78843 Department of Dermatology, Oregon Health and Sciences University, Portland, OR, USA.
12223 Department of Dermatology, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA.
J Cutan Med Surg. 2022 Jan-Feb;26(1):17-24. doi: 10.1177/12034754211035093. Epub 2021 Aug 2.
Representative images of pathology in patients with skin of color are lacking in most medical education resources. This particularly affects training in dermatology, which relies heavily on the use of images to teach pattern recognition. The presentation of skin pathology can vary greatly among different skin tones, and this lack of representation of dark skin phototypes challenges providers' abilities to provide quality care to patients of color.In Botswana and other countries in sub-Saharan Africa, this challenge is further compounded by limited resources and access to dermatologists. There is a need for improved and accessible educational resources to train medical students and local medical providers in basic skin lesion description and diagnosis.
We examined whether online Perceptual and Adaptive Learning Modules (PALMs) composed of representative dark skin images could efficiently train University of Botswana medical students to more accurately describe and diagnose common skin conditions in their community.
Year 4 and 5 medical students voluntarily completed PALMs that teach skin morphology, configuration, and distribution terminology and diagnosis of the most common dermatologic conditions in their community. Pre-tests, post-tests and delayed-tests assessed knowledge acquisition and retention.
PALMs training produced statistically significant ( < .0001) improvements in accuracy and fluency with large effect sizes (1.5, 3.7) and good retention after a 12.5-21-week median delay. Limitations were a self-selected group of students, a single institution, slow internet connections, and high drop-out rates.
Overall, population-specific PALMs are a useful tool for efficient development of pattern recognition in skin disease description and diagnosis.
在大多数医学教育资源中，缺乏有色人种皮肤病理学的代表性图像。这尤其影响皮肤科的培训，皮肤科严重依赖图像来教授模式识别。不同肤色的皮肤病理学表现差异很大，而这种深色皮肤类型的代表性不足，挑战了提供者为有色人种患者提供优质护理的能力。在博茨瓦纳和撒哈拉以南非洲的其他国家，由于资源有限和皮肤科医生的缺乏，这一挑战更加复杂。需要改进和普及教育资源，以培训医学生和当地医疗服务提供者，使其能够基本描述和诊断皮肤病变。
我们研究了由代表性深色皮肤图像组成的在线感知和适应学习模块（PALM）是否可以有效地培训博茨瓦纳大学医学生，使其更准确地描述和诊断其社区中常见的皮肤状况。
四年级和五年级医学生自愿完成 PALM，学习皮肤形态、形态和分布术语以及其社区中最常见皮肤病的诊断。通过前测、后测和延迟测试评估知识获取和保留情况。
PALM 培训在准确性和流畅性方面产生了统计学上显著的（<0.0001）提高，具有较大的效应量（1.5、3.7），并且在 12.5-21 周的中位数延迟后保留良好。限制因素包括学生的自我选择、单一机构、互联网连接缓慢和高辍学率。
总体而言，特定人群的 PALM 是一种高效开发皮肤疾病描述和诊断中模式识别的有用工具。
