Numerous studies have shown the superiority of clinical over administrative data for these purposes.728 731,733,734 The latter data lack critical clinical variables that are necessary for adequate risk adjust- ment,732,735 they may confuse comorbidities and complica- tions, and they may contain inaccurate case numbers and mortality rates. Outcomes measures, such as mortality, should always be adjusted for patient severity on admission (ie, risk-adjusted or risk-standardized )777 780; otherwise, pro- viders will be hesitant to care for severely ill patients, who are more likely to die from their disease. In addition, if a hospital or surgeon is found to be a low-performing outlier on the basis of unadjusted results, the hospital or surgeon may claim that their patients were sicker. Statistical methodologies should account for small sample sizes and clustered patient observations within institutions, and hierarchical or random- effects models have been advocated by some investiga- tors.743,781 787 Point estimates of outcomes should always be accompanied by measures of statistical uncertainty, such as CIs. The units of analysis and reporting for provider profiling also have implications. Surgeon-level reports are published together with hospital reports in several states, but their smaller sample sizes typically require data aggregation over several years. Surgeon-level reporting may also increase the potential for risk aversion, as the anticipated worse results of the highest-risk patients are not diluted by the larger volume of a hospital or group. 