From Surveys to Sentiment: A Review of Patient Feedback Collection and Analysis Methods

Abstract

Patient feedback plays a crucial role in improving the quality, responsiveness, and patient-centric approach of healthcare services. This paper presents a comprehensive review of both traditional and digital methods used to collect patient feedback, emphasizing their value in improving healthcare delivery, examines the tools and channels used, including surveys, interviews, and multichannel digital platforms. The review further explores sentiment analysis techniques applied to patient feedback, focusing on how machine learning, deep learning, and large language models are used to interpret and categorize unstructured text. The recent literature is systematically analyzed, with comparative tables that highlight feature extraction methods, classification algorithms, and performance metrics reported in various studies. Additionally, the paper addresses key challenges in feedback collection and sentiment analysis. Future research directions are proposed, such as automating feedback systems and incorporating patient perspectives into quality improvement frameworks. This review is intended to assist Healthcare IT Professionals and medical Data Scientists who deal with healthcare delivery and computational analysis, whose target is to extract actionable insights from patient feedback using modern AI techniques. [JJCIT 2025; 11(3.000): 390-404]

Keywords

• patient feedback
• sentiment analysis
• lexicon
• machine learning
• deep learning
• generative ai