Accepted for/Published in: JMIR Formative Research
Date Submitted: Dec 5, 2024
Date Accepted: Jun 10, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Transforming Patient Feedback into Actionable Insights: A Natural Language Processing Approach to Patient-Centered Care
ABSTRACT
Background:
This research paper presents a comprehensive application of the Knowledge Discovery in Databases (KDD) framework, integrated with Action Research principles, to analyze a large dataset of patient experience feedback collected at Alexandra Hospital in Singapore in 2023.
Objective:
To develop and evaluate a comprehensive methodology for analyzing patient feedback data using natural language processing (NLP) and KDD approaches, aiming to identify key patterns, themes, and variations in patient experience across different demographic groups and care settings, and to translate these insights into actionable improvements in healthcare delivery.
Methods:
By employing a robust suite of natural language processing (NLP) and text mining techniques, the study uncovers rich, multidimensional insights into the key drivers, patterns, and variations in patient-reported experiences and perceptions across diverse care settings and patient subgroups.
Results:
The study demonstrates the power of combining advanced data science approaches with participatory, action-oriented stakeholder engagement to translate analytical findings into targeted, evidence-based initiatives for improving the quality, safety, and patient-centeredness of healthcare delivery. The research highlights the importance of a nuanced, contextualized understanding of patient feedback that goes beyond simple sentiment analysis to unpack the complex interplay of thematic, emotional, relational, and behavioral dimensions shaping patient experiences and satisfaction.
Conclusions:
The study makes significant contributions to the growing field of patient experience analytics and patient-centered care innovation, and generates insights and implications relevant to healthcare contexts grappling with the challenges of listening to and partnering with patients and communities in an era of data-driven healthcare. Clinical Trial: Not Applicable
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