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Process mining offers a new way to understand healthcare processes by analyzing digital event data. This technique helps discover how processes work and ensures they align with desired models for better quality and efficiency.

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Area of Science:

  • Health Informatics
  • Process Management
  • Data Science

Background:

  • Improving healthcare quality and efficiency requires understanding real-world processes.
  • Traditional methods like interviews and observations provide valuable insights but can be time-consuming.
  • A need exists for complementary techniques to analyze healthcare operations.

Purpose of the Study:

  • Introduce process mining as a novel technique for healthcare process analysis.
  • Demonstrate how process mining can uncover process insights from digital event data.
  • Illustrate the application of process mining in healthcare settings.

Main Methods:

  • Process mining utilizes automatically registered digital events from information systems.
  • Analysis is based on event logs to construct process models (discovery).
  • Conformance checking compares actual processes against prescriptive models.

Main Results:

  • Process mining enables the discovery of as-is healthcare process models.
  • It facilitates the assessment of conformance between actual and ideal processes.
  • The paper presents examples showcasing process mining's utility.

Conclusions:

  • Process mining is a valuable complementary technique for healthcare process improvement.
  • It offers objective insights into process execution through data analysis.
  • The method aids in enhancing both the quality and efficiency of healthcare delivery.