Event log generation in a health system: A case study

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

Process mining has recently gained considerable attention as a family of methods and tools that aim at discovering and analyzing business process executions. Process mining starts with event logs, i.e., ordered lists of performed activities. Since event data is typically not stored in a process-oriented way, event logs have to be generated first. Experience shows that event log generation takes a substantial effort in process mining projects. This case study reports on the experiences made during the event log generation from the real-world data warehouse of a large U.S. health system. As the focal point, the case study looks at activities and processes that are related to the treatment of low back pain. Guided by the main phases of event log generation, i.e., extraction, correlation, and abstraction, we report on challenges faced, solutions found, and lessons learned. The paper concludes with future research directions that have been derived from the lessons learned from the case study.

Original languageEnglish
Title of host publicationBusiness Process Management - 18th International Conference, BPM 2020, Proceedings
EditorsDirk Fahland, Chiara Ghidini, Jörg Becker, Marlon Dumas
PublisherSpringer Science and Business Media Deutschland GmbH
Pages505-522
Number of pages18
ISBN (Print)9783030586652
DOIs
StatePublished - 2020
Event18th International Conference on Business Process Management, BPM 2020 - Seville, Spain
Duration: 13 Sep 202018 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12168 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Business Process Management, BPM 2020
Country/TerritorySpain
CitySeville
Period13/09/2018/09/20

Keywords

  • Case study
  • Event log generation
  • Healthcare
  • Process mining

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