The Impact of Under Coding of Cardiac Severity and Comorbid Diseases on the Accuracy of Hospital Report Cards

Medical Care ◽  
2005 ◽  
Vol 43 (8) ◽  
pp. 801-809 ◽  
Author(s):  
Peter C. Austin ◽  
Jack V. Tu ◽  
David A. Alter ◽  
C David Naylor
Keyword(s):  
Report Cards ◽  
Hospital Report ◽  
Comorbid Diseases ◽  
Hospital Report Cards ◽  
The Impact

Competition and the impact of online hospital report cards

2014 ◽  
Vol 34 ◽  
pp. 42-58 ◽  
Author(s):  
Shin-Yi Chou ◽  
Mary E. Deily ◽  
Suhui Li ◽  
Yi Lu
Keyword(s):  
Report Cards ◽  
Hospital Report ◽  
Hospital Report Cards ◽  
The Impact

Abstract 5: Another Inconvenient Truth: The Pernicious Effects of Hospital Case Volumes on the Accuracy of Hospital Report Cards

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Mathew J Reeves ◽  
Peter C Austin
Keyword(s):  
Risk Adjustment ◽  
Event Rate ◽  
Report Cards ◽  
Random Errors ◽  
Hospital Case ◽  
Hospital Report ◽  
Number Of Patients ◽  
Provider Volume ◽  
Hospital Report Cards ◽  
The Impact

Background: Hospital report cards are published with increasing frequency, but their accuracy is controversial, especially when case volumes are small. Our objective was to determine the relationship between hospital case volumes and the accuracy of hospital report cards using simulation studies. Methods: Monte Carlo simulations were used in a setting such that the true hospital rankings were known with certainty and perfect risk-adjustment was possible. Parameters used to generate simulated datasets were obtained from analyses of 31,000 hospitalized AMI patients in Ontario. We varied the number of patients per hospital from 100 to 2000 in increments of 100. For each scenario we simulated 500 datasets and determined the correlation between the true hospital ranking (determined by hospital-specific random effects) and the observed ranking (determined by observed vs. expected (O-E) or predicted vs. expected (P-E) ratios). Baseline simulations used the observed 30-day AMI mortality of 11%. In sensitivity analysis we explored the impact of using a lower event rate (2% mortality) commonly associated with cardiovascular procedures. Results: Provider volume had a strong effect on the accuracy of hospital report cards. When the event rate was high (11%), provider volume had to exceed 500 before the correlation between known and observed rankings exceeded 80%, and for hospitals with 200 or fewer cases the correlation was <60% (Figure). When the event rate was low (2%), provider volume had to exceed 1200 before the correlation exceeded 80%, and for hospitals with 200 or fewer cases the correlation was <40%. Conclusions: These results highlight the substantial amount of error that occurs when profiling hospitals even in the environment of perfect risk adjustment. These random errors increase with decreasing hospital volume and decreasing event rates. Hospital report cards display acceptable levels of accuracy only when provider volumes exceed those seen in many hospitals.

Hospital Report Cards

JAMA Surgery ◽  
2014 ◽  
Vol 149 (2) ◽  
pp. 143 ◽  
Author(s):  
Justin B. Dimick ◽  
Samantha K. Hendren
Keyword(s):  
Report Cards ◽  
Hospital Report ◽  
Hospital Report Cards

Optimal Statistical Decisions for Hospital Report Cards

2005 ◽  
Vol 25 (1) ◽  
pp. 11-19 ◽  
Author(s):  
Peter C. Austin ◽  
Geoffrey M. Anderson
Keyword(s):  
Report Cards ◽  
Hospital Report ◽  
Statistical Decisions ◽  
Hospital Report Cards

Impact of the choice of benchmark on the conclusions of hospital report cards

2004 ◽  
Vol 148 (6) ◽  
pp. 1041-1046 ◽  
Author(s):  
Peter C. Austin ◽  
David A. Alter ◽  
Geoffrey M. Anderson ◽  
Jack V. Tu
Keyword(s):  
Report Cards ◽  
Hospital Report ◽  
Hospital Report Cards

scholarly journals Accuracy of Hospital Report Cards Based on Administrative Data

2006 ◽  
Vol 0 (0) ◽  
pp. 060720074824035-??? ◽  
Author(s):  
Laurent G. Glance ◽  
Andrew W. Dick ◽  
Turner M. Osler ◽  
Dana B. Mukamel
Keyword(s):  
Administrative Data ◽  
Report Cards ◽  
Hospital Report ◽  
Hospital Report Cards
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