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Related Experiment Videos

Logistic-based patient grouping for multi-disciplinary treatment.

Laura Maruşter1, Ton Weijters, Geerhard de Vries

  • 1Department of I&T, Faculty of Technology Management, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands. l.maruster@tm.tue.nl

Artificial Intelligence in Medicine
|September 18, 2002
PubMed
Summary

This study introduces a novel logistic-driven approach to group patients with peripheral arterial vascular (PAV) diseases. This method facilitates the creation of specialized, multi-disciplinary healthcare units for improved patient care coordination.

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Area of Science:

  • Healthcare logistics and operations research
  • Medical informatics and data analysis
  • Vascular surgery and patient management

Background:

  • Healthcare systems face challenges in coordinating patient care across specialized units.
  • Existing patient grouping methods often focus on pathophysiology, not logistical needs.
  • Multi-disciplinary care requires effective patient flow management between hospital units.

Purpose of the Study:

  • To develop and evaluate a logistic-driven grouping approach for identifying patient groups needing multi-disciplinary care.
  • To create a framework for forming new, specialized healthcare units based on patient logistics.
  • To utilize machine learning for patient selection into these new units.

Main Methods:

  • Utilized a database of 3,603 patients with peripheral arterial vascular (PAV) diseases.

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  • Applied clustering techniques to group patients based on six key logistic variables.
  • Employed machine learning to develop classification rules for patient selection.
  • Evaluated grouping quality based on utility for new unit creation and patient selection.

    • Main Results:

    • Two distinct logistic groupings of PAV patients were identified.
    • Classification rules were successfully formulated to predict patient cluster membership.
    • The proposed method demonstrated the ability to create medically and logistically homogeneous patient groups.
    • The groupings were useful for designing new multi-disciplinary units and selecting eligible patients.

    Conclusions:

    • A logistic-driven grouping methodology can effectively identify patient cohorts for specialized care.
    • This approach supports the creation of efficient, multi-disciplinary healthcare units.
    • Machine learning aids in the precise selection of patients for these targeted treatments.