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First Steps Towards Process Mining in Distributed Health Information Systems

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Języki publikacji
EN
Abstrakty
EN
Business Intelligence approaches such as process mining can be applied to the healthcare domain in order to gain insight into the complex processes taking place. Disclosing as-is processes helps identify room for improvement and answers questions from medical professionals. Existing approaches are based on proprietary log data as input for mining algorithms. Integrating the Healthcare Enterprise (IHE) defines in its Audit Trail and Node Authentication (ATNA) profile how real-world events must be recorded. Since IHE is used by many healthcare providers throughout the world, an extensive amount of log data is produced. In our research we investigate if audit trails, generated from an IHE test system, carry enough content to successfully apply process mining techniques. Furthermore we assess the quality of the recorded events in accordance with the maturity level scoring system. A simplified simulation of the organizational workflow in a radiological practice is presented. Based on this simulation a process mining task is conducted.
Twórcy
autor
  • Research Department of e-Health, Integrated Care, University of Applied Sciences Upper Austria, Austria, 4232, Hagenberg
autor
  • Research Department of e-Health, Integrated Care, University of Applied Sciences Upper Austria, Austria, 4232, Hagenberg
Bibliografia
  • [1] W. Van der Aalst, A. Adriansyah, A. K. A. de Medeiros, F. Arcieri, T. Baier, T. Blickle, J. C. Bose, P. van den Brand, R. Brandtjen, J. Buijs et al., “Process mining manifesto,” in Business process management workshops. Springer, 2012, pp. 169–194.
  • [2] W. Van der Aalst, Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, 2011. [Online]. Available: http://books.google.at/books?id=I1KOAfiqfxYC
  • [3] H. Verbeek, J. C. Buijs, B. F. Van Dongen, and W. M. Van Der Aalst, “Xes, xesame, and prom 6,” in Information Systems Evolution. Springer, 2011, pp. 60–75.
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  • [5] M. Lang, T. Bürkle, S. Laumann, and H.-U. Prokosch, “Process mining for clinical workflows: challenges and current limitations.” Studies in health technology and informatics, vol. 136, pp. 229–234, 2007.
  • [6] R. Mans, H. Schonenberg, G. Leonardi, S. Panzarasa, A. Cavallini, S. Quaglini, and W. van der AALST, “Process mining techniques: an application to stroke care,” Studies in health technology and informatics, vol. 136, p. 573, 2008.
  • [7] R. Mans, M. Schonenberg, M. Song, W. M. van der Aalst, and P. J. Bakker, “Application of process mining in healthcare–a case study in a dutch hospital,” in Biomedical Engineering Systems and Technologies. Springer, 2009, pp. 425–438.
  • [8] Á. Rebuge and D. R. Ferreira, “Business process analysis in healthcare environments: A methodology based on process mining,” Information Systems, vol. 37, no. 2, pp. 99–116, 2012.
  • [9] L. Perimal-Lewis, S. Qin, C. Thompson, and P. Hakendorf, “Gaining insight from patient journey data using a process-oriented analysis approach,” in Proceedings of the Fifth Australasian Workshop on Health Informatics and Knowledge Management-Volume 129. Australian Computer Society, Inc., 2012, pp. 59–66.
  • [10] E. L. Siegel and D. S. Channin, “Integrating the healthcare enterprise: A primer: Part 1. introduction 1,” Radiographics, vol. 21, no. 5, pp. 1339–1341, 2001.
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  • [15] IHE, “Audit trail and node authentication (atna), ihe it infrastructure (iti) technical framework volume 1 (iti-tf-1) integration profiles,” pp. 68–81, 2014.
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  • [24] W. Van der Aalst, T. Weijters, and L. Maruster, “Workflow mining: Discovering process models from event logs,” Knowledge and Data Engineering, IEEE Transactions on, vol. 16, no. 9, pp. 1128–1142, 2004.
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Bibliografia
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