LAUSR

Healthcare is the most pivotal domain of every nation. With the sudden upraise of the COVID-19 pandemic, there has been a major concern for the healthcare industry to provide quality medical services to the common people. Vulnerable healthcare conditions have proven to be fatal for the patients. Conspicuously, it has become indispensable to assess the quality of healthcare services provided by the hospitals. The current article focuses on analyzing healthcare service quality delivered by the hospitals and healthcare centers. Specifically, the presented framework utilizes Internet of Things (IoT) technology to acquire real-time ambient data inside smart hospitals. The quantification of the healthcare service is performed using the Probability of Health Grade (PoHG) to classify data segments using the probabilistic Bayesian belief model. Furthermore, the temporal data abstraction is performed for the numerical analysis of healthcare service quality in terms of the health quality index (HQI). Finally, a 2-player game theory-inspired decision modeling is performed to analyze healthcare quality in a time-sensitive manner. The proposed framework is assessed using a simulated environment where 225325 data segments are analyzed. Results are compared with state-of-the-art techniques in which enhanced performance measures are registered in terms of classification efficacy (93.74%), decision-making efficiency [coefficient of determination (95%), accuracy (97.53%), mean square error (2.01%), and root mean square error (1.95%)], temporal delay (96.62 s), and reliability (91.58%).