Fuzzy Decision-Making Techniques in Healthcare Service Quality Evaluation
Summary
Fuzzy decision-making techniques have emerged as powerful tools for evaluating healthcare service quality by accommodating the inherent vagueness of human judgement and the complexity of clinical environments. Drawing on fuzzy set theory, these approaches encode expert assessments and patient perceptions into membership functions that reflect degrees of satisfaction, reliability and responsiveness rather than crisp scores. Within a multi-criteria decision-making (MCDM) framework, methods such as fuzzy Analytic Hierarchy Process, fuzzy Best–Worst Method, fuzzy TOPSIS and fuzzy VIKOR are employed to weight and rank hospitals or service units against dimensions like tangibles, assurance, empathy and timeliness. Extensions integrating rough sets, copula functions or Bayesian networks further permit the modelling of intercriteria dependencies and dynamic uncertainties. Across global health systems under resource constraints and pandemic pressures, fuzzy MCDM facilitates transparent benchmarking, identifies priority areas for improvement and informs managers and policymakers on balanced resource allocation to enhance patient satisfaction and clinical outcomes.
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Fuzzy Decision-Making Techniques in Healthcare Service Quality Evaluation publication trend
The graph below shows the total number of articles in fuzzy decision-making techniques in healthcare service quality evaluation across all publications each year (not limited to Nature Index journals).
Technical terms
Fuzzy set theory: A mathematical framework in which elements have degrees of membership between 0 and 1, enabling the representation of imprecise or vague information.
Multi-Criteria Decision-Making (MCDM): A class of methods for evaluating and ranking alternatives based on multiple, often conflicting, criteria.
Best–Worst Method (BWM): An MCDM weighting technique that derives criterion weights through pairwise comparisons of the most and least important factors.
Copula Bayesian Network: A probabilistic graphical model that uses copula functions to model complex dependencies among variables within a Bayesian network structure.
Fuzzy rough set: A hybrid approach combining fuzzy sets and rough set theory to handle both vagueness and indiscernibility in decision-making data.
References
- A healthcare service quality assessment model using a fuzzy best–worst method with application to hospitals with in-patient services. Healthcare Analytics (2023).
- A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment. Complex & Intelligent Systems (2023).
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