Authors:
Thomas Loveday
1
;
Mark Wiggins
1
;
Marino Festa
2
and
David Schell
2
Affiliations:
1
Macquarie University, Australia
;
2
Children’s Hospital at Westmead, Australia
Keyword(s):
Expertise, Diagnosis, Feature Selection, Feature Extraction, Cues, Medicine.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Data Engineering
;
Feature Selection and Extraction
;
Information Retrieval
;
Information Retrieval and Learning
;
Medical Imaging
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Perception
;
Software Engineering
;
Theory and Methods
Abstract:
Medical expertise is typically denoted on the basis of experience, but this approach appears to lack validity and reliability. The present study investigated an innovative assessment of diagnostic expertise in medicine. This approach was developed from evidence that expert performance develops following the acquisition of cue associations in memory, which facilitates diagnostic pattern-recognition. Four distinct tasks were developed, for which the judicious extraction and selection of environmental cues may be advantageous. Across the tasks, performance clustered into two levels, reflecting competent and expert performance. These clusters were only weakly correlated with traditional methods of identifying domain experts, such as years of experience. The significance of this outcome is discussed in relation to training, evaluation and assessment.