Reducing healthcare-associated infections (HAIs) remains a significant challenge for patient safety and sustainability of healthcare services worldwide, and a pressing need exists for decision-making models that can effectively encapsulate uncertainty, ambiguity, and discordant expert opinions. Traditional infection-control evaluation models mainly depend on classical or basic fuzzy methodologies, which unfortunately fall short in encapsulating circular preference behaviors of expert judgments and extreme ambiguities.
This study introduces a robust decision-support framework centered on Circular q-Rung Orthopair Fuzzy sets (q-ROF). This model is integrated with a modified Comprehensive and Robust Aggregation with Dominance-based Incomparable and Incomplete Scores (CRADIS) method. The proposed model exhibits unique capabilities in modeling the uncertainty, hesitation, and cyclical judgment patterns that are common in many health assessments led by experts.
Additionally, this study revamps the established CRADIS method within the Circular q-ROF context by way of customizing normalization, aggregation, and ranking processes. The proposed model provides an analytical tool for evaluating infection-control measures in hospitals regarding efficiency, safety, cost, and sustainability. A numerical case study is provided to illustrate its applicability, along with comparisons with popular existing fuzzy aggregation-based decision models to demonstrate the ranking behavior.
Also, a sensitivity analysis is conducted to evaluate the robustness of results considering the volatility of parameters and changes in expert weights. The outcomes reveal that the proposed approach yields stable and interpretable rankings, ensuring a high degree of robustness under conditions of excessive uncertainty. The introduction of this structured and reliable decision-support tool is a significant advancement in the area of hospital infection-control policy formulation.