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Genetic algorithm based system for patient scheduling in highly constrained situations

V Podgorelec1, P Kokol

  • 1University of Maribor, Faculty of Electrical Engineering and Computer Science, Slovenia.

Journal of Medical Systems
|April 29, 1998
PubMed
Summary
This summary is machine-generated.

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Optimizing patient scheduling is complex. This study introduces an automated method using genetic algorithms and machine learning to create efficient schedules, minimizing wait times and maximizing resource use.

Area of Science:

  • Health Informatics
  • Operations Research
  • Artificial Intelligence

Background:

  • Patient scheduling in healthcare involves complex logistical challenges, including coordinating appointments across various devices, physicians, and therapists.
  • Current manual or heuristic scheduling methods often fail to achieve optimal outcomes, leading to prolonged patient wait times and underutilized resources.

Purpose of the Study:

  • To develop and present a novel automated scheduling method for complex healthcare scenarios.
  • To optimize patient flow by minimizing completion time and patient waiting, while maximizing the utilization of therapeutic devices.

Main Methods:

  • A powerful automated scheduling method was developed utilizing genetic algorithms and machine learning for highly constrained situations.
  • The method ensures the generation of only feasible solutions by adhering to all required constraints throughout the scheduling process.

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Main Results:

  • The developed algorithm was applied to schedule patients with diverse therapy needs to a limited set of therapeutic devices.
  • The results demonstrated encouraging outcomes, with all generated schedules being feasible and efficient.

Conclusions:

  • The automated scheduling method offers a robust solution for complex healthcare scheduling problems.
  • The algorithm's feasibility and efficiency make it suitable for integration into interactive user interfaces, significantly aiding in the management of patient and human resources.