Abstract
Hospitals increasingly use process models for structuring their care processes. Activities performed to patients are logged to a database but these data are rarely used for managing and improving the efficiency of care processes and quality of care. In this paper, we propose a synergy of process mining with data discovery techniques. In particular, we analyze a dataset consisting of the activities performed to 148 patients during hospitalization for breast cancer treatment in a hospital in Belgium. We expose multiple quality of care issues that will be resolved in the near future, discover process variations and best practices and we discover issues with the data registration system. For example, 25 % of patients receiving breast-conserving therapy did not receive the key intervention "revalidation”. We found this was caused by lowering the length of stay in the hospital over the years without modifying the care process. Whereas the process representations offered by Hidden Markov Models are easier to use than those offered by Formal Concept Analysis, this data discovery technique has proven to be very useful for analyzing process anomalies and exceptions in detail.
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References
Anyanwu, K., Sheth, A., Cardoso, J., Miller, J., Kochut, K.: Healthcare enterprise process development and integration. Journal of research and practice in information technology 35(2), 83–98 (2003)
Blum, T., Padoy, N., Feussner, H., Navab, N.: Workflow mining for visualization and analysis of surgery. International journal of computer assisted radiology and surgery 3 Suppl. 1, (June 2008)
Campbell, H., Hotchkiss, R., Bradshaw, N., Porteous, M.: Integrated care pathways. British Medical Journal 316, 133–137 (1998)
Ferreira, D., Zacarias, M., Malheiros, M., Ferreira, P.: Approaching process mining with sequence clustering: experiments and findings. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 360–374. Springer, Heidelberg (2007)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)
Mans, R.S., Schonenberg, M.H., Song, M., Aalst, W.M.P., Bakker, P.J.M.: Application of process Mining in health care - a case study in a Dutch hospital. In: Biomedical engineering systems and technologies, International Joint conference, BIOSTEC 2008, Funchal, Madeira, Portugal, January 28-31, Springer, Heidelberg (2008)
Maruster, L., Van der Aalst, W.M.P., Weijters, A.J.M.M., Van der Bosch, A., Daelemans, W.: Automated discovery of workflow models from hospital data. In: Proceedings of the ECAI workshop on knowledge discovery and spatial data, pp. 183–190 (2002)
Maruster, L., Weijters, A.J.M.M., Van der Aalst, W.M.P., Van den Bosch, A.: A rule-based approach for process discovery dealing with noise and imbalance in process logs. Data mining and knowledge discovery 13 (2006)
Murphy, K.: Hidden Markov Model (HMM) MATLAB toolbox (1998), http://people.cs.ubc.ca/~murphyk/Software/HMM/hmm.html
Peters, E.M., Dedene, G., Houck, C.: Business Process Discovery and Workflow Intelligence Techniques with healthcare applications. In: Proceedings of the INFORMS ORAHS 2009 Conference, Leuven (2009), http://www.econ.kuleuven.be/eng/tew/academic/prodbel/ORAHS2009/page5.htm
Poelmans, J., Elzinga, P., Viaene, S., Dedene, G.: A case of using formal concept analysis in combination with emergent self organizing maps for detecting domestic violence. In: Perner, P. (ed.) Advances in Data Mining. Applications and Theoretical Aspects. LNCS, vol. 5633, pp. 247–260. Springer, Heidelberg (2009)
Quaglini, S.: Process Mining in Healthcare: A Contribution to Change the Culture of Blame. In: Book Business Process Management Workshops. LNBIP, vol. 17, pp. 308–311. Springer, Heidelberg (2009)
Rabiner, L.R.: A tutorial on Hidden Markov Models and selected applications in speech recognition. Proceedings IEEE 77(2), 257–286 (1989)
Skinner, J., Chandra, A., Goodman, D., Elliot, S.F.: The elusive connection between health care spending and quality. Health Affairs - Web Exclusive w119-w123 (2008)
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Poelmans, J., Dedene, G., Verheyden, G., Van der Mussele, H., Viaene, S., Peters, E. (2010). Combining Business Process and Data Discovery Techniques for Analyzing and Improving Integrated Care Pathways. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2010. Lecture Notes in Computer Science(), vol 6171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14400-4_39
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DOI: https://doi.org/10.1007/978-3-642-14400-4_39
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