Leveraging Natural Language Processing for In-Depth Analysis and Insights from Hospital Patient Feedback Data

The patient's feedback is a key component in enhancing healthcare service. This research employed sophisticated NLP tools to analyze the feedback of 500 hospitals in Bangalore, India, aiming at improving healthcare quality. Experimenting with various Python libraries, the research began preprocessing feedback employing tokenization, using LDA for topic modeling and VADER for sentiment analysis. The analysis highlighted core areas of patient concern and satisfaction, with treatment quality, staff/doctor interactions, and general service delivery being the most pronounced. Sentiment analysis detected close to 0.6 positive sentiment scores for treatment quality, staff interaction and other facilities received moderate positive expressions, about 0.4 and 0.35, respectively. This paper demonstrates the success of NLP in extracting valuable data from patient comments and provides patient satisfaction with diverse hospital services. The research presents useful directions for hospital administrators and healthcare providers to perfect service quality, indicating the role of advanced text analysis in patient care.