An efficient method for kidney allocation problem: a credibility-based fuzzy common weights data envelopment analysis...

Area of Science:

• Transplantation Science
• Operations Research
• Medical Informatics

Background:

• Organ transplantation faces chronic organ scarcity, necessitating efficient and equitable allocation systems.
• Existing organ allocation models struggle with uncertainty and vague patient-organ matching factors.
• Continuous revisions aim to balance equity and medical efficiency in organ distribution.

Purpose of the Study:

• To develop a Data Envelopment Analysis (DEA)-based model for evaluating patient-kidney pair efficiency in organ allocation.
• To enhance organ allocation fitness by addressing inherent uncertainties and vague factors using fuzzy programming.
• To ensure fairness and improve outcomes in kidney transplantation through a robust evaluation framework.

Main Methods:

• Employed a Credibility-based Fuzzy Common Weights DEA (CFCWDEA) approach, treating patient-kidney pairs as decision-making units (DMUs).
• Utilized fuzzy programming to manage vague and intervallic medical and non-medical allocation factors.
• Ensured fairness via a common set of weights applied to all DMUs for consistent assessment.

Main Results:

• The proposed deterministic model demonstrated superiority in enhancing kidney allocation outcomes.
• The fuzzy DEA method proved applicable and effective across various credibility levels in a real-world case study.
• The model successfully ranked patient-kidney pairs based on calculated efficiency scores.

Conclusions:

• The CFCWDEA model offers a fair and efficient approach to kidney allocation, particularly under uncertainty.
• This fuzzy DEA method provides a novel solution for incorporating vague factors into organ allocation decisions.
• The validated model can significantly improve the fitness and outcomes of kidney transplantation networks.