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.
Industrial Management & Data Systems – Emerald Publishing
Published: Aug 23, 2011
Keywords: Hong Kong; Medical diagnosis; Medical treatment; Doctors; Knowledge elicitation; Electronic medical records; Knowledge sharing; Concept map; Medical decision support
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