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Process mining for healthcare: Characteristics and challenges.

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This summary is machine-generated.

Process mining, used for analyzing business processes, has limited adoption in healthcare despite available data. This paper highlights healthcare-specific challenges and characteristics for successful process mining implementation.

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Area of Science:

  • Data Science
  • Healthcare Informatics
  • Business Process Management

Background:

  • Process mining analyzes logged business process data.
  • Healthcare utilizes process mining for diagnostic, treatment, and organizational processes.
  • Systematic adoption of process mining in healthcare is limited, mostly confined to research case studies.

Purpose of the Study:

  • To identify and present the unique characteristics of healthcare processes relevant to process mining.
  • To outline open challenges for the research community in applying process mining to healthcare.
  • To promote the research and application of data-driven healthcare process improvement.

Main Methods:

  • Literature review and analysis of healthcare process characteristics.
  • Identification of domain-specific requirements for process mining in healthcare.
  • Discussion of challenges and future research directions.

Main Results:

  • Healthcare processes exhibit high variability and a patient-centered focus, requiring tailored process mining approaches.
  • There's a gap between the potential of process mining and its systematic application in clinical settings.
  • The Process-Oriented Data Science in Healthcare Alliance aims to bridge this gap.

Conclusions:

  • Successful process mining in healthcare necessitates addressing its unique features like variability and patient-centricity.
  • Further research and community collaboration are crucial for advancing data-driven healthcare process improvements.
  • This paper serves as a foundational initiative for the wider adoption of process mining in the healthcare domain.