Leveraging AI to Drive Timely Improvements in Patient Experience Feedback: Algorithm Validation

• Published in: JMIR medical informatics Vol. 13; p. e60900
• Main Authors: Khanbhai, Mustafa, Carenzo, Catalina, Aryasinghe, Sarindi, Manton, David, Mayer, Erik
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 10.07.2025
• Subjects: Algorithms; Artificial Intelligence; Automatic text analysis; Automation; Classification; England; Feedback; Humans; Language; Large language models; Libraries; Machine Learning; Multimedia; Natural Language Processing; New Methods; Original Paper; Participatory Medicine & E-Patients; Patient Engagement and Empowerment; Patient Satisfaction; Python; Quality Improvement; R&D; Real time; Research & development; Responsible Health AI; Sentiment analysis; Servers; Software; Support vector machines; Text categorization; Tools, Programs and Algorithms; England; United Kingdom > UK; free-text analysis; natural language processing; patient experience; friends and family test; quality improvement; patient feedback; health informatics; machine learning; artificial intelligence; algorithm

Understanding and improving patient care is pivotal for health care providers. With increasing volumes of the Friends and Family Test (FFT) data in England, manual analysis of this patient feedback poses challenges for many health care organizations. This underscores the importance of automated text...