scholarly journals Impact of Teaching Intensity and Sociodemographic Characteristics on CMS Hospital Compare Quality Ratings

2018 ◽  
Vol 33 (8) ◽  
pp. 1221-1223 ◽  
Author(s):  
Wenshuai Wan ◽  
C. Jason Liang ◽  
Richard Duszak ◽  
Christoph I. Lee
2019 ◽  
Vol 35 (2) ◽  
pp. 110-116 ◽  
Author(s):  
Jeff Liao ◽  
Emily Aaronson ◽  
Jungyeon Kim ◽  
Xiu Liu ◽  
Colleen Snydeman ◽  
...  

A variety of hospital characteristics, including teaching status, ownership, location, and size, have been shown to be associated with quality measure performance. The association of hospital characteristics, including teaching intensity, with performance on the Centers for Medicare & Medicaid Services (CMS) SEP-1 sepsis measure has not been well studied. Utilizing a statewide, all-payer database and the CMS Hospital Compare database, this study investigated the association of various hospital characteristics with early SEP-1 performance in 48 acute hospitals in Massachusetts. Hospital teaching intensity and Magnet designation did not have a statistically significant association with SEP-1 performance in multivariable linear modeling. However, SEP-1 performance was higher in smaller, for-profit hospitals with higher case mix index. This finding suggests that emergency department activity, hospital ownership, and patient complexity should be studied further across a larger geographic spectrum and longitudinally as hospitals implement efforts to reduce morbidity associated with sepsis.


Medical Care ◽  
2020 ◽  
Vol 58 (4) ◽  
pp. 376-383 ◽  
Author(s):  
John Fahrenbach ◽  
Marshall H. Chin ◽  
Elbert S. Huang ◽  
Mary K. Springman ◽  
Stephen G. Weber ◽  
...  

2020 ◽  
Vol 15 (7) ◽  
pp. 407-410
Author(s):  
Jianhui Hu ◽  
David R Nerenz

Using the Hospital Compare overall hospital quality star ratings and other publicly available data on acute care hospitals, we examined star ratings for the flagship hospitals of a set of multihospital health systems in the United States. We compared star ratings and hospital characteristics of flagship and nonflagship hospitals across and within 113 health systems. The system flagship hospitals had significantly lower star ratings than did nonflagship hospitals, and they did not generally have the highest star ratings in their own systems. Higher teaching intensity, larger bed size, higher uncompensated care, and higher disproportionate share hospital (DSH) patient percentage were all significantly associated with lower star ratings of flagship hospitals when compared with nonflagship hospitals across all health systems; the flagship hospital of a system was more likely to have the lowest star rating in its system if the difference in DSH percentage was relatively large between the flagship and nonflagship hospitals in that system.


2021 ◽  
Author(s):  
Hari Ramasubramanian ◽  
Satish Joshi ◽  
Ranjani Krishnan

BACKGROUND Popular online portals provide free and convenient access to user-generated quality reviews. Centers for Medicare and Medicaid Services (CMS) also provide patients with Hospital Compare Star Ratings (HCSR), a single public measure of hospital quality aggregating multiple quality dimensions. Consumers often use crowdsourced hospital ratings on platforms such as Google to select hospitals, but it is unknown if these ratings reflect a comprehensive measure of clinical quality. OBJECTIVE We analyze if Google online quality ratings, which reflect the wisdom of the crowd, are associated with HCSR, which reflect the wisdom of the experts. CMS revised the methodology of assigning star ratings to hospitals. Therefore, we analyze these associations before and after the 2021 revisions of the CMS rating system. METHODS We extracted Google ratings using Application Programming Interface (API) in June 2020. The HCSR data of April 2020 (before the revision of HCSR methodology) and April 2021 (after the revision of HCSR methodology) were obtained from CMS’ Hospital Compare (HC) website. We also extracted scores for the individual components of hospital quality for each of the hospitals in our sample using the code provided by HC. Fractional Response Model (FRM) was used to estimate the association between Google Ratings and HCSR and individual components of quality. RESULTS Results indicate that Google ratings are statistically associated with HCSR (P<.001) after controlling for hospital level effects. A one star improvement in CMS ratings before the change in methodology (after the change in methodology) is expected to increase the Google ratings by 0.145 (0.135) on average (95% CI 0.127- 0.163; P<.001, 95% CI 0.116-0.153; P<.001). The analyses with individual components of hospital quality reveal that Google ratings are not associated with components of HCSR that require medical expertise such as ‘Safety of care’ or ‘Readmissions’. The revised CMS rating system ameliorates previous partial inconsistencies in association between Google ratings and component scores of HCSR. CONCLUSIONS Overall, crowd sourced Google hospital ratings are informative about expert CMS hospital quality ratings and several individual quality components that are easier for patients to evaluate. Therefore, hospitals should not expect improvements in quality metrics that require expertise to assess such as safety of care and readmission to result in improved Google star ratings. Hospitals can benefit from using crowd-sourced ratings as timely, easily available, and dynamic indicators of their quality performance.


1971 ◽  
Author(s):  
A. George Gitter ◽  
Saul L. Franklin
Keyword(s):  

1979 ◽  
Vol 35 (2) ◽  
pp. 68-71 ◽  
Author(s):  
Robert A. Haugen

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