In systems that are complex and have ill-defined inputs and outputs, and in situations where insufficient data is gathered to permit exhaustive analysis of activity pathways, it is difficult to get at process descriptions. The complexity conceals patterns of activity, even to experts, and the system is resistant to statistical modelling because of its high dimensionality. Such is the situation in hospital emergency departments, as borne out by the paucity of process models for them despite the continued and vociferous efforts of experts over many years. In such complex and ill-defined situations, it may be possible to access fairly complete records of activities that have taken place. This is the case in many hospital emergency departments, where records are routinely kept of procedures that patients undergo. Extracting process definitions from these records by self organized clustering is neither a pure technical analysis, nor a completely social one, but rather somewhere between these extremes. This chapter describe use of Self Organised Feature Maps to reveal general treatment processes – actual work practices – that may be monitored, measured and managed.
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Agrawal, R., Imielinski, T., and Swami, A. (1993) Mining Association Rules Between Sets of Items in Large Databases, Paper presented at the ACM SIGMOD Conference on Management of Data, Washington, DC.
Agrawal, R., Gunopulos, D., and Leymann, F. (1998) Mining Process Models from Workflow Logs, Paper presented at the Sixth International Conference on Extending Database Technology, Valencia, Spain.
Angluin, D., and Smith, C. H. (1983) Inductive Inference: Theory and Methods, Computing Surveys 15 (3) 237–69.
Averill, R. F., Muldoon, J. M., Vertrees, J. C., Goldfield, N. I., Mullin, R. L., Fineran, E. C., Zhang, M. Z., Steinbeck, B., and Grant, T. (1998) The Evolution of Casemix Measurement Using Diagnosis Related Groups (Drgs). 3M Health Information Systems, Wallingford, CT.
Bandyopadhyay, S., and Maulik, U. (2001) Nonparametric Genetic Clustering: Comparison of Validity Indices, IEEE Transactions on Systems Man and Cybernetics Part C- Applications and Reviews 31 (1) 120–5.
Bezdek, J. C., and Pal, N. R. (1998) Some New Indexes of Cluster Validity, IEEE Transactions on Systems, Man and Cybernetics, Part B 28 (3) 301–15.
Bond, M., Baggoley, C., Erwich-Nijhout, M., and Phillips, D. (1998) Urgency, Disposition and Age Groups: A Casemix Model for Emergency Departments, Emergency Medicine Australasia 10 (2) 103–10.
Cameron, J., Baraff, L., and Sekhon, R. (1990) Case-Mix Classification for Emergency Departments, Medical Care 28 146–58.
Ceglowski, A., Churilov, L., and Wasserthiel, J. (2004) Process Mining Informed Industrial Engineering in Hospital Emergency Departments, Paper presented at the 5th Asia-Pacific Industrial Engineering and Management Systems Conference (APIEMS), Gold Coast, Australia.
Ceglowski, A., Churilov, L., and Wassertheil, J. (2007) Combining Data Mining and Discrete Event Simulation for a Value-Added View of a Hospital Emergency Department, Journal of the Operational Research Society 58 (2) 246–54.
Chou, C.-H., Su, M.-S., and Lai, E. (2003) A New Cluster Validity Measure for Clusters with Different Densities, Paper presented at the IASTED International Conference on Intelligent Systems and Control, Salzburg, Austria, June 25–27.
Coleridge, J. T., Cameron, P. A., White, J. B., and Epstein, J. (1993) Profile of Emergency Department Attendances, Emergency Medicine Australasia 5 (1) 18–25.
Cook, J. E., and Wolf, A. L. (1998) Discovering Models of Software Processes from Event-Based Data, ACM Transactions on Software Engineering and Methodology 7 (3) 215–49.
de Medeiros, A. K. A., van der Aalst, W. M. P., and Weijters, A. J. M. M. (2003) Workflow Mining: Current Status and Future Directions. In On the Move to Meaningful Internet Systems 2003: Coopis, Doa, and Odbase (Meersman, R., Tari, Z., and Schmidt, D. C., eds.), pp. 389–406. Springer, Berlin Heidelberg New York.
Djohan, V. (2002) Towards Integrated Clinical Process Modelling in an Acute Emergency Department, Information Technology, Monash University, Melbourne.
Duckett, S. J., Jackson, D., and Scully, B. (1997) Webpage: Paying for Hospital Emergency Care. Acute Health Division, Department of Human Services, Melbourne, http://www.dhs.vic.gov.au/ahs/archive/emerg/index.htm#Contents, Accessed 17 May.
Earl, M. J. (1994) The New and the Old of Business Process Redesign, The Journal of Strategic Information Systems 3 (1) 5–22.
Francis, R. L., McGinnis, L. F., and White, J. A. (1992) Facility layout and location: an analytical approach. Prentice Hall, Englewood Cliffs, NJ.
Gospodarevskaya, E., Churilov, L., and Wallace, L. (2005) Modeling the Patient Care Process of an Acute Care Ward in a Public Hospital: A Methodological Perspective, Paper presented at the HICSS, Hawaii, 3–7 Jan.
Han, J., and Kamber, M. (2001) Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco.
