Development of Visual Diagnostic Expertise in Pathology - An Information-processing Study
- Affiliations of the authors: Center for Pathology Informatics, Center for Biomedical Informatics, Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania (RSC); Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh Pennsylvania (GJN); Center for Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh Pennsylvania (CPF); Department of Pathology and Laboratory Medicine, University of Kentucky Medical Center, Lexington, Kentucky (JS)
- Correspondence and reprints: Rebecca S. Crowley, MD, MS, Center for Pathology Informatics, UPMC Shadyside Cancer Pavilion–307, 5230 Centre Avenue, Pittsburgh, PA 15232; e-mail: <crowleyrs{at}msx.upmc.edu>
- Received 14 March 2002
- Accepted 28 August 2002
Abstract
Objective To identify key features contributing to trainees’ development of expertise in microscopic pathology diagnosis, a complex visual task, and to provide new insights to help create computer-based training systems in pathology.
Design Standard methods of information-processing and cognitive science were used to study diagnostic processes (search, perception, reasoning) of 28 novices, intermediates, and experts. Participants examined cases in breast pathology; each case had a previously established gold standard diagnosis. Videotapes correlated the actual visual data examined by participants with their verbal “think-aloud” protocols.
Measurements Investigators measured accuracy, difficulty, certainty, protocol process frequencies, error frequencies, and times to key diagnostic events for each case and subject. Analyses of variance, chi-square tests and post-hoc comparisons were performed with subject as the unit of analysis.
Results Level of expertise corresponded with differences in search, perception, and reasoning components of the tasks. Several discrete steps occur on the path to competence, including development of adequate search strategies, rapid and accurate recognition of anatomic location, acquisition of visual data interpretation skills, and transitory reliance on explicit feature identification.
Conclusion Results provide the basis for an empirical cognitive model of competence for the complex tasks of microscopic pathology diagnosis. Results will inform the development of computer-based pedagogy tools in this domain
