Organizations involved in complex tasks have several ways to examine and improve the quality of their task performance. The most common approach is to assess how closely processes adhere to descriptors of ideal performance or match absolute or relative ideals. As an example, an industrial process involving inserting and tightening 2 screws can be evaluated by means of both process measures (the time required to attach and tighten the screws, variability in the time it takes, whether all screws are tightened to the same degree) and outcomes (how well the gear assembly works afterward and the breakdown rate of the gear assembly over time). Anesthesiologists are routinely involved in similarly complex care processes, in which both process measures (such as antibiotic timing) and outcome measures (such as postoperative reintubation) exist.A prerequisite to this approach to quality performance and work output is an assumption that the coupling between "ideal" task performance and outcome metrics is tight and easily identifiable. Clinical medical care, however, can introduce unpredictable variation into an already complex environment. Evolving care pathways, a high degree of complexity, unanticipated process changes, and diagnostic and therapeutic uncertainty may all cause clinical care to deviate from the expected. In addition, environmental constancy is not guaranteed. A process designed to optimize perioperative antibiotic delivery, for example, may suffer if new drug packaging increases the likelihood of a dangerous drug swap, if the process for identifying patient allergies is changed, or if epidemiological assumptions regarding perioperative pathogens change.The combination of high complexity, diagnostic/therapeutic uncertainty, and unanticipated environmental factors invites the possibility of an unexpected process or outcome failure. Importantly, such failures