TY - JOUR
T1 - A cognitive taxonomy of medical errors
AU - Zhang, Jiajie
AU - Patel, Vimla L.
AU - Johnson, Todd R.
AU - Shortliffe, Edward H.
N1 - Funding Information:
Two preliminary, short reports of this study, which were customized to target the cognitive science community and the medical informatics community, were presented and published at the 24th Annual Conference of the Cognitive Science Society and at the 2002 Annual Symposium of American Medical Informatics Association [44,45] . The current article is a much longer paper with substantial extensions and new developments. The Project/effort depicted was sponsored by the US Dept of Army under Cooperative Agreement #DAMD17-97-2-7016. The content of the information does not necessarily reflect the position/policy of the government or NMTB, and no official endorsement should be inferred. We thank James P. Turley and Julie Brixey for valuable comments.
PY - 2004/6
Y1 - 2004/6
N2 - Objective. Propose a cognitive taxonomy of medical errors at the level of individuals and their interactions with technology. Design. Use cognitive theories of human error and human action to develop the theoretical foundations of the taxonomy, develop the structure of the taxonomy, populate the taxonomy with examples of medical error cases, identify cognitive mechanisms for each category of medical error under the taxonomy, and apply the taxonomy to practical problems. Measurements. Four criteria were used to evaluate the cognitive taxonomy. The taxonomy should be able (1) to categorize major types of errors at the individual level along cognitive dimensions, (2) to associate each type of error with a specific underlying cognitive mechanism, (3) to describe how and explain why a specific error occurs, and (4) to generate intervention strategies for each type of error. Results. The proposed cognitive taxonomy largely satisfies the four criteria at a theoretical and conceptual level. Conclusion. Theoretically, the proposed cognitive taxonomy provides a method to systematically categorize medical errors at the individual level along cognitive dimensions, leads to a better understanding of the underlying cognitive mechanisms of medical errors, and provides a framework that can guide future studies on medical errors. Practically, it provides guidelines for the development of cognitive interventions to decrease medical errors and foundation for the development of medical error reporting system that not only categorizes errors but also identifies problems and helps to generate solutions. To validate this model empirically, we will next be performing systematic experimental studies.
AB - Objective. Propose a cognitive taxonomy of medical errors at the level of individuals and their interactions with technology. Design. Use cognitive theories of human error and human action to develop the theoretical foundations of the taxonomy, develop the structure of the taxonomy, populate the taxonomy with examples of medical error cases, identify cognitive mechanisms for each category of medical error under the taxonomy, and apply the taxonomy to practical problems. Measurements. Four criteria were used to evaluate the cognitive taxonomy. The taxonomy should be able (1) to categorize major types of errors at the individual level along cognitive dimensions, (2) to associate each type of error with a specific underlying cognitive mechanism, (3) to describe how and explain why a specific error occurs, and (4) to generate intervention strategies for each type of error. Results. The proposed cognitive taxonomy largely satisfies the four criteria at a theoretical and conceptual level. Conclusion. Theoretically, the proposed cognitive taxonomy provides a method to systematically categorize medical errors at the individual level along cognitive dimensions, leads to a better understanding of the underlying cognitive mechanisms of medical errors, and provides a framework that can guide future studies on medical errors. Practically, it provides guidelines for the development of cognitive interventions to decrease medical errors and foundation for the development of medical error reporting system that not only categorizes errors but also identifies problems and helps to generate solutions. To validate this model empirically, we will next be performing systematic experimental studies.
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U2 - 10.1016/j.jbi.2004.04.004
DO - 10.1016/j.jbi.2004.04.004
M3 - Article
C2 - 15196483
AN - SCOPUS:2942601198
SN - 1532-0464
VL - 37
SP - 193
EP - 204
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
IS - 3
ER -