Abstract
Since healthcare processes are pre-eminently heterogeneous and multi-disciplinary, information systems supporting these processes face important challenges in terms of design, implementation and diagnosis. Nonetheless, streamlining clinical pathways with the purpose of delivering high quality care while at the same time reducing costs is a promising goal. In this paper, we propose a methodology founded on process mining for intelligent analysis of clinical pathway data. Process mining can be considered a valuable approach to obtain a better understanding about the actual way of working in human-centric processes such as clinical pathways by investigating the event data as recorded in healthcare information systems. However, capturing tangible knowledge from clinical processes with their ad hoc and complex nature proves difficult. Accordingly, this paper proposes a data analysis methodology focussing on the extraction of tangible insights from clinical pathway data by adopting both a drill up and a drill down perspective.
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References
OECD: OECD health data 2010: Statistics and indicators (2010), http://www.oecd.org/health/healthdata
Kohn, L.T., Corrigan, J.M., Donaldson, M.S.: To Err Is Human: Building a Safer Health System. The National Academies Press, Washington DC (2000); Committee on Quality of Health Care in America, Institute of Medicine
van der Aalst, W.M.P.: Process Mining - Discovery, Conformance and Enhancement of Business Processes. Springer (2011)
Weske, M.: Business Process Management: Concepts, Languages, Architectures. Springer (2007)
Dumas, M., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Process-Aware Information Systems: Bridging People and Software through Process Technology. John Wiley & Sons, Inc. (2005)
Lenz, R., Reichert, M.: It support for healthcare processes - premises, challenges, perspectives. Data Knowl. Eng. 61(1), 39–58 (2007)
Anyanwu, K., Sheth, A.P., Cardoso, J., Miller, J.A., Kochut, K.: Healthcare enterprise process development and integration. Journal of Research and Practice in Information Technology 35(2), 83–98 (2003)
Lenz, R., Elstner, T., Siegele, H., Kuhn, K.A.: A practical approach to process support in health information systems. Journal of the American Medical Informatics Association 9(6), 571–585 (2002)
Reijers, H.A., Russell, N., van der Geer, S., Krekels, G.A.M.: Workflow for healthcare: A methodology for realizing flexible medical treatment processes. In: [24], pp. 593–604
van der Aalst, W.M.P., Weijters, A.J.M.M., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)
Alves de Medeiros, A.K., Weijters, A.J.M.M., van der Aalst, W.M.P.: Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery 14(2), 245–304 (2007)
Weijters, A.J.M.M., van der Aalst, W.M.P., Alves de Medeiros, A.K.: Process mining with the heuristicsminer algorithm. BETA Working Paper Series 166, TU Eindhoven (2006)
Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust process discovery with artificial negative events. Journal of Machine Learning Research 10, 1305–1340 (2009)
Song, M., van der Aalst, W.M.P.: Towards comprehensive support for organizational mining. Decision Support Systems 46(1), 300–317 (2008)
Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)
Mans, R.S., Schonenberg, H., Song, M., van der Aalst, W.M.P., Bakker, P.J.M.: Application of Process Mining in Healthcare - A Case Study in a Dutch Hospital. In: Fred, A.L.N., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2008. CCIS, vol. 25, pp. 425–438. Springer, Heidelberg (2008)
Rebuge, Á., Ferreira, D.R.: Business process analysis in healthcare environments: A methodology based on process mining. Information Systems 37(2), 99–116 (2012)
Bose, R.P.J.C., van der Aalst, W.M.P.: Analysis of patient treatment procedures. In: [23], pp. 165–166
Caron, F., Vanthienen, J., De Weerdt, J., Baesens, B.: Advanced care-flow mining and analysis. In: [23], pp. 167–168
Günther, C.W.: Process Mining in Flexible Environments. PhD thesis, TU Eindhoven (2009)
Bose, R.P.J.C., van der Aalst, W.M.P.: Trace clustering based on conserved patterns: Towards achieving better process models. In: [24], pp. 170–181
Hu, Y.: Algorithms for Visualizing Large Networks. In: Naumann, U., Schenk, O. (eds.) Combinatorial Scientific Computing (to appear)
Daniel, F., Barkaoui, K., Dustdar, S. (eds.): BPM Workshops 2011, Part I. LNBIP, vol. 99. Springer, Heidelberg (2012)
Rinderle-Ma, S., Sadiq, S.W., Leymann, F. (eds.): BPM 2009. LNBIP, vol. 43. Springer, Heidelberg (2010)
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De Weerdt, J., Caron, F., Vanthienen, J., Baesens, B. (2013). Getting a Grasp on Clinical Pathway Data: An Approach Based on Process Mining. In: Washio, T., Luo, J. (eds) Emerging Trends in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36778-6_3
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DOI: https://doi.org/10.1007/978-3-642-36778-6_3
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