An interval type-2 fuzzy multi-criteria decision-making approach for patient bed allocation

Area of Science:

• Healthcare Operations Research
• Decision Science
• Health Informatics

Background:

• Effective inpatient bed management is crucial for optimizing healthcare resources and patient care quality.
• Existing patient prioritization frameworks often fail to account for patient-specific factors, criterion interdependencies, and decision-making uncertainty.
• A significant imbalance between hospital bed capacity and patient demand necessitates improved decision-making tools.

Purpose of the Study:

• To develop a hybrid multi-criteria decision-making (MCDM) framework for patient prioritization in inpatient settings.
• To integrate interval type-2 fuzzy sets, DEMATEL, and VIKOR methods to address uncertainty and interdependencies in decision-making.
• To provide a robust tool for equitable and efficient resource allocation in healthcare.

Main Methods:

• A hybrid MCDM framework combining interval type-2 fuzzy sets, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, and the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method.
• Interval type-2 fuzzy sets were employed to manage linguistic uncertainty in evaluations.
• DEMATEL was utilized to identify causal relationships among evaluation criteria, and the 2-tuple linguistic VIKOR method was applied for ranking patient admission alternatives.

Main Results:

• Empirical analysis identified medical urgency, diagnosis type priority, and wait time as the most influential criteria in patient prioritization.
• The proposed framework successfully modeled interdependencies among decision criteria.
• The study demonstrated the framework's ability to provide transparent and structured rankings for patient admission.

Conclusions:

• The developed hybrid MCDM framework offers a flexible and interpretable approach to improve patient admission decision-making.
• It provides a robust tool for equitable and efficient resource allocation in capacity-constrained healthcare environments.
• This research contributes to healthcare operations research by enhancing patient prioritization strategies.