Abstract
Accurate disease diagnosis is the cornerstone of patient care, directly impacting the quality of health care delivery and patient outcomes. Developing diagnostic competence is a critical goal of pathology residency training. This study uses longitudinal data from trainees during pathology residency to examine the association between years of training and performance in 4 key interpretive phases: detecting critical regions, recognizing their relevance, describing histopathological features, and rendering an accurate diagnosis. Using a study set of 32 digital whole slide images of breast biopsies, 155 pathology residents from 10 US academic medical centers each reviewed 14 cases across multiple years of residency training. Image-viewing behavior, annotation accuracy, and diagnostic decisions were recorded and analyzed. Generalized estimating equations were used to assess the relationship between residency year and metrics capturing the 4 interpretive phases. Year of residency training was not significantly associated with the detection of critical regions or recognizing their importance. However, each year of residency training was associated with 2% less time spent viewing critical regions (P = 0.025) and 1% more attentional coverage of the image space (P = 0.01), suggesting that residents adopt broader scanning strategies over time. Further, each year of residency training was associated with 19% higher odds of writing an accurate annotation (odds ratio = 1.19, P < 0.001) and 18% higher odds of making an accurate diagnosis (odds ratio = 1.18, P < 0.001). Exploratory analyses indicated that accurate feature annotation was the key predictor of diagnostic accuracy, highlighting its importance as a foundational skill in clinical decision-making. Both the ability to describe histopathological features and diagnostic accuracy increase with years of residency training, and the former appears to be a key component of the latter. Competency-based training in pathology should incorporate targeted interventions to improve histopathological feature recognition and description, which appears most likely to improve overall diagnostic performance.