Abstract
Business process mining algorithms discover processes from event logs that record sequences of events or actions. Typical event logs may or may not contain information about the attributes of the actions, such as the particular workstations used to carry out an action or the identity of the person performing the action. In this paper, we test the effect of action attributes on action sequence using data from electronic medical records at five dermatology clinics. We demonstrate that action sequence is influenced by attributes such as actors (who does what) and workstations (what is done where) that are not typically considered relevant to process flow control. We introduce a new metric – attribute alignment – that summarizes the extent to which actions are carried out with the same attributes throughout a process instance. If each action is always performed with the same attributes, attribute alignment is 100%. We discuss the implications and limitations of this finding for research and practice.
This material is based upon work supported by the National Science Foundation under Grant No. SES-1734237. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This research was also supported in part by University of Rochester CTSA (UL1 TR002001) from the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We are grateful for comments from Jan Recker and Jan Mendling on an early version of the analysis.
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Kim, I., Pentland, B.T., Wolf, J.R., Xie, Y., Frank, K., Pentland, A.P. (2019). Effect of Attribute Alignment on Action Sequence Variability: Evidence from Electronic Medical Records. In: Hildebrandt, T., van Dongen, B., Röglinger, M., Mendling, J. (eds) Business Process Management Forum. BPM 2019. Lecture Notes in Business Information Processing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-030-26643-1_11
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