The Hospital Compare Mortality Model and the Volume–Outcome Relationship

Silber, Jeffrey H.; Rosenbaum, Paul R.; Brachet, Tanguy; Ross, Richard N.; Bressler, Laura J.; Even‐Shoshan, Orit; Lorch, Scott A.; Volpp, Kevin G.

doi:10.1111/j.1475-6773.2010.01130.x

Cited by 88 publications

(85 citation statements)

References 28 publications

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“…Silber et al 15 have demonstrated that models that ignore volume effects are highly unlikely, by design, to ever identify low-volume centers as outliers, a result that we have duplicated in our findings. 15 As prior reports suggest a clinically significant volume-outcome relationship for CAS, 24 we chose to include hospital volume in our risk-standardization models.…”

Section: Modeling Issuessupporting

confidence: 80%

“…It is possible that the inclusion of hospital variables more specific to CAS outcomes might provide better discrimination between hospitals. Silber et al 15 demonstrated the critical importance of including hospital-level predictors in the Hospital Compare method of investigating risk-standardized outcomes, and in fact, our study revealed several hospital factors that were independent predictors of adverse outcomes. As hospitals are currently required to report structural characteristics to Medicare in the course of obtaining CAS recertification, this may be an ideal mechanism for collecting data that could be incorporated in a more detailed hospital-level mortality model.…”

Section: Limitationsmentioning

confidence: 60%

“…14,15 Unlike patient-level effects, hospital effects in a hierarchical (Bayesian) model influence the prior assumption about hospital performance; thus if lowvolume hospitals as a class have worse outcomes, the prior mortality assumption at any given low-volume hospital will be higher than at high-volume hospitals. Silber et al 15 have demonstrated that models that ignore volume effects are highly unlikely, by design, to ever identify low-volume centers as outliers, a result that we have duplicated in our findings. 15 As prior reports suggest a clinically significant volume-outcome relationship for CAS, 24 we chose to include hospital volume in our risk-standardization models.…”

Section: Modeling Issuesmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of Clinical Outcomes From Carotid Artery Stenting Among US Hospitals

Epstein

Yang

et al. 2014

Circ: Cardiovascular Quality and Outcomes

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Background— The Centers for Medicare and Medicaid Services require hospitals performing carotid artery stenting (CAS) to recertify the quality of their programs every 2 years, but currently this involves no explicit comparisons of postprocedure mortality across hospitals. Hence, the current recertification process may fail to identify hospitals that are performing poorly in relation to peer institutions. Our objective was to compare risk-standardized procedural outcomes across US hospitals that performed CAS and to identify hospitals with statistically high postprocedure mortality rates. Methods and Results— We conducted a retrospective cohort study of Medicare beneficiaries who underwent CAS from July 2009 to June 2011 at 927 US hospitals. Thirty-day risk-standardized mortality rates were calculated using the Hospital Compare statistical method, a well-validated hierarchical generalized linear model that included both patient-level and hospital-level predictors. Claims were examined from 22 708 patients undergoing CAS, with a crude 30-day mortality rate of 2.0%. Risk-standardized 30-day mortality rates after CAS varied from 1.1% to 5.1% ( P <0.001 for the difference). Thirteen hospitals had risk-standardized mortality rates that were statistically ( P <0.05) higher than the national mean. Conversely, 5 hospitals had risk-standardized mortality rates that were statistically ( P <0.05) lower than the national mean. Conclusions— We used administrative claims to identify several CAS hospitals with excessively high 30-day mortality after carotid stenting. When combined with information currently used by Medicare for CAS recertification, such as clinical registry data and program reports, clinical outcomes comparisons could enhance Medicare’s ability to identify hospitals that are questionable candidates for recertification.

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Section: Modeling Issuessupporting

confidence: 80%

Section: Limitationsmentioning

confidence: 60%

Section: Modeling Issuesmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison of Clinical Outcomes From Carotid Artery Stenting Among US Hospitals

Epstein

Yang

et al. 2014

Circ: Cardiovascular Quality and Outcomes

View full text Add to dashboard Cite

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“…Very few hospitals were identified as having mortality that differed from the national rate (99.5 percent were labeled as having heart attack mortality that was no different than the national rate). 15 Thus, this report was unlikely to influence patients' or hospitals' behavior. Hospital Compare reported hospitalspecific levels of mortality performance in 2008; however, the introduction of this mortality reporting was too late in our study period to make a practical difference in our results.…”

mentioning

confidence: 82%

Medicare’s Public Reporting Initiative On Hospital Quality Had Modest Or No Impact On Mortality From Three Key Conditions

2012

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Hospital Compare, Medicare's public reporting initiative, began reporting measures of hospital quality for almost all US acute care hospitals in 2005. The impact of this public reporting initiative on patient mortality is unknown. We used Medicare claims data from the period 2000-08 to estimate the effect of Hospital Compare on thirty-day mortality for heart attack, heart failure, and pneumonia. Our analysis indicates that the fact that hospitals had to report quality data under Hospital Compare led to no reductions in mortality beyond existing trends for heart attack and pneumonia and led to a modest reduction in mortality for heart failure. We conclude that Medicare's public reporting initiative for hospitals has had a minimal impact on patient mortality.

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“…Although there is very little data about any one small hospital, hence very little data to check a statement about one small hospital, there is plenty of data about small hospitals as a group. When Hospital Compare's predictions for all small hospitals are added up, it is unambiguously clear that the risk at small hospitals as a group is well above the national average; see Silber et al (2010).…”

Section: Individualized Bayes Predictions Should Calibrate With Soundmentioning

confidence: 99%

Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare

George

Ročková

Rosenbaum

et al. 2017

Journal of the American Statistical Association

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Bayesian models are increasingly fit to large administrative data sets and then used to make individualized recommendations. In particular, Medicare's Hospital Compare webpage provides information to patients about specific hospital mortality rates for a heart attack or Acute Myocardial Infarction (AMI). Hospital Compare's current recommendations are based on a random-effects logit model with a random hospital indicator and patient risk factors. Except for the largest hospitals, these individual recommendations or predictions are not checkable against data, because data from smaller hospitals are too limited to provide a meaningful check. Before individualized Bayesian recommendations, people derived general advice from empirical studies of many hospitals; e.g., prefer hospitals of type 1 to type 2 because the risk is lower at type 1 hospitals. Here we calibrate these Bayesian recommendation systems by checking, out of sample, whether their predictions aggregate to give correct general advice derived from another sample. This process of calibrating individualized predictions against general empirical advice leads to substantial revisions in the Hospital Compare model for AMI mortality. In order to make appropriately calibrated predictions, our revised models incorporate information about hospital volume, nursing staff, medical residents, and the hospital's ability to perform cardiovascular procedures. For the ultimate purpose of comparisons, hospital mortality rates must be standardized to adjust for patient mix variation across hospitals. We find that indirect standardization, as currently used by Hospital Compare, fails to adequately control for differences in patient risk factors and systematically underestimates mortality rates at the low volume hospitals. To provide good control and correctly calibrated rates, we propose direct standardization instead.

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The Hospital Compare Mortality Model and the Volume–Outcome Relationship

Cited by 88 publications

References 28 publications

A Comparison of Clinical Outcomes From Carotid Artery Stenting Among US Hospitals

A Comparison of Clinical Outcomes From Carotid Artery Stenting Among US Hospitals

Medicare’s Public Reporting Initiative On Hospital Quality Had Modest Or No Impact On Mortality From Three Key Conditions

Mortality Rate Estimation and Standardization for Public Reporting: Medicare’s Hospital Compare

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