Transforming Patient Feedback Into Actionable Insights Through Natural Language Processing: Knowledge Discovery and Action Research Study.

BACKGROUND

Patient feedback has emerged as a critical measure of health care quality and a key driver of organizational performance. Traditional manual analysis of unstructured patient feedback presents significant challenges as data volumes grow, making it difficult to extract meaningful patterns and actionable insights efficiently.

OBJECTIVE

We attempted to develop and evaluate a comprehensive methodology for analyzing patient feedback data using natural language processing and Knowledge Discovery in Databases (KDD) approaches, aiming to identify key patterns, themes, and variations in patient experiences across different demographic groups and care settings, and to translate these insights into actionable improvements in health care delivery.

METHODS

This study applied an integrated KDD-action research framework to analyze 126,134 patient feedback entries collected at Alexandra Hospital, Singapore, in 2023. A comprehensive suite of text mining techniques, including sentiment analysis, topic modeling, emotion detection, and aspect-based sentiment analysis, was employed to uncover patterns in patient-reported experiences. The dataset included 92,578 (73.4%) entries containing free-text comments, comprising 1,568,932 tokens with an average comment length of 16.9 words. Multiple analytical techniques were used to ensure the validity and reliability of the findings. Stakeholder engagement throughout the research process facilitated the translation of analytical insights into practical improvements. The study was granted an exemption from ethical review by the National Healthcare Group Domain Specific Review Board (number: NUS-IRB-2025-087E), with a waiver of informed consent granted for this retrospective analysis of deidentified patient feedback data.

RESULTS

Text mining analysis revealed a moderately positive overall sentiment across the feedback corpus (average polarity score: 0.42), with 68.8% (63,685/92,578) of comments classified as positive, 25.4% (23,515/92,578) as neutral, and 5.8% (5378/92,578) as negative. Topic modeling identified 10 distinct topics, including staff attitude and service (9443/92,578, 10.2%), health care staff professionalism (9350/92,578, 10.1%), hospital environment (9258/92,578, 10.0%), and waiting time (9258/92,578, 10.0%). Aspect-based sentiment analysis highlighted nurse attitude (sentiment score: 0.65), staff helpfulness (0.61), and doctor expertise (0.58) as the most positive aspects, while waiting time (-0.42) and billing transparency (-0.28) emerged as the most negative. Demographic segmentation revealed significant variations in patient priorities, with younger patients (<35 years) expressing 37% more concerns about digital accessibility and efficiency than older patients who valued face-to-face interactions 42% more highly. Implementation of targeted interventions based on these findings resulted in measurable improvements, including an 18% increase in waiting time satisfaction, a 15% improvement in doctor-patient communication ratings, and a 23% reduction in billing-related complaints.

CONCLUSIONS

The integration of natural language processing techniques with KDD and action research principles provides a powerful framework for transforming unstructured patient feedback into actionable insights for health care improvement. This approach enables health care organizations to understand the complex patterns and drivers of patient experience, identify targeted improvement opportunities, and implement evidence-based initiatives that enhance care quality and patient-centeredness.