Online Reviews of Health Care Facilities.

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.