MULTICRITERIA MODEL WITH HESITANT LINGUISTIC TERMS AND OPTIMIZED CONSENSUS FOR PATIENTS PRIORITIZATION IN EXAM WAITING LISTS

Patient prioritization in waiting lists for elective exams is a critical challenge in public health systems. In the case of colonoscopy, traditional ordering by chronological entry may neglect relevant clinical risk factors for colorectal cancer. This study proposes the PP-Index, a group multicriteria decision support model that integrates Hesitant Fuzzy Linguistic Term Sets (HFLTS), Consensus Reaching Process (CRP) and Particle Swarm Optimization (PSO), combined with the VIKOR method. The model was applied to 165 patients on a waiting list at a university hospital, with the participation of 38 decision makers. The decision makers assessed 21 clinical risk criteria using hesitant linguistic terms to capture uncertainties in judgment. Decision maker weighting was performed objectively based on education, degree and experience. For consensus optimization, PSO was applied with initial particles obtained from SAW, uniform weights and random weights, generating four criteria weight scenarios.To generate the patient ranking, the VIKOR method was used, with sensitivity analysis varying the compromise parameter v between 0 and 1. Decision maker weights showed weak correlation (-0.2894 < ρ < 0.3957). The PP-Index achieved a global consensus of 0.7913 in the PSO scenarios, higher than 0.7833 from SAW. Criteria weights showed high stability (0.9669 < ρ < 0.9914). The four PP-Index rankings had very strong correlations with each other (ρ ≥ 0.9955) and weak negative correlations with the chronological ranking (-0.2050 < ρ < -0.2270). The Friedman test (χ² = 229.032; p < 0.001) confirmed significant differences. VIKOR sensitivity analysis indicated stability for v≤0.6. It is concluded that the PP-Index is robust and feasible. Operationally, the model works in two phases: calibration with specialists and automatic ordering of new patients, representing an alternative to improve the prioritization of elective exams in the Brazilian Public Health System - SUS.