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.