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Summary
This summary is machine-generated.

This study introduces a new algorithm to improve clinical process conformance checking by using fuzzy set theory to handle logging uncertainties. The method provides more accurate diagnostics, enhancing the reliability of healthcare process analysis.

Keywords:
Clinical deviationsConformance checkingEvent-log re-orderingFuzzy setsProcess miningUncertainty

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Area of Science:

  • Health Informatics
  • Process Mining
  • Clinical Process Modeling

Background:

  • Hospitals use clinical pathways for standardized patient treatment.
  • Conformance checking analyzes process execution against pathways.
  • Uncertainty in logging clinical activities compromises diagnostic accuracy.

Purpose of the Study:

  • To address uncertainty in clinical process logging.
  • To enhance the reliability of conformance checking diagnostics.
  • To incorporate expert knowledge into conformance analysis.

Main Methods:

  • Developed a novel conformance checking algorithm.
  • Utilized fuzzy set theory to incorporate expert knowledge.
  • Defined a fuzzy time window for timestamp violation assessment.

Main Results:

  • The proposed method achieved more accurate diagnostics than state-of-the-art approaches.
  • Experiments on a real-life Dutch hospital case study validated the method's effectiveness.
  • Demonstrated improved assessment of timestamp violations in clinical process logs.

Conclusions:

  • The fuzzy set theory-based algorithm effectively mitigates logging uncertainty in clinical pathways.
  • The approach offers more reliable conformance diagnostics for healthcare processes.
  • Findings can guide strategies to reduce logging uncertainty in clinical practice.