Detecting adverse events for patient safety research: a review of current methodologies <s> Developing a taxonomy of medical errors <s> One critical step in addressing and resolving the problems associated with human errors is the development of a cognitive taxonomy of such errors. In the case of errors, such a taxonomy may be developed (1) to categorize all types of errors along cognitive dimensions, (2) to associate each type of error with a specific underlying cognitive mechanism, (3) to explain why, and even predict when and where, a specific error will occur, and (4) to generate intervention strategies for each type of error. Based on Reason's (1992) definition of human errors and Norman's (1986) cognitive theory of human action, we have developed a preliminary action-based cognitive taxonomy of errors that largely satisfies these four criteria in the domain of medicine. We discuss initial steps for applying this taxonomy to develop an online medical error reporting system that not only categorizes errors but also identifies problems and generates solutions. <s> BIB001 </s> Detecting adverse events for patient safety research: a review of current methodologies <s> Developing a taxonomy of medical errors <s> Is the solution for medical errors medical or cognitive? In this AMIA2001 panel on medical error, we argued that medical error is primarily an issue for cognitive science and engineering, not for medicine, although the knowledge of the practice of medicine is essential for the research and prevention of medical errors. The three panelists presented studies that demonstrate that cognitive research is the foundation for theories of medical errors and interventions of error reductions. <s> BIB002
Within this cognitive framework, errors are divided into two major categories: (1) slips or lapses that result from the incorrect execution of a correct action sequence and (2) mistakes that result from the correct execution of an incorrect action sequence  . To develop such cognitive theories, we need to start from the very definition of error: an error is the outcome of a system that fails to achieve the intended outcome in a planned sequence of mental or physical actions. In other words, errors are errors of actions. Therefore, a cognitive theory of medical errors is based on a basic theory of human actions. A cognitive theory of human action most appropriate for medical errors is the seven-stage action theory developed by Norman   and refined by Zhang and his colleagues BIB001 BIB002  . One critical step toward reducing medical errors in particular and human errors in general is a cognitive taxonomy of errors that can (1) categorize all types of medical errors along cognitive dimensions, (2) associate each type of medical errors to a specific underlying cognitive mechanism, (3) describe how, explain why, and even predict when and where a specific error will occur, and (4) generate intervention strategies for each type of error. Based on ReasonÕs  definition of human errors and NormanÕs cognitive theory of human action   , a preliminary action-based cognitive taxonomy of medical errors has been developed that largely satisfies these four criteria BIB001 . This taxonomy can categorize all types of errors (slips and mistakes) according to the stages of the action cycle. The authors have identified a set of cognitive mechanisms (substantial but not exhaustive) that underlie each type of slip or mistake. This taxonomy can also explain why and describe how a specific error occurs. With further development and refinement, it may have enough predictive power to help designers and implementers to anticipate more effectively when and where an error might occur. Finally, at a high, conceptual level, the authors have generated guidelines for the development of cognitive interventions and have proposed a framework for the development of medical error reporting systems that over time can monitor and provide solutions or enhance prevention of the kinds of errors that are reported-which may require collection of additional data elements at the time of error or adverse event detection.