Healthcare service quality evaluation: An integrated decision-making methodology and a case study. (August 2022)

• Record Type: Journal Article
• Authors: Karasan, Ali; Erdogan, Melike; Cinar, Melih
• Is Part Of: Socio-economic planning sciences. Number 82(2022)Part A
• Publication Date: 2022-08
• Subjects: Applied sciences -- Fuzzy inference system -- Healthcare management -- Interval valued fuzzy sets -- Pythagorean fuzzy sets -- Service quality; Planning -- Periodicals; Economic policy -- Periodicals; Social policy -- Periodicals; Planification -- Périodiques; Politique économique -- Périodiques; Politique sociale -- Périodiques; ECONOMIC PLANNING; SOCIAL PLANNING; DECISION-MAKING; 361
• DOI: 10.1016/j.seps.2022.101234
• Languages: English
• ISSNs: 0038-0121

Abstract

Abstract: Developments and changes in health services give importance to the concept of competition and encourage to increase quality among the healthcare centers. During these processes, the most required expectations from the system are achieving the desired quality and maintenance of the system environments. In this sense, measurement and continuous improvement of service quality are crucial for health centers to provide better service to patients. For this aim, this paper presents an integrated assessment methodology based on the distance-based Pythagorean Fuzzy Multiple Criteria Decision Making (MCDM) method, TOPSIS, and Fuzzy Inference System (FIS) for the measurement of health care service quality. To show the efficiency of the proposed method, a case study is carried out by comparing the ten clinics of a private hospital. Five dimensions of service quality (SERVQUAL) have been used as the main criteria for evaluating the service quality levels of the clinics, and multiple sub-criteria are determined for each dimension for a detailed analysis. Pythagorean Fuzzy TOPSIS is executed to the determined decision matrices to obtain inputs of the fuzzy rule-based system. Then, the clinic's service quality levels are calculated by implementing the fuzzy inference system. Moreover, sensitivity analyses based on the changes in the decision makers' weights are also applied to check the flexibility of the results. Finally, implementations with respect to theoretical, managerial, and policy aspects are discussed based on the obtained results. Highlights: A framework is developed to assess the service quality levels of the clinics. A fuzzy-based integrated methodology is utilized to handle uncertain data. The results are analyzed by applying sensitivity analyses to demonstrate their flexibility. Implications of the results are discussed to recommend possible improvements.