Fuzzy decision support systems for hospital infection management: a circular q-ROF CRADIS method to prevention and control

Healthcare-associated infections remain one of the most critical challenges for patient safety and healthcare sustainability, which urgently requires decision-making frameworks that can model uncertainty, hesitation, and conflicting expert opinions. Most of the existing studies in hospital infection-control evaluation models have relied on classical or basic fuzzy approaches, which cannot represent the circular preference behavior of expert judgments and high degrees of ambiguity. In this respect, this study proposes a decision-support framework based on Circular q-Rung Orthopair Fuzzy sets integrated with a modified CRADIS method. The Circular q-ROF structure enables simultaneous modeling of uncertainty, hesitation, and periodic judgment patterns frequently faced in many expert-driven healthcare assessments. Furthermore, this study reformulates the modified CRADIS method under the Circular q-ROF environment with the necessary adaptation of normalization, aggregation, and ranking procedures. Based on the proposed model, hospital infection-control measures are evaluated with respect to several criteria covering effectiveness, safety, cost, and sustainability. A numerical case study is provided to demonstrate its applicability, and comparisons are conducted with some of the well-known existing fuzzy aggregation-based decision models to demonstrate the ranking behavior. Sensitivity analysis is conducted to examine the robustness of the results when considering parameter variation and changes in expert weights. Accordingly, the results have shown that the proposed approach provided stable and interpretable rankings, ensuring good robustness of the results under high uncertainty conditions. This paper contributes a structured and reliable decision-support tool for hospital infection-control policy formulation.