Outcome measures offer the potential advantage of pro- viding readouts on entire populations, rather than smaller popu- lation subsets, and they focus on the end results of care that are most important to clinicians and patients. The challenges with outcome measures are primarily in the risk-adjustment modeling methods, which, though never perfect, can substan- tially enhance the ability to compare outcomes across different delivery teams, settings, locations, and systems.20 Krumholz et have described 7 preferred attributes of models used for outcomes that are publicly reported (Table 5), which this the writing committee strongly believes should remain at the core of any performance measure that includes outcomes. 4.2.1. Level of Attribution/Aggregation Contributions of multiple healthcare providers across mul- tiple settings are reflected in outcomes associated with any particular episode of care, and this can be especially true in the case of PCI. In addition, various data sources and data systems are the window into that episode, such that the ability to aggregate data at the level of an individual clinician ver- sus a broader grouping (eg, practice or hospital) will depend on the types of data available and the outcomes being evalu- ated. Although data are increasingly available, most sources of information, like administrative claims data, generally lack adequate granularity to be of meaningful use for attribution of outcomes performance at the level of the individual provider, which makes aggregation of PCI outcomes more appropriate for the health system or hospital. 4.2.2. Infrequently Occurring Complications Certain outcomes could be of inarguable importance in PCI but occur rarely. Such outcomes are difficult to interpret at the individual-provider level simply because of the fact that low-frequency events in a small sample size will produce unreliable estimates of provider performance. 