In modern healthcare, patient satisfaction is widely recognized as a cornerstone of healthcare quality assessment, influencing not only clinical outcomes but also institutional reputation and patient loyalty. Yet, its inherently subjective and multifaceted nature makes it difficult to capture with conventional tools. This study introduces an inference system, developed within the framework of artificial intelligence, to provide a more nuanced evaluation of patient-centered care. The model examines eight qualitative indicators of patient experience, including communication, accessibility, staff competence, and perceived treatment outcomes, translating them into measurable outputs through linguistic variables. Relying on a Mamdani approach combined with centroid defuzzification, the system generates an interpretable satisfaction score on a 0–10 scale. Applied to real-world clinical data, this approach proves effective in managing uncertainty, improving decision support, and offering a refined perspective for patient experience evaluation, ultimately supporting more responsive and human-centered healthcare delivery.
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