focus should be more on enhancing knowledge about risks and how to prevent them rather than on blaming, shaming, and punishing individuals. This process of understanding is the lynchpin of an effective safety culture, and its importance points to the main deficiencies in existing standards for representing potential or actual adverse medical events. One hallmark of effective analysis of adverse events is that it leads to system changes that inherently make it easier for those working in a health care delivery environment to do the job right, as opposed to a constant emphasis on more education or closer oversight—both second-hand markers for blame. Since much of health care is organized around the convenience of clinicians, however, it is important to note that interventions that alter the sequence of work flow are more challenging to implement.

Efforts such as those of ORC Macro⁴ and DQIP extend the breadth of the nomenclature needed for adverse event systems by including errors of omission. In the latter cases, in addition to characterizing errors and near-miss events by specifying what, which, when, and where, there is a need for additional elements or classification.

For example, an error of commission, such as the ICD-9, Clinical Modification (CM) measure of foreign body left in during a procedure, may be adequately characterized by knowing the probable cause for leaving the foreign body in (why), the conditions under which it occurred (when and where), and the people present for the procedure (who). On the other hand, analysis of an error of omission (e.g., DQIP measure for HbA1C count), could benefit from more data about the patient. The DQIP measures indicate the specific patient data required to confirm a diagnosis of diabetes (prescription or dispensing of insulin and/or oral hypoglycemics/ antihyperglycemics during the reporting year, exclusion of women with gestational diabetes). To assess errors of omission, the dataset to compare HbA1C test rates should be expanded to include data about how the diagnosis was established, in addition to data for risk stratification or covariate analysis.