Abstract
IMPORTANCE: Understanding patient experience is crucial for improving health care delivery. However, language patterns and themes correlated with negative or positive ratings are not well known. OBJECTIVE: To examine online reviews of US health care facilities, identifying language patterns and themes associated with negative or positive ratings. DESIGN, SETTING, AND PARTICIPANTS: For this cross-sectional study, all reviews of US health care facilities offering essential health benefits, as defined by the Affordable Care Act, posted on 1 online platform (Yelp.com) under "Health & Medical" from January 1, 2017, to December 31, 2023, were obtained. Reviews are posted voluntarily with ratings (1 star = lowest, 5 stars = highest) and open-ended review narratives regarding patients' care experiences. MAIN OUTCOMES AND MEASURES: The primary outcome was the correlation between n-grams (1- to 3-word sequences) and review ratings (negative: 1 or 2 stars; positive: 4 or 5 stars). Secondary measures included linguistic analysis and topic modeling based on standard machine-learning algorithms. Machine-learning and natural-language processing, including n-gram correlation, linguistic feature analysis, and topic modeling, were applied to determine correlations with review star ratings. RESULTS: A total of 1 099 901 online reviews from 138 605 facilities were identified over the 7-year study period. Among these, nearly one-half (46.3%) were negative and one-half (50.1%) were positive, with a median (IQR) rating of 4 (1-5) stars. The word "not" was most correlated with negative ratings (r = 0.31; 95% CI, 0.31-0.32), whereas "and" was most correlated with positive ratings (r = 0.35; 95% CI, 0.35-0.36). Among 200 topics, the strongest negative correlations involved payment issues (r = 0.25; 95% CI, 0.25-0.25) and poor treatment (r = 0.24; 95% CI, 0.23-0.24); the strongest positive correlations involved kindness (r = 0.32; 95% CI, 0.32-0.32) and anxiety relief (r = 0.32; 95% CI, 0.32-0.32). CONCLUSIONS AND RELEVANCE: In this cross-sectional analysis, negative patient experiences frequently centered on quality of communication and administrative issues. Negative feedback centered on unmet expectations, whereas positive reviews emphasized supportive staff interactions. Incorporating real-time online-review data into existing quality-improvement frameworks-such as patient experience dashboards or service recovery protocols-could help clinicians, administrators, and policymakers identify emerging concerns, monitor patient sentiment, and tailor interventions that enhance patient-centered care across diverse health care settings.