ArticleJAMA network open2025
Online Reviews of Health Care Facilities.
Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.