AI-Driven Healthcare Decision Support System: Utilizing Machine Learning and Sentiment Analysis to Interpret Patient Feedback

With medical digitalization, online patient reviews have become crucial for evaluating healthcare quality. Traditional surveys face limitations in sample size and response rate, hindering real-time patient satisfaction assessment. This study develops an AI-driven Healthcare Decision Support System (AI-DSS) using NLP and ML to analyze patient reviews from NTUH and TVGH, identifying key satisfaction factors. Using BERT for sentiment analysis and text mining, feedback is categorized into five dimensions: facility environment, waiting time, staff attitude, medical procedures, and treatment outcomes. Multiple linear regression quantifies their impact on satisfaction, while a ChiSquare Test compares hospital performance. Results show waiting time and staff attitude as critical factors, with significant differences in the facility environment and staff attitude (p < 0.001). This study confirms AI's role in real-time satisfaction monitoring and data-driven healthcare improvements