In this study, we find that high-risk patients undergoing CABG surgery are more likely to be treated by high-quality surgeons than by low-quality surgeons. We find no evidence, in this study, that high-quality surgeons are selectively avoiding high-risk patients. To our knowledge, this is the first study to examine whether high-risk patients are less likely to receive CABG surgery from high-quality surgeons compared with lower-risk patients.
From the perspective of optimizing overall outcomes, it is reassuring to see that high-quality surgeons are more likely to operate on the most severely ill patients. This study is not able to determine the mechanisms for these findings. Referring physicians may preferentially send the sickest patients to the highest quality surgeons while avoiding sending these high-risk patients to low-quality surgeons. It is possible that some low-quality surgeons are rejecting more difficult cases based on their perception of their own abilities. Further work is necessary to better understand the underlying mechanisms for these findings.
The findings of this study should be interpreted with caution because this study has several limitations. First, although we were able to control for the effects of patient race and ethnicity, we could not control for patient income level or insurance coverage. However, since patient race and ethnicity tend to be correlated with education level, income, and insurance coverage (Smedley, Stith, and Nelson 2003
), the impact of omitting these SES variables will be attenuated in this analysis. Furthermore, prior work has demonstrated that patients with better insurance coverage (Mukamel, Murthy, and Weimer 2000
) and higher income levels (Rothenberg et al. 2004
) tend to be treated by higher-quality surgeons. Assuming that patients with better insurance coverage and higher income levels tend to be healthier, then it is likely that omitting these variables from our model would have led us to underestimate the likelihood that high-risk patients will be treated by high-quality surgeons.
Second, the inverse relation between surgeon quality and patient risk could appear to follow from the mathematical relationship between surgeon quality, measured by the ratio of the observed-to-expected mortality rate, and patient risk, quantified using patient expected mortality rate. On initial examination of this mathematical relationship, it would appear that a surgeon with a higher expected mortality rate would necessarily have a lower O-to-E ratio. However, a surgeon with a sicker case mix would also be expected to have a higher observed mortality rate, causing both the numerator (OMR) and the denominator (EMR) in the O-to-E ratio to increase. Furthermore, patient outcome, and therefore the surgeon's observed mortality rate, is assumed to be a function of both baseline patient risk factors and surgeon quality. To expect the surgeon's O-to-E ratio to simply decrease with increases in patient risk would imply that surgeon quality and patient severity-of-disease do not have a significant impact on patient outcome. Clearly, patient severity-of-disease is an important determinant of patient outcome. And, there is extensive literature documenting that there is significant variability in outcomes across surgeons for both cardiac (O'Connor et al. 1991
; Hannan et al. 1994
) and noncardiac procedures after controlling for patient case mix (Birkmeyer et al. 2003
). Assuming an unbiased prediction model, the O-to-E ratio should only be a function of surgeon quality, and would not be expected to decrease (or increase) simply because of changes in surgeon case mix.
Furthermore, the patient-level expected mortality rate is a stochastic variable. Our analysis is based on a regression model in which the dependent variable is the surgeon O-to-E ratio and the independent variable is the patient-level expected mortality rate. Errors in the patient-level expected mortality rate would be associated with inverse changes in the surgeon O-to-E ratio. But, the contribution of the error term that is due to the estimation of the patient-level estimated mortality rate should be small as long as the physician volume is relatively large. Therefore, the mechanically induced bias resulting from having the “same” variable on both sides should be small. In fact, if a surgeon has “n” patients, then the error term on the right hand side is “1/n” times the error in the estimation of the patient-level expected mortality rate.
Third, measures of physician quality may be sensitive to the choice of risk adjustment model (Iezzoni 1997
; Glance, Osler, and Dick 2002a
), unobserved risk factors (Lilford et al. 2004
), sample sizes (Dimick, Welch, and Birkmeyer 2004
), the use of in-hospital mortality versus 30-day mortality, and the choice of modeling methodology (Glance et al. 2006
). It is not possible to know with absolute certainty whether or not quality rankings used as the basis for our analysis accurately reflect true surgeon quality.
Fourth, it could be argued that a subset of surgeons designated as “high-quality” surgeons are identified as high-quality precisely because they are systematically upcoding patient risk factors. If extensive miscoding is occurring, then it is possible that a subset of surgeons is receiving credit for treating higher risk patients. In theory, this group of surgeons would be both more likely to be mislabeled as high quality (because their predicted mortality rate would increase) and to be found caring for higher-risk patients (due to upcoding of patient risk factors). Even though the NYS DOH conducts extensive audits, it is not possible to completely insure the accuracy of the data. We cannot rule out upcoding of patient risk factors as an explanation for our findings.
Fifth, our analysis may be biased if we have incorrectly estimated patient risk. In our conceptual model, we hypothesized that high-risk patients may be “similar” to racial and ethnic minorities with respect to access to high-quality physicians. In the case of racial/ethnic minorities, patient race is relatively fixed (of course, one can argue that patient race may differ depending on whether it is assigned by the provider or by the patient). In the case of “high-risk” patients, risk level is a function of observable and unobservable risk factors, and is subject to potential bias caused by the omission of important risk factors and model misspecification. Moreover, surgeon quality is also based on the same set of patient risk factors, and is also subject to misspecification and omitted variable bias.
Finally, this study does not consider the possibility that high-risk patients who would benefit from CABG surgery are unable to find a surgeon willing to operate on them. This study only considers patients who actually underwent CABG.
Despite these limitations, we believe that the findings of this study are relatively robust. Our analysis controlled for patient race and ethnicity, which are very important drivers of patient access. The use of clinical, as opposed to administrative data to identify patient risk groups and to rank surgeon quality, is a significant strength of this analysis. Furthermore, since NYS has one of the earliest and most credible CABG report cards, the use of data from NYS is ideal for exploring the impact of public release of outcome data on patient access to high-quality physicians.