A cognitive taxonomy of medical errors.
OBJECTIVE
Propose a cognitive taxonomy of medical errors at the level of individuals and their interactions with technology.


METHODS
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.


METHODS
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.


CONCLUSIONS
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.
Zhang
Jiajie
J
School of Health Information Sciences, University of Texas Health Science Center at Houston, 7000 Fannin, Suite 600, 77030, USA. jiajie.zhang@uth.tmc.edu
Patel
Vimla L
VL
Johnson
Todd R
TR
Shortliffe
Edward H
EH
D003071:Cognition; D019336:Cognitive Science / Q000379:methods; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D016247:Information Storage and Retrieval / Q000379:methods*; D019300:Medical Errors / Q000145:classification* / Q000517:prevention & control*; D016347:Medical Records Systems, Computerized; D008954:Models, Biological; D012308:Risk Management / Q000379:methods