The MEWMA chart is one of the traditional multivariate charts which are widely employed in inspecting the quality of manufacturing and services. This chart is created through monitoring the small shifts of mean vectors of variable quality characteristics. Often in practice, the measurement of a quality characteristic produces uncertain, incomplete values, so that ambiguous numbers are obtained. In this condition, a neutrosophic-based control chart can overcome the problem resulting from the ambiguous data. The paper’s objective is to construct a new multivariate monitoring scheme based on a neutrosophic chart, namely the neutrosophic Multivariate EWMA (NMEWMA). Furthermore, the performance of the new multivariate monitoring scheme is evaluated in detecting process shifts employing the Average Run Length (ARL) and Standard Deviation Run Length (SDRL). This control chart is an innovation in the quality monitoring of uncertain data. The research result obtained indicates that the NMEWMA chart performs better than the MEWMA in finding the small mean shifts as well as in the real case application.

This study investigates the utilization of Google Maps reviews to assess hospital service quality. Patient-generated reviews were analyzed using a sentiment analysis framework incorporating the Bidirectional Encoder Representations from Transformers (BERT) classification model. The p control chart was employed to monitor the distribution of negative sentiment. The results of the sentiment analysis revealed a predominance of positive reviews over negative ones. The BERT classifier achieved excellent performance, with AUC values of 99.95% and 93.72% for training and testing data, respectively. However, the p control chart indicated that the hospital's performance still requires improvement, as several observations fell outside the statistically controlled range. Common patient complaints centered on lengthy wait times and queues, highlighting areas for targeted quality enhancement initiatives. This research demonstrates the potential of leveraging patient feedback to inform hospital quality improvement efforts.