{"doi":"10.1001/jamanetworkopen.2025.24505","title":"Online Reviews of Health Care Facilities","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.","journal":"JAMA Network Open","year":2025,"id":534320,"datarank":0.16479184330021646,"base_score":1.0986122886681096,"endowment":1.0986122886681096,"self_citation_contribution":0.16479184330021646,"citation_network_contribution":0.0,"self_endowment_contribution":0.16479184330021646,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7219,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":710201,"name":"Sharath Chandra Guntuku","orcid":"0000-0002-2929-0035","position":1,"is_corresponding":false},{"id":1315810,"name":"Lauren Southwick","orcid":"0000-0001-5290-9759","position":2,"is_corresponding":false},{"id":692633,"name":"Raina M. Merchant","orcid":"0000-0002-9801-6881","position":3,"is_corresponding":false},{"id":789984,"name":"Anish K. Agarwal","orcid":"0000-0003-2175-0196","position":4,"is_corresponding":false},{"id":1010504,"name":"Neil K. R. Sehgal","orcid":"0000-0003-2532-8531","position":0,"is_corresponding":true}],"reference_count":16,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:51:47.434742Z","pmid":"40748639","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}