Beneath the surface of talking about physicians: A statistical model of language for patient experience comments

• Published in: Patient experience journal 2019-07, Vol.6 (2), p.51-58
• Main Authors: Turpen, Taylor, Matthews, Lea, Guney, Senem
• Format: Article
• Language: English
• Subjects: language data; natural language processing (nlp); patient experience; patient satisfaction surveys
• ISSN: 2372-0247; 2372-0247
• DOI: 10.35680/2372-0247.1276

This study applies natural language processing (NLP) techniques to patient experience comments. Our goal was to examine the language describing care experiences with two groups of physicians: those with scores in the top 100 and those with scores in the bottom 100 among all physicians (n=498) who received scores from patient satisfaction surveys. Our analysis showed a statistically significant difference in the language used to describe care experiences with these two distinct groups of physicians. This analysis illustrates how to apply NLP techniques in categorizing and building a statistical model for language use in order to identify meaningful language and significant phrasing in a dataset of natural language. We provide a review of limited work at the intersection of language analysis and patient experience. We present our analysis and conclude with a discussion on what care providers and patient experience leaders can learn from language used in patient experience comments for the delivery of patient-centered care. Experience Framework This article is associated with the Innovation & Technology lens of The Beryl Institute Experience Framework. ( http://bit.ly/ExperienceFramework ) Access other PXJ articles related to this lens. Access other resources related to this lens