INTRODUCTION
Traditional methods for instructing dermatopathology rely heavily on approaches such as didactics and standard microscopy. In response to shortcomings of con- ventional medical education techniques, advances in learning sciences offer promising new tools for learners and instructors with a goal of more efficiently acquiring knowledge.1 Prior studies have utilized digital test-enhanced learning to evaluate proficiency in clinical dermatology,2 histology,3 and radiography.4 The use of learning analytics in dermatopathology has not been well studied.
We present a web-based dermatopathology learning application to demonstrate that resident education can be enhanced using data-driven approaches. We developed an electronic platform to assess the acquisition of dermatopathology skills by dermatology residents over time with the goal of optimizing their dermatopathologic educational curriculum.
We present a web-based dermatopathology learning application to demonstrate that resident education can be enhanced using data-driven approaches. We developed an electronic platform to assess the acquisition of dermatopathology skills by dermatology residents over time with the goal of optimizing their dermatopathologic educational curriculum.
METHODS
Participant Recruitment and Setting
For this study, we recruited 24 dermatology residents in the Ronald O. Perelman Department of Dermatology at the New York University School of Medicine.To compare with a reference standard, we also invited three board-certified dermatopathologists from three separate institutions to take the survey under study conditions. All participants provided informed consent, and the study was approved by the NYU Institutional Review Board.
Histologic Image Selection and Diagnostic Classification
Selected histologic images of 90 different cases comprised 18 diagnostic entities in the spindle cell neoplasm differential. Each case was chosen from a slide teaching collection created by the Ronald O. Perelman Department of Dermatology, and three magnifications were captured using the Olympus DP70 camera and Aperio slide scanner. The cases included had each been previously diagnosed by an experienced board-certified dermatopathologist and included: atypical fibroxanthoma, an- giofibroma, angioleiomyoma, blue nevus, dermatofibroma, dermatofibrosarcoma protuberans, dermatomyofibroma, desmoplastic melanoma, hypertrophic scar, Kaposi sarcoma, leiomyoma, neurofibroma, neurothekeoma, nodular fasciitis, normal skin, scar, schwannoma, and traumatic neuroma.
For this study, we recruited 24 dermatology residents in the Ronald O. Perelman Department of Dermatology at the New York University School of Medicine.To compare with a reference standard, we also invited three board-certified dermatopathologists from three separate institutions to take the survey under study conditions. All participants provided informed consent, and the study was approved by the NYU Institutional Review Board.
Histologic Image Selection and Diagnostic Classification
Selected histologic images of 90 different cases comprised 18 diagnostic entities in the spindle cell neoplasm differential. Each case was chosen from a slide teaching collection created by the Ronald O. Perelman Department of Dermatology, and three magnifications were captured using the Olympus DP70 camera and Aperio slide scanner. The cases included had each been previously diagnosed by an experienced board-certified dermatopathologist and included: atypical fibroxanthoma, an- giofibroma, angioleiomyoma, blue nevus, dermatofibroma, dermatofibrosarcoma protuberans, dermatomyofibroma, desmoplastic melanoma, hypertrophic scar, Kaposi sarcoma, leiomyoma, neurofibroma, neurothekeoma, nodular fasciitis, normal skin, scar, schwannoma, and traumatic neuroma.