Harper, P. (2005) Combining Data Mining Tools with Health Care Models for Improved Understanding of Health Processes and Resource Utilization, Clinical and Investigative Medicine 28 (6) 338.
Huang, Z. (1998) Extensions to the K-Means Algorithm for Clustering Large Data Sets with Categorical Values, Data Mining and Knowledge Discovery 2 283–304.
IDS-Scheer (2004) Webpage: Aris Process Performance Manager. IDS-Scheer, http://www.ids-scheer.com, Accessed 13 October 2006.
Ishikawa, K. (1986) Guide to Quality Control (2nd revised ed.). Asian Productivity Organization, Tokyo.
Jain, A. K., Murty, M. N., and Flynn, P. J. (1999) Data Clustering: A Review, ACM Computing Surveys (CSUR) 31 (3) 264–323.
Jelinek, G. A. (1995a) Casemix Classification of Patients Attending Hospital Emergency Departments in Perth, Western Australia: Development and Evaluation of an Urgency-Based Casemix System. Doctor of Medicine, University of Western Australia, Perth.
Jelinek, G. A. (1995b) A Casemix Information System for Australian Hospital Emergency Departments. A Report to the Commissioner of Health, Western Australia, Perth.
Kaufman, L., and Rousseeuw, P. J. (1990) Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
Kennedy, R., Lee, Y., Van Roy, B., Reed, C., and Lippmann, R. (1998) Solving Data Mining Problems through Pattern Recognition. Prentice Hall, Englewood Cliffs, NJ.
Kohonen, T. (1995) Self-Organizing Maps. Springer, Berlin Heidelberg New York.
Konz, S. A. (1994) Facility Design: Manufacturing Engineering. Holcomb Hathaway, Scottsdale, AZ.
Kotonya, G., and Sommerville, I. (1998) Requirements Engineering: Processes and Techniques. Wiley, New York.
Laguna, M., and Marklund, J. (2005) Business Process Modeling, Simulation, and Design. Prentice Hall, Upper Saddle River, NJ.
Liaw, S.-T., Hill, T., Bryce, H., and Adams, G. (2001) Emergency and Primary Care at a Melbourne Hospital: Reasons for Attendance and Satisfaction, Australian Health Review 24 (2) 120–34.
Melan, E. H. (1993) Process Management: Methods for Improving Products and Service. McGraw-Hill: Copublished with ASQC Quality Press, New York.
Michie, D., Spiegelhalter, D. J., and Taylor, C. C. (1994) Machine Learning, Neural and Statistical Classification. Prentice Hall, Englewood Cliffs, NJ.
Muther, R. (1973) Systematic Layout Planning (2nd ed.). Cahners, Boston.
Orr, G., and Müller, K.-R. (1998) Neural Networks: Tricks of the Trade. Springer, Berlin Heidelberg New York.
Rennecker, J. (2004) Updating Ethnography to Investigate Contemporary Organisational Forms. In The Handbook of Information Systems Research (Whitman, M. E., and Woszczynski, A. B., eds.), pp. xi, 349. Idea, Hershey, PA.
Savage, S., Scholtes, S., and Zweidler, D. (2006) Probability Management, OR/MS Today 33 (1).
Schuler, D., and Namioka, A. (1993) Participatory Design: Principles and Practices. Lawrence Erlbaum, Hillsdale, NJ.
Sinreich, D., and Marmor, Y. N. (2004) A Simple and Intuitive Simulation Tool for Analyzing Emergency Department Operations, Paper presented at the 2004 Winter Simulation Conference, Washington, DC.
The Committee on the Future of Emergency Care in the United States Health System (2006) Hospital-Based Emergency Care: At the Breaking Point. Institute of Medicine (IOM), Washington, DC.
Theodoridis, S., and Koutroumbas, K. (1999) Pattern Recognition. Academic, San Diego.
van der Aalst, W. M. P., van Dongena, B. F., Herbst, J., Marustera, L., Schimm, G., and Weijters, A. J. M. M. (2003) Workflow Mining: A Survey of Issues and Approaches, Data & Knowledge Engineering 47 (2) 237–67.
Walley, P., Watt, A., Davies, C., Huang, A., and Ma, K. (2001) A Study of Demand for Emergency Access Health Services in Two UK Health Regions. Warwick Business School, Warwick.
Ward, J. (1963) Hierarchical Grouping to Optimise an Objective Function, Journal of the American Statistical Association 58 236–44.
Weerakkody, V., and Currie, W. (2003) Integrating Business Process Reengineering with Information Systems Development: Issues and Implications. In Business Process Management (Bpm2003) (van der Aalst, W. M. P., ter Hofstede, A. H. M., and Weske, M., eds.), pp. 302–20. Springer, Berlin Heidelberg New York.
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Ceglowski, A., Churilov, L. (2008). Using Self Organising Feature Maps to Unravel Process Complexity in a Hospital Emergency Department: A Decision Support Perspective. In: Phillips-Wren, G., Ichalkaranje, N., Jain, L.C. (eds) Intelligent Decision Making: An AI-Based Approach. Studies in Computational Intelligence, vol 97. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76829-6_13
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