TY - JOUR
T1 - Recovery at the edge of error
T2 - Debunking the myth of the infallible expert
AU - Patel, Vimla L.
AU - Cohen, Trevor
AU - Murarka, Tripti
AU - Olsen, Joanne
AU - Kagita, Srujana
AU - Myneni, Sahiti
AU - Buchman, Timothy
AU - Ghaemmaghami, Vafa
N1 - Funding Information:
This research was supported by an award from the James S McDonnell Foundation (Grant No. 220020152 ). We would like to thank the attending physicians, residents, and nurses at both the hospital sites for their voluntary contribution to our study.
PY - 2011/6
Y1 - 2011/6
N2 - The notion that human error should not be tolerated is prevalent in both the public and personal perception of the performance of clinicians. However, researchers in other safety-critical domains have long since abandoned the quest for zero defects as an impractical goal, choosing to focus instead on the development of strategies to enhance the ability to recover from error. This paper presents a cognitive framework for the study of error recovery, and the results of our empirical research into error detection and recovery in the critical care domain, using both laboratory-based and naturalistic approaches. Both attending physicians and residents were prone to commit, detect and recover from errors, but the nature of these errors was different. Experts corrected the errors as soon as they detected them and were better able to detect errors requiring integration of multiple elements in the case. Residents were more cautious in making decisions showing a slower error recovery pattern, and the detected errors were more procedural in nature with specific patient outcomes. Error detection and correction are shown to be dependent on expertise, and on the nature of the everyday tasks of the clinicians concerned. Understanding the limits and failures of human decision-making is important if we are to build robust decision-support systems to manage the boundaries of risk of error in decision-making. Detection and correction of potential error is an integral part of cognitive work in the complex, critical care workplace.
AB - The notion that human error should not be tolerated is prevalent in both the public and personal perception of the performance of clinicians. However, researchers in other safety-critical domains have long since abandoned the quest for zero defects as an impractical goal, choosing to focus instead on the development of strategies to enhance the ability to recover from error. This paper presents a cognitive framework for the study of error recovery, and the results of our empirical research into error detection and recovery in the critical care domain, using both laboratory-based and naturalistic approaches. Both attending physicians and residents were prone to commit, detect and recover from errors, but the nature of these errors was different. Experts corrected the errors as soon as they detected them and were better able to detect errors requiring integration of multiple elements in the case. Residents were more cautious in making decisions showing a slower error recovery pattern, and the detected errors were more procedural in nature with specific patient outcomes. Error detection and correction are shown to be dependent on expertise, and on the nature of the everyday tasks of the clinicians concerned. Understanding the limits and failures of human decision-making is important if we are to build robust decision-support systems to manage the boundaries of risk of error in decision-making. Detection and correction of potential error is an integral part of cognitive work in the complex, critical care workplace.
KW - Cognitive informatics
KW - Decision making
KW - Error detection
KW - Error recovery
KW - Expertise
KW - Laboratory-based
KW - Naturalistic
KW - Patient safety
UR - http://www.scopus.com/inward/record.url?scp=79957623355&partnerID=8YFLogxK
U2 - 10.1016/j.jbi.2010.09.005
DO - 10.1016/j.jbi.2010.09.005
M3 - Article
C2 - 20869466
AN - SCOPUS:79957623355
SN - 1532-0464
VL - 44
SP - 413
EP - 424
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
IS - 3
ER -