The relationship of the value of outcome comparisons to the number of patients per provider.
OBJECTIVE
Monte Carlo 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 well-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 patient 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.
Hartz
A J
AJ
Medical College of Wisconsin, Department of Family & Community Medicine, Milwaukee 53226, USA.
Kuhn
E M
EM
Krakauer
H
H
D003198:Computer Simulation; D001026:Coronary Artery Bypass / Q000401:mortality*; D006302:Health Services Research / Q000379:methods; D017052:Hospital Mortality; D006761:Hospitals / Q000592:standards / Q000706:statistics & numerical data; D006801:Humans; D006278:Medicare; D009010:Monte Carlo Method; D009518:New York / Q000453:epidemiology; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010414:Pennsylvania / Q000453:epidemiology; D010820:Physicians / Q000592:standards / Q000706:statistics & numerical data; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology; D016526:Workload