Salava A, Salmela V. Diagnostic errors during perceptual learning in dermatology: a prospective cohort study of Finnish undergraduate students.
Clin Exp Dermatol 2024;
49:866-874. [PMID:
38391032 DOI:
10.1093/ced/llae063]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/14/2024] [Accepted: 02/19/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND
Perceptual learning modules (PLMs) have been shown to significantly improve learning outcomes in teaching dermatology.
OBJECTIVES
To investigate the quantity and quality of diagnostic errors made during undergraduate PLMs and their potential implications.
METHODS
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.
RESULTS
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.
CONCLUSIONS
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.
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