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Accounting for Fairness in a Two-Stage Stochastic Programming Model for Kidney Exchange Programs.

Hyunwoo Lee1, Seokhyun Chung2, Taesu Cheong3

  • 1School of Industrial Management Engineering, Korea University, Seoul 02841, Korea. lhw0429@korea.ac.kr.

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|July 18, 2018
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Summary

Kidney exchange programs can now prioritize patient satisfaction for fairer transplants. This new model accounts for individual pair expectations, reducing transplant failures and improving overall program utility.

Keywords:
exceptional eventkidney exchange programtwo-stage stochastic programmingunfairness indicator

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Area of Science:

  • Nephrology
  • Transplantation
  • Operations Research

Background:

  • Kidney exchange programs (KEPs) facilitate incompatible donor-recipient pairs to receive life-saving transplants.
  • Current KEP models often prioritize maximizing exchanges or social utility, neglecting individual pair satisfaction.
  • Fairness, defined by patient-donor pair satisfaction, is crucial for successful kidney transplantation.

Purpose of the Study:

  • To introduce a novel framework for kidney exchange that incorporates fairness as a primary objective.
  • To address the issue of potential transplant failures due to unmet expectations within kidney exchange programs.
  • To develop a stochastic programming model that balances overall utility with individual pair satisfaction.

Main Methods:

  • A two-stage stochastic programming approach was employed to model kidney exchanges.
  • The first stage maximizes total utility, while the second stage minimizes penalties for transplant failures.
  • The model incorporates scenarios of potential failures based on the perceived value of exchanged kidneys.

Main Results:

  • The proposed stochastic model demonstrates improved performance over traditional deterministic models.
  • The model effectively accounts for fairness by considering individual patient-donor pair satisfaction.
  • Computational results validate the model's ability to enhance kidney exchange outcomes.

Conclusions:

  • Integrating fairness, defined by pair satisfaction, is essential for successful kidney exchange programs.
  • Stochastic programming offers a robust framework for optimizing kidney exchanges under uncertainty.
  • This approach has the potential to increase transplant rates and improve patient well-being in kidney transplantation.