A pathologist[unreadable]s assessment is the gold standard for deciding whether cancer is present or absent. However, community pathologists incorrectly diagnose melanoma in up to 17% of cases, while missing it altogether in 2%-13%. We will evaluate the extent of these diagnostic errors, their sources, and their impact on the U.S. population. Techniques that may reduce errors, such as double reading and continuing education, will also be evaluated. To achieve our goals, an expert panel of pathologists will establish a diagnosis for cases in 5 test sets using a Delphi approach. More than 100 U.S. pathologists will independently review two test sets during 2 phases, separated by 6 months. Our specific aims are: 1. To quantify the extent and evaluate possible sources of pathologists[unreadable] errors in interpreting biopsy specimens of pigmented lesions. 2. To assess whether the addition of independent double reading by two or more pathologists on all or a subset of cases can improve interpretive accuracy. 3. To develop and evaluate an individualized Web-based educational intervention to reduce errors. 4. To quantify the implications of false negative and false positive interpretations and the impact of strategies to reduce errors on short-term patient care and associated resource utilization within the U.S. population. In summary, we will evaluate the accuracy of pathologists[unreadable] interpretation of melanocytic lesions, emphasizing the classification of dysplastic nevi and early stage melanoma, where previous studies reveal a concerning degree of diagnostic error. This large multi-center study of community pathologists addresses a topic of growing clinical importance. Our research team includes international experts in melanoma pathology, statistical measurement of test accuracy, and studies of diagnostic accuracy in clinical medicine. The proposed work is innovative in that we will go beyond simply quantifying the existence of errors. We will study patient and pathologist characteristics associated with inaccurate diagnoses in order to identify possible reasons for diagnostic errors (e.g., inadequate detection vs. inadequate classification). We will then evaluate methods for improving health care delivery by using double reading and also an educational program that will be tested in a randomized intervention study. Finally, we will use a model-based analysis to make projections from our data to the clinical implications of inaccurate diagnoses at the U.S. population level.