The relationship of the value of outcome comparisons to the number of patients per provider

Author(s): Kuhn, E. M., Krakauer, H.

Journal/Book: Int J Qual Health Care. 1997; 9: The Boulevard, Langford Lane, Kidlington, Oxford, England OX5 1GB. Pergamon-Elsevier Science Ltd. 247-254.

Abstract: Purpose: Monte Carte methods were used to assess how the value of outcome comparisons depends on the number of patients per provider. Methods: We simulated two patient data sets that have been used for wed-known studies of outcome comparisons: mortality rates for coronary artery bypass surgeons from New York and Pennsylvania, and 30-day hospital mortality rates of Medicare patients from a national data set. In the simulated data sets, each surgeon or hospital provider was assigned a true or underlying probability of mortality. Results: For the simulated CABG surgery data set, the underlying probability of mortality explained 30% of the variation in the observed mortality rate when there were 100 patients per physician, and 63% when there were 400 patients. The positive predictive value of using an observed mortality rate in the bottom 10% to identify a surgeon whose underlying probability of mortality was in the bottom 10% was 31% for 100 patients and 59% for 400 patients, The relationship between underlying and observed rates was weaker in the simulated Medicare data set with the same number of patients per provider. For a given data set, the amount of random variation in the observed rates of adverse outcomes among providers can be estimated with a simple equation. Conclusions: The results show that the assessment of provider outcomes may be greatly affected by random variation, An indication of the amount of random variation in a given data set can be obtained from the examples in this study and an equation for estimating random variation.

Note: Article Hartz AJ, Med Coll Wisconsin, Dept Family & Community Med, 1000 N 92ND St, Milwaukee,WI 53226 USA

Keyword(s): outcomes studies; risk-adjusted outcomes; sample size; random variation; Monte Carlo; HOSPITAL MORTALITY; SEVERITY; ILLNESS; QUALITY; RATES

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