A Linguistic Pythagorean Fuzzy Decision Framework for Patient Satisfaction Improvement Strategy Selection in Hospitals

Patient satisfaction has emerged as a pivotal indicator of healthcare quality, yet the selection of effective improvement strategies remains a complex decision problem characterized by conflicting criteria, imprecise expert judgments, and inherent uncertainty. This study develops a novel multi-criteria decision-making (MCDM) framework for selecting optimal patient satisfaction improvement strategies in a Pythagorean fuzzy environment. Five alternatives are evaluated against six patient-centered criteria. Linguistic assessments are collected from seven domain experts—a hospital administrator, chief physician, head nurse, quality manager, patient representative, healthcare academic, and IT/systems manager—and converted into Pythagorean fuzzy numbers. Expert opinions are subsequently aggregated using a Pythagorean fuzzy Aczel-Alsina weighted aggregation operator, which offers superior flexibility in modeling varying degrees of uncertainty. Criterion weights are objectively determined via the CRITIC (Criteria Importance Through Intercriteria Correlation) method, capturing both contrast intensity and inter-criteria conflict. The decision problem is addressed using a TOPSIS method incorporating a spherical distance measure for Pythagorean fuzzy numbers, which mitigates the loss of information associated with conventional distance metrics. A sensitivity analysis is conducted across seven distinct weight scenarios to examine the robustness of the ranking. Furthermore, the results are benchmarked against established MCDM methods, including SAW, WPM, CoCoSo, and VIKOR, to validate the reliability and consistency of the proposed approach.