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The impact of undercoding of cardiac severity and comorbid diseases on the accuracy of hospital report cards

Austin PC, Tu JV, Alter DA, Naylor CD. Med Care. 2005; 43(8):801-9.


Context — Hospital report cards usually are based on administrative discharge abstracts. However, cardiac severity and comorbidities generally are under-reported in administrative data.

Objective — We sought to determine how undercoding of cardiac severity and comorbidities affects the determination that some hospitals are high-mortality outliers.

Design — Simulations using retrospective data on 18,795 patients admitted with an acute myocardial infarction (AMI) to 109 acute care hospitals in Ontario.

Main Outcome Measure — Change in the number of hospitals that remained high-mortality outliers after adjusting for potentially increased prevalence of as many as 9 separate measures of cardiac severity and comorbid conditions, individually or together.

Results — For most measures of cardiac severity and comorbidities, increasing the prevalence of each factor to the highest observed hospital-specific prevalence seldom altered the status of high-mortality outlier hospitals. Increases in the prevalence of cardiogenic shock or acute renal failure to even the median level led to reclassification of up to 4 of the 12 high-mortality outlier hospitals to nonoutlier status. Most high-mortality outlier hospitals were reclassified if the maximum prevalence was imputed for these 2 factors. Simultaneously increasing the prevalence of all comorbidities to the median level typically converted the status of about half the outlier hospitals. Not until the prevalence of all measures of cardiac severity and comorbidities were simultaneously increased to the maximum observed hospital-specific prevalence, did all hospitals initially classified as high-mortality outliers revert to nonoutlier status.

Conclusions — Undercoding of severity and comorbidities in administrative data in itself is very unlikely to account for the outlier status of most hospitals. However, some potential for misclassification of individual institutions exists if influential factors are variably coded.

Keywords: Data evaluation Health informatics Hospitals

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