Development of Visual Diagnostic Expertise in Pathology: An Information-processing Study

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 computerbased pedagogy tools in this domain ■ J Am Med Inform Assoc. 2003;10:39–51. DOI 10.1197/jamia.M1123. Workbench (PAW). This work was supported by the National Library of Medicine Medical Informatics Training Grant Number 5-T15LM07059 and a grant from the University of Pittsburgh School of Medicine–Pathology Postdoctoral Research Training Program (PPRTP). Preliminary findings from this study were reported in the Proceedings of the AMIA Fall Symposium, 2001. 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: . Received for publication: 3/14/02; accepted for publication: 8/28/02. by gest on N ovem er 4, 2016 ht://jam ia.oxfournals.org/ D ow nladed from Microscopic pathology, a subdiscipline of pathology, focuses on diagnosis of disease by histologic examination. Patients’ tissues and cells obtained during biopsies, aspirates, and operations are permanently affixed to glass slides, stained, and examined. The assignment of a pathologic diagnosis is critical for any patient with cancer and for many other diseases. Pathologic diagnostic classification is reported to the referring physician and determines therapy and prognosis. Although models of the diagnostic process in microscopic pathology have been advanced,1,2 few, if any, have achieved empirical validation. No previous studies have identified key features required to establish human expertise in this domain. Pathology residencies typically require 5 years of training, approximately half of which is devoted to acquiring skills in diagnostic surgical pathology. To develop expertise, trainees require long residencies (and often additional subspecialty fellowships) to expose them to a sufficiently large number of cases, including a wide variety of rare and unusual patterns. This study used information-processing and cognitive science methodologies to compare the visual diagnostic processes of novice, intermediate, and expert pathologists. Two goals were sought: (1) to understand basic cognitive processes underlying visual diagnostic expertise and (2) to gain insights useful for developing an intelligent Pathology tutoring system.3 Computer-based educational applications in microscopic pathology diagnosis can supplement traditional training by exposing trainees to large numbers of rare patterns in a short time. Intelligent tutoring systems (ITS) have an advantage over standard computer-assisted instruction (CAI) because they provide a simulated, realistic task environment in which individualized coaching and feedback can occur. Previous ITS work indicates that successful systems closely couple content with empirical research to (1) define the tutoring task, (2) characterize the scaffold of steps to expertise for tutoring, (3) determine the cognitive rules forming the basis of expertise in that domain, and (4) identify important errors and misconceptions made by students. Following previous work in similar domains,4–6 we designed our study to analyze the development of expertise.