation approach in enhancing tacit knowledge sharing

S.L. Ting, W.M. Wong, Y.K. Tse, W.H. Yip

Research output: Contribution to journalArticlepeer-review


Purpose – The purpose of this paper is to present an automatic Medical Knowledge Elicitation
System (MediKES), which is designed to improve elicitation and sharing of tacit knowledge acquired
by physicians. The system leverages the clinical information stored in electronic medical record
systems, by representing the acquired information in a series of knowledge maps.
Design/methodology/approach – The system architecture of the proposed MediKES is first
discussed, and then a case study on an application of the proposed system in a Hong Kong medical
organization is presented to illustrate the adoption process and highlight the benefits that can be
realized from deployment of the MediKES.
Findings – The results of the case study show that the proposed solution is more reliable and
powerful than traditional knowledge elicitation approaches in capturing physicians’ tacit knowledge,
transforming it into a machine-readable form, as well as enhancing the quality of the medical
judgment made by physicians.
Practical implications – A prototype system has been constructed and implemented on a trial
basis in a medical organization. It has proven to be of benefit to healthcare professionals through its
automatic functions in representing and visualizing physicians’ diagnostic decisions.
Originality/value – Knowledge is key to improving the quality of the medical judgment of
physicians. However, researchers and practitioners are still striving for more effective ways of
capturing tacit knowledge and transforming it into a machine-readable form so as to enhance
knowledge sharing. In this paper, the authors reveal that the knowledge retrieval and the visual
knowledge representation functions of the proposed system are able to facilitate knowledge sharing
among physicians. Thus, junior physicians can use it as a decision support tool in making better
diagnostic decisions.
Original languageEnglish
Pages (from-to)1039
Number of pages1064
JournalIndustrial Management & Data Systems
Issue number7
Publication statusPublished - 2011

Cite this