Knowledge elicitation approach in enhancing tacit knowledge sharing

– 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., – 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., – 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., – 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., – 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.

[1]  Jerome A. Osheroff,et al.  Bmc Medical Informatics and Decision Making Information Management to Enable Personalized Medicine: Stakeholder Roles in Building Clinical Decision Support , 2009 .

[2]  José Geraldo Pereira Barbosa,et al.  A proposed architecture for implementing a knowledge management system in the Brazilian National Cancer Institute , 2009 .

[3]  Naeema H. Jabr.  Physicians' attitudes towards knowledge transfer and sharing , 2007 .

[4]  Nicolino J. Pizzi,et al.  Information Processing in Biomedical Applications , 2009, Human-Centric Information Processing Through Granular Modelling.

[5]  Harleen Kaur,et al.  An Integrated Approach in Medical Decision-Making for Eliciting Knowledge , 2010, Web-Based Applications in Healthcare and Biomedicine.

[6]  Nancy J. Cooke,et al.  Knowledge Elicitation , 2003 .

[7]  Roberta Calderwood,et al.  Critical decision method for eliciting knowledge , 1989, IEEE Trans. Syst. Man Cybern..

[8]  Thompson Ml.  Characteristics of information resources preferred by primary care physicians , 1997 .

[9]  W. B. Lee,et al.  CASESIAN: A knowledge-based system using statistical and experiential perspectives for improving the knowledge sharing in the medical prescription process , 2010, Expert Syst. Appl..

[10]  Eva Hudlicka,et al.  Requirements elicitation with indirect knowledge elicitation techniques: comparison of three methods , 1996, Proceedings of the Second International Conference on Requirements Engineering.

[11]  William R. Hersh,et al.  BMC Medical Informatics and Decision Making , 2009 .

[12]  한인구.  THE IMPACT OF MEASUREMENT SCALE AND CORRELATION STRUCTURE ON CLASSIFICATION PERFORMANCE OF INDUCTIVE LEARNING AND STATISTICAL METHODS , 1991 .

[13]  Werner Dubitzky,et al.  Microarray Data Integration and Machine Learning Techniques For Lung Cancer Survival Prediction , 2003 .

[14]  M. Shaw,et al.  Using an Expert System with Inductive Learning to Evaluate Business Loans , 1988 .

[15]  Thomas E. McKee.  Predicting bankruptcy via induction , 1995, J. Inf. Technol..

[16]  M. Shah,et al.  Effects of Physician Experience on Costs and Outcomes on an Academic General Medicine Service: Results of a Trial of Hospitalists , 2002, Annals of Internal Medicine.

[17]  Michal Wozniak.  Two-Stage Classifier for Diagnosis of Hypertension Type , 2006, ISBMDA.

[18]  Jean-Baptiste Lamy,et al.  Using data mining techniques to explore physicians' therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes , 2009, BMC Medical Informatics Decis. Mak..

[19]  M L Thompson,et al.  Characteristics of information resources preferred by primary care physicians. , 1997, Bulletin of the Medical Library Association.

[20]  Martin Dawes,et al.  Knowledge management in clinical practice: a systematic review of information seeking behavior in physicians , 2003, Int. J. Medical Informatics.

[21]  Francis Lau,et al.  A study of information technology use and implementation of electronic medical record systems in BC medical practices , 2009 .

[22]  Richard T. Herschel,et al.  Tacit to explicit knowledge conversion: knowledge exchange protocols , 2001, J. Knowl. Manag..

[23]  S. Grefsheim,et al.  Information needs and information seeking in a biomedical research setting: a study of scientists and science administrators. , 2007, Journal of the Medical Library Association : JMLA.

[24]  Nigel Shadbolt,et al.  Knowledge Elicitation: A Systematic Approach , 1995 .

[25]  C. A. Kirkwood,et al.  Automatic detection of gait events: a case study using inductive learning techniques. , 1989, Journal of biomedical engineering.

[26]  Vassilis Koutkias,et al.  Semantic Integration of Cervical Cancer Data Repositories to Facilitate Multicenter Association Studies: The ASSIST Approach , 2009, Cancer informatics.

[27]  Kensaku Kawamoto,et al.  Bmc Medical Informatics and Decision Making a National Clinical Decision Support Infrastructure to Enable the Widespread and Consistent Practice of Genomic and Personalized Medicine , 2009 .

[28]  Donald R. Cooper,et al.  Business Research Methods , 1980 .

[29]  E. N. Corlett,et al.  Evaluation of Human Work , 2005 .

[30]  Tatjana Zrimec,et al.  Methods for Automatic Honeycombing Detection in HRCT images of the Lung , 2007 .

[31]  Nancy J. Cooke,et al.  Varieties of knowledge elicitation techniques , 1994, Int. J. Hum. Comput. Stud..

[32]  Brian R. Gaines,et al.  Eliciting Knowledge and Transferring It Effectively to a Knowledge-Based System , 1993, IEEE Trans. Knowl. Data Eng..

[33]  Stephen B. Johnson,et al.  Conceptual knowledge acquisition in biomedicine: A methodological review , 2007, J. Biomed. Informatics.

[34]  Clarence D Kreiter,et al.  An evaluation of information-seeking behaviors of general pediatricians. , 2004, Pediatrics.

[35]  David N. Ford,et al.  Expert knowledge elicitation to improve formal and mental models , 1998 .

[36]  Shin-Yuan Hung,et al.  Effect of computer self-efficacy on the use of executive support systems , 2001, Ind. Manag. Data Syst..

[37]  David N. Ford,et al.  Expert knowledge elicitation to improve mental and formal models , 1997 .

[38]  Reinhold Haux,et al.  Health information systems - past, present, future , 2006, Int. J. Medical Informatics.

[39]  Martin Dawes,et al.  Information Needs and Information-Seeking Behavior of Primary Care Physicians , 2007, The Annals of Family Medicine.

[40]  Alexandre A Ardichvili,et al.  Tacit knowledge in professional practice , 2000 .

[41]  Kauko Leiviskä,et al.  Knowledge acquisition for decision support systems on an electronic assembly line , 2009, Expert Syst. Appl..