Researchers around the world have extensively explored the service quality of healthcare research domain. Several research studies have focused on the service quality of health organizations within their countries, and some of them presented generic models. One study showed that understanding how hospital in-patients evaluate service quality performance can improve the current healthcare system’s outcomes and service quality, raising satisfied in-patient numbers and keeping patients coming back to the hospitals [10]. Service quality was assessed on five aspects (tangible, reliable, responsiveness, assurance, and empathy), according to Parasuraman et al. [11,12]. The researchers designed an assessment model to assess hospitals’ service quality [13,14,15,16,17,18]. According to Duggirala et al. [19], hospital service quality in a developing country is determined by seven factors: infrastructure, administrative procedures, workforce quality, clinical care protocols, safety, long-term experience, and social responsibility. Aagja and Garg [20] suggested five pillars to improve public hospital service quality: admission, medical care, holistic support, discharge procedure, and public accountability. Numerous elements that can be categorized in various ways influence a patient’s perception of a hospital. For example, physical factors (ambiance, infrastructure, tangibles, etc.); interaction factors (staff behavior, expertise, attitude, etc.); and other factors (waiting time, availability, safety, loyalty).

On the contrary, Kondasani and Panda [21] linked the hospital’s service quality with patient loyalty. They adopted a questionnaire-based approach and collected data from five private hospitals in India. Their findings showed that patients’ perceptions were positively impacted by the interaction between service providers and consumers, the quality of the facilities, and interactions with support staff. Similarly, different service quality measurement models were explored to quantify the service quality of hospitals in Thailand, and feedback was taken from people from four different continents (Asia, Australia, America, and Europe). With varying amounts of quality dimensions and quality attributes, four distinct models for evaluating service quality were established based on the different continents. Asian patients offered a four-facet model comprising twenty items, whereas European patients offered a two-dimensional model with sixteen variables. Patients from Australia similarly revealed a two-dimensional model, but it contained 22 items, whereas Americans offered a three-dimensional model, which contained 17 elements. It was reported that nationality and demographics also significantly affected service satisfaction in addition to size and location factors. Most of the research studies utilized a questionnaire-based approach to obtain the patients’ satisfaction levels based on several dimensions [8,22,23].

According to some researchers, patients need more expertise and information to accurately evaluate the technical components of medical services, such as practitioners’ diagnostic abilities or surgeons’ surgical capabilities. Patients are highly qualified to assess functional quality parameters, like laboratory sanitation, waiting time, etc. [24,25]. Therefore, some researchers only focused on a particular department or service for assessing service quality. For example, Zarie et al. [26] focused on emergency departments’ service quality and compared private and public hospitals. A questionnaire was developed based on twenty questions. It was reported that the private hospital’s emergency department was better. Some researchers have suggested that even hospitals’ supply chain management can significantly affect the service quality dimension and hospital performance [27,28]. Similarly, Han et al. [29] utilized the data obtained from the government initiative of a hotline for patient feedback to measure service quality. The patients’ feedback and complaints were utilized to make recommendations to the hospital to improve their service quality. In another research study, the role of digital platforms in healthcare was evaluated, and their impact on patient satisfaction was analyzed [30]. Sharifi et al. [31] presented a comparison of two models, where both models could investigate the level of service quality in healthcare centers. Both models’ findings demonstrated an unfavorable void between the service users’ expectations and perceptions. Kristinawati et al. [32] utilized a structural equation model (SEM) to analyze the data obtained through questionnaires filled by randomly selected patients at a hospital in Indonesia. The study intended to find the relationship between hospital service quality and customer contentment. It was revealed from the results that there is a significant impact of hospital service quality and satisfaction on loyalty. Patel and Patel [33] employed a combination of confirmatory factor analysis and SEM to analyze the data obtained from a survey of 316 patients from 29 hospitals in India. The goal was to assess how hospital service quality characteristics affected outpatient satisfaction and to identify the demographic factors that influenced that satisfaction. Gavahi et al. [34] adopted QFD (quality function deployment) to improve the service quality in radiology centers. Whereas Junior et al. [35] employed a methodology as a planning tool to measure service quality in a surgical center in Brazil. It was reported that the suggested approach enhanced the decision-making process, increasing the effectiveness of the operation of the surgical center. Duc Thanh et al. [36] proposed a service performance tool to measure the service quality in an oncology public hospital in Vietnam.

