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Process mining for healthcare decision analytics with micro-costing estimations.

Sander J J Leemans1, Andrew Partington2, Jonathan Karnon2

  • 1RWTH, Aachen, Germany.

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Summary

This study links process mining with decision analytics to better estimate healthcare costs and consequences. It introduces a new process model with trace data and micro-costing for improved resource allocation and value-for-money assessments.

Keywords:
Decision analyticsHealthcare economicsProcess mining

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Area of Science:

  • Health economics
  • Decision analytics
  • Process mining

Background:

  • Healthcare decision-makers manage constrained resources, evaluating value for money based on process changes.
  • Process mining quantifies care processes but often excludes post-process costs and consequences.
  • Existing decision-analysis models are costly to operationalize for forecasting downstream effects.

Purpose of the Study:

  • To bridge the gap between process mining and decision analytics for healthcare resource management.
  • To introduce a novel process model integrating trace data for decision-analytical modeling.
  • To enhance these models with process-based micro-costing estimations.

Main Methods:

  • Developed a new process model incorporating trace data for individual or cohort-level analysis.
  • Integrated process-based micro-costing estimations into the enhanced process models.
  • Evaluated the approach with health economics and decision modeling experts.

Main Results:

  • The new process model effectively links process mining with decision analytics.
  • Process-based micro-costing provides detailed event-level cost information.
  • Experts discussed the utility of the outputs for decision-making.

Conclusions:

  • This integrated approach enhances the ability to assess the value for money of healthcare process changes.
  • The developed models offer a more efficient method for forecasting downstream consequences of healthcare interventions.
  • This work provides a foundation for improved resource allocation in healthcare settings.