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Automatic Process Comparison for Subpopulations: Application in Cancer Care.

Francesca Marazza1, Faiza Allah Bukhsh1, Jeroen Geerdink2

  • 1Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands.

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Summary

This study introduces an automated method to compare patient flow models from electronic health records. It helps hospitals understand variations in care for different patient groups, improving quality control.

Keywords:
MIMIC databasebreast cancer carecancer typesprocess comparisonprocess miningquality control

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Area of Science:

  • Health Informatics
  • Process Mining
  • Data Science

Background:

  • Hospital processes can deviate from standards due to complexity and unforeseen events.
  • Understanding patient flow is crucial for quality control and improvement in healthcare.
  • Comparing process models for different patient populations from electronic health records is challenging.

Purpose of the Study:

  • To develop an automated approach for comparing process models extracted from electronic health records.
  • To enable efficient comparison of patient flows across different patient populations.

Main Methods:

  • Process discovery from electronic health record event data.
  • Cross-log conformance checking for comparing process models.
  • Application of standard graph similarity measures.
  • User study to establish ground truth for model similarity.
  • Evaluation on MIMIC and Dutch hospital breast cancer patient databases.

Main Results:

  • Average fitness indicates visual similarity for the ZGT use case.
  • Average precision and graph edit distance correlate with visual similarity for cancer patient models in MIMIC.
  • Identified specific similarity measures effective for different datasets.

Conclusions:

  • The proposed approach automates the comparison of process models from electronic health records.
  • Different similarity measures are effective for different types of process model comparisons.
  • Further research is needed to determine optimal similarity metrics for various healthcare process analyses.