Alsawat [37] and Alumran et al. [38] employed a questionnaire-based approach to assess patients’ satisfaction with services in the emergency departments of hospitals in Saudi Arabia. Gentili [39] emphasized that the fuzzy technique is an efficient tool in modeling the human power of making decisions based on natural language, and its link with Bayesian inference can make it more effective. The most accredited theory in neuroscience maintains that human reasoning is Bayesian [40]. Kumar and Rambabu [41] proposed a fuzzy technique for order performance by similarity to the ideal solution for ranking the hospitals based on patients’ opinions. However, only six factors were considered by them. Another researcher used a fuzzy analytic hierarchy process to rank the quality of four hospitals [42]. Alkafaji and Al-shemary [43] used the hospital consumer assessment of healthcare providers and systems to collect data from the patients and then applied a fuzzy-based method to assess the hospital service quality for two hospitals in Iraq, and several hospitals in the United States of America. The results of the five assessment categories showed that over half of the US hospitals were in the good to very good range. Babroudi et al. [44] presented an integrated model with Z-number theory and a fuzzy cognitive map for health service quality measurement. The results showed that hospital hygiene, hospital reliability, and completeness of the hospital, with ratios of 0.9305, 0.9559, and 0.9268, respectively, were the most significant criteria in enhancing healthcare service quality in a pandemic situation. Some researchers have even applied the fuzzy approach to measure service quality in other industries, such as the hotel industry [45].

Although a lot of research has been performed in the area of healthcare service quality, there are still many gaps that prevent us from fully understanding and accurately measuring and improving hospital service quality. A predominant limitation in the existing literature is the over-reliance on traditional methodologies that often fail to effectively address the multi-dimensional and ambiguous nature of healthcare service quality. Despite a wide variety of performance metrics and evaluation frameworks being proposed, many need help encapsulating diverse criteria and uncertainties in a single, meaningful index. Further, healthcare services’ intricate and intangible nature often leads to inconsistent results, reduced reliability, and misinterpretation. Several researchers have presented an assessment model for hospital service quality; however, most of them are based on a qualitative framework, and there is a paucity of mathematical models, and the factors considered in these models are limited and not comprehensive. The necessity for a systematic and reliable technique of evaluating the quality of hospital services as perceived by patients has increased along with healthcare advancement.

Furthermore, the majority of the currently used techniques for evaluating the quality of healthcare are unable to deal with the vagueness and subjective assessment that characterize human perceptions and decision-making processes. This becomes a critical barrier when trying to gain accurate and comprehensive insights into patient satisfaction and care quality. A further research gap is the limited focus on robust and easy-to-understand measures that can be readily implemented by healthcare administrators and stakeholders, limiting the practical applicability of many existing models. These gaps underscore the need for a novel approach to manage healthcare evaluations’ inherent uncertainties and complexity and effectively transform the multi-faceted criteria into a single, interpretable performance index. Moreover, as reported in the literature, service quality involves multiple dimensions, and it is not easy to comprehend. Thus, the proposed model has established a single index, so that the management, as well as the customer, can easily evaluate the hospital’s service quality. In addition, it has also provided a useful method for hospital management to know about the strengths and weaknesses in their service areas where they can focus on enhancing the service quality of their hospital. To address the issues of vagueness and subjective judgment, the adopted research methodology utilized a fuzzy approach, and the details of the proposed methodology are presented in the subsequent sections.