Abstract
Early warning system (EWS) is a technology to mitigate risk in multi disciplinary areas. Issues on timeliness for timely reporting are still regarded as a main challenge in EWS. Therefore in this paper, we suggest the model of the integration between knowledge management system (KMS) and EWS known as KMS@EWS for clinical diagnostics (CD) environment. The integration model is to combine the advantage of KM system with the EWS functionalities and its components. The proposed model is based on empirical study by using literatures on KMS and EWS. We synthesize the findings of KMS and EWS model of integration. To demonstrate the application of this model, we propose into CD environment as a platform KMS@EWS system implementation. Our propose model can provide early warning when any abnormalities or peculiar pattern of disease and symptoms arise and detected during the interaction between physicians and patients. Thus, this model can support the managing of data and information for timely reporting and detection by providing decision facilitation thru early warning during the CD processes.
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Realdi, G., Previato, L., Vitturi, N.: Selection of diagnostic tests for clinical decision making and translation to a problem oriented medical record. Clinica Chimica Acta 393(1), 37–43 (2008)
Bravata, D., et al.: Systematic Review: Surveillance systems for early detection of bioterrorism-related diseases. Emerg. Infect. Dis. 10, 100–108 (2004)
Pavlin, J.A.: Investigation of disease outbreaks detected by syndromic surveillance systems. Journal of Urban Health: Bulletin of the New York Academy of Medicine 80(supplement 1), i107–i114 (2003)
Lombardo, J., et al.: A systems overview of the electronic surveillance system for the early notification of community-based epidemics (ESSENCE II). Journal of Urban Health: Bulletin of the New York Academy of Medicine 80(supplement 1), i32–i42 (2003)
Tsui, F., et al.: Technical description of RODS: a real-time public health surveillance system. Journal of the American Medical Informatics Association 10(5), 399 (2003)
Reis, B., et al.: AEGIS: a robust and scalable real-time public health surveillance system. British Medical Journal 14(5), 581 (2007)
Damianos, L., Zarrella, G., Hirschman, L.: The MiTAP System for Monitoring Reports of Disease Outbreak (2006)
Brownstein, J., Freifeld, C.: HealthMap: The development of automated real-time internet surveillance for epidemic intelligence. Euro Surveill 12(48), 3322 (2007)
Madoff, L.C.: ProMED-Mail: An Early Warning System for Emerging Diseases. Clinical Infectious Diseases 39(2), 227–232 (2004)
Chen, H.: Knowledge management systems: a text mining perspective (2001)
Abidi, S.S.R.: Knowledge management in healthcare: towards [] knowledge-driven’decision-support services. International Journal of Medical Informatics 63(1-2), 5–18 (2001)
Satyadas, A., Harigopal, U., Cassaigne, N.P.: Knowledge management tutorial: an editorial overview. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 31(4), 429–437 (2001)
Alavi, M., Leidner, D.E.: Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly 25(1), 107–136 (2001)
Choo, C.W.: The knowing organization: How organizations use information to construct meaning, create knowledge and make decisions* 1. International Journal of Information Management 16(5), 329–340 (1996)
Zack, M.H.: Managing codified knowledge. Sloan Management Review 40(4), 45–58 (1999)
Bukowitz, W.R., Williams, R.L.: The Knowledge Management Fieldbook. Prentice Hall, London (2000)
McElroy, M.W.: The New Knowledge Management: Complexity, Learning, and Sustainable Innovation. Butterworth Heinemann, Boston (2003)
Rudy, L., Ruggles, I.: Knowledge Management Tools. Butterworth Heinemann, Boston (1997)
Chua, A.: Knowledge management system architecture: a bridge between KM consultants and technologists. International Journal of Information Management 24(1), 87–98 (2004)
Kerschberg, L.: Knowledge management in heterogeneous data warehouse environments. Data Warehousing and Knowledge Discovery, 1–10 (2001)
Grasso, V.F., Beck, J.L., Manfredi, G.: Automated decision procedure for earthquake early warning. Engineering Structures 29(12), 3455–3463 (2007)
Austin, A.: Early Warning and The Field: A Cargo Cult Science? (2004)
ISDR, Hyogo Framework for Action 2005-2015: Building the Resilience of Nations and Communities to Disasters (2005)
Grasso, V.F., Singh, A.: Global Environmental Alert Service (GEAS). Advances in Space Research 41(11), 1836–1852 (2008)
Li, N., et al.: Developing a knowledge-based early warning system for fish disease/health via water quality management. Expert Systems with Applications 36(3), 6500–6511 (2009)
Gierl, L., Schmidt, R.: Geomedical warning system against epidemics. International Journal of Hygiene and Environmental Health 208(4), 287–297 (2005)
Ebi, K.L., Schmier, J.K.: A stitch in time: improving public health early warning systems for extreme weather events. Epidemiologic Reviews 27(1), 115 (2005)
Zhang, Y., et al.: Automatic online news monitoring and classification for syndromic surveillance. Decision Support Systems 47(4), 508–517 (2009)
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Noor, N.M., Abdullah, R., Selamat, M.H. (2011). A Model of Knowledge Management System and Early Warning System (KMS@EWS) for Clinical Diagnostic Environment. In: Mohamad Zain, J., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds) Software Engineering and Computer Systems. ICSECS 2011. Communications in Computer and Information Science, vol 179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22170-5_7
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DOI: https://doi.org/10.1007/978-3-642-22170-5_7
Publisher Name: Springer, Berlin, Heidelberg
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