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A stochastic tabu search algorithm to align physician schedule with patient flow.

Nazgol Niroumandrad1, Nadia Lahrichi2

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This study optimizes cancer patient pretreatment scheduling to reduce delays. A new scheduling model improves patient flow and physician satisfaction, addressing uncertainties in patient arrivals and cancer types.

Keywords:
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Area of Science:

  • Operations Research
  • Healthcare Management
  • Medical Informatics

Background:

  • The cancer patient pretreatment phase, from referral to treatment plan confirmation, faces delays.
  • Physicians are identified as critical bottlenecks in this crucial phase.
  • Patient flow and pretreatment duration are key metrics for efficient cancer care.

Purpose of the Study:

  • To develop an optimized weekly cyclic schedule for the cancer patient pretreatment phase.
  • To improve patient flow and reduce the overall pretreatment duration.
  • To incorporate physician satisfaction into the scheduling objective function.

Main Methods:

  • A Mixed-Integer Programming (MIP) model was formulated for the scheduling problem.
  • A tabu search algorithm was developed to solve the MIP model.
  • Both deterministic and stochastic (uncertainty) cases were considered.

Main Results:

  • The proposed tabu search algorithm demonstrated strong performance against CPLEX in deterministic scenarios.
  • The stochastic approach effectively handles uncertainties in patient arrival, profile, and cancer type.
  • The method was validated through a real-world application.

Conclusions:

  • Optimized scheduling significantly improves cancer patient pretreatment efficiency.
  • The developed model and algorithm offer a practical solution for reducing healthcare bottlenecks.
  • Addressing uncertainty and physician satisfaction leads to better patient care pathways.