Natural Language Processing of Sentiments Identified in Patient Comments Associated with Less Than Top-Rated Care

Background: Natural language processing (NLP) analysis of patient comments about their care can inform improvement initiatives. Objective: We used NLP to quantify sentiments and identify topics in patient comments associated with submaximal ratings of experience. Methods: Using a set of 1117 patient comments associated with ratings 1-4 out of 5 from a commercial source, we analyzed associated sentiments measured by Linguistic Inquiry and Word Count software and associated themes using topic modeling. Results: In the sentiment analysis, positive sentiments were associated with better numerical ratings while word count, numbers, ethnicity, and negative tones were associated with lower ratings. Topics of "listening, concern, and collaboration" were associated with 1-star ratings and "logistics" and "pain" with 4-star ratings. Conclusion: The finding that NLP analysis of comments from submaximal patient ratings of experience is consistent with evidence that the worst ratings are associated with relationship issues and more moderate ratings are associated with process issues affirms the ability of NLP to analyze large amounts of patient comments to identify opportunities to improve patient experience of care.