EVALUATING PATIENT SATISFACTION THROUGH A FUZZY LOGIC FRAMEWORK BASED ON SIMULATED PROFILES

Measuring patient satisfaction remains a major challenge in healthcare, as it involves perceptions that are often subjective, multidimensional, and expressed in vague or imprecise terms.This study introduces a fuzz y logic framework designed to model the complexity of patient experie nce using linguistic variables and transparent reasoning rules.In the abs ence of real-world data, which are currently being collected through a clinical questionnaire, the system is tested on a set of simulated patient profiles. Eight key dimensions of satisfaction are considered, including communication, accessibility, staff competence, perceived outcomes, and infrastructure. Each criterion is represented by tailored membership functions, and a Mamdani inference mechanism produces an overall satisfaction score ranging from 0 to 10. A synthetic dataset of fifty patient profiles was generated based on expected distributions, allowing for an initial assessment of the systems consistency and relevance. The results suggest that the model provides meaningful and interpretable outputs,offering a promising tool for future integration into healthcare quality monitoring platforms. This simulation-based study serves as a preparatory step before empirical deployment and highlights the flexibi lity of fuzzy logic in capturing the nuances of patient perception