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Discovering health-care processes using DeciClareMiner.

Steven Mertens1, Frederik Gailly1, Geert Poels1

  • 1Faculty of Economics and Business Administration, Department of Business Informatics and Operations Management, Ghent University, Ghent, Belgium.

Health Systems (Basingstoke, England)
|June 20, 2019
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Summary

This study introduces DeciClareMiner, an algorithm for process mining and decision mining. It efficiently extracts healthcare process models and knowledge, significantly improving upon traditional methods.

Keywords:
Process miningdecision mininghealth-care modellingknowledge extraction

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Area of Science:

  • Computer Science
  • Healthcare Informatics
  • Process Mining

Background:

  • Healthcare processes are often flexible and knowledge-intensive, leading to tacit knowledge and unclear process views.
  • Effective process modeling is crucial for understanding and optimizing healthcare delivery.

Purpose of the Study:

  • To propose DeciClareMiner, an algorithm combining process and decision mining.
  • To extract process models and associated knowledge from historical healthcare data.
  • To address the challenge of tacit knowledge in complex service processes.

Main Methods:

  • Developed DeciClareMiner algorithm integrating process and decision mining techniques.
  • Applied the algorithm to a realistic healthcare case study.
  • Compared DeciClareMiner's performance against an exhaustive search benchmark.

Main Results:

  • DeciClareMiner generated a comprehensive DeciClare model in 10 minutes.
  • The model accurately represented 93% of healthcare episodes with atomic constraints.
  • This significantly outperformed the 50 hours required for a 100% complete model via exhaustive search.

Conclusions:

  • DeciClareMiner offers a major improvement in efficiency and effectiveness for healthcare process discovery.
  • The algorithm successfully extracts valuable process models and knowledge from complex healthcare data.
  • This approach enhances visibility and understanding of tacit knowledge within healthcare processes.