Department of Dermatology, Venereology and Allergology, University Hospital Helsinki and University of Helsinki, Helsinki, Finland.
Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
Clin Exp Dermatol. 2024 Jul 19;49(8):866-874. doi: 10.1093/ced/llae063.
Perceptual learning modules (PLMs) have been shown to significantly improve learning outcomes in teaching dermatology.
To investigate the quantity and quality of diagnostic errors made during undergraduate PLMs and their potential implications.
The study data were acquired from 8 successive dermatology courses (2021-23) from 142 undergraduate medical students. Digital PLMs were held before, during and after the courses. We investigated the number and distribution of diagnostic errors, differences between specific skin conditions and classified the errors based on type.
Diagnostic errors were not randomly distributed. Some skin conditions were almost always correctly identified, whereas a significant number of errors were made for other diagnoses. Errors were classified into one of three groups: mostly systematic errors of relevant differential diagnoses ('similarity' errors); partly systematic errors ('mixed' errors); and 'random' errors. While a significant learning effect during the repeated measures was found in accuracy (P < 0.001, η²P = 0.64), confidence (P < 0.001, η²P = 0.60) and fluency (P < 0.001, η²P = 0.16), the three categories differed in all outcome measures (all P < 0.001, all η²P > 0.47). Visual learning was more difficult for diagnoses in the similarity category (all P < 0.001, all η²P > 0.12) than for those in the mixed and random categories.
Error analysis of PLMs provided relevant information about learning efficacy and progression, and systematic errors in tasks and more difficult-to-learn conditions. This information could be used in the development of adaptive, individual error-based PLMs to improve learning outcomes, both in dermatology and medical education in general.
感知学习模块(PLMs)已被证明可显著提高皮肤科教学的学习效果。
调查本科 PLM 中诊断错误的数量和质量及其潜在影响。
本研究数据来自于 142 名本科医学生连续 8 次皮肤科课程(2021-2023 年)。在课程之前、期间和之后进行数字 PLM。我们调查了诊断错误的数量和分布,以及特定皮肤状况之间的差异,并根据类型对错误进行了分类。
诊断错误并非随机分布。某些皮肤状况几乎总是能正确识别,而其他一些诊断则会出现大量错误。错误可分为以下三组:相关鉴别诊断的主要系统性错误(“相似性”错误);部分系统性错误(“混合”错误);以及“随机”错误。虽然在准确性(P < 0.001,η²P = 0.64)、信心(P < 0.001,η²P = 0.60)和流畅度(P < 0.001,η²P = 0.16)方面,重复测量的学习效果有显著提高,但在所有结果测量中,三个类别均有差异(均 P < 0.001,均 η²P > 0.47)。与混合和随机类别相比,相似类别中的诊断对视觉学习更具挑战性(均 P < 0.001,均 η²P > 0.12)。
PLM 的错误分析提供了有关学习效果和进展的相关信息,以及任务中的系统性错误和更难学习的条件。这些信息可用于开发自适应、基于个体错误的 PLM,以提高皮肤科乃至整个医学教育的学习效果。