Identifying error types in visual diagnostic skill assessment
| Autor: | Christian P. Mol, Marieke van der Schaaf, Olle ten Cate, Koen L. Vincken, Jan P. J. van Schaik, Cécile J. Ravesloot, Josephine C.B.M. Huige, Anouk van der Gijp |
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| Rok vydání: | 2017 |
| Předmět: |
Students
Medical Computer science Clinical Biochemistry Medicine (miscellaneous) computer.software_genre Machine learning Humans Radiology test Diagnostic Errors business.industry Health Policy Biochemistry (medical) Public Health Environmental and Occupational Health Reproducibility of Results Radiography Perception Clinical Competence Educational Measurement Artificial intelligence Visual expertise Abnormality Radiology business computer Natural language processing Coding (social sciences) |
| Zdroj: | Diagnosis. 4:93-99 |
| ISSN: | 2194-802X 2194-8011 |
| DOI: | 10.1515/dx-2016-0033 |
| Popis: | Background: Misinterpretation of medical images is an important source of diagnostic error. Errors can occur in different phases of the diagnostic process. Insight in the error types made by learners is crucial for training and giving effective feedback. Most diagnostic skill tests however penalize diagnostic mistakes without an eye for the diagnostic process and the type of error. A radiology test with stepwise reasoning questions was used to distinguish error types in the visual diagnostic process. We evaluated the additional value of a stepwise question-format, in comparison with only diagnostic questions in radiology tests. Methods: Medical students in a radiology elective (n=109) took a radiology test including 11–13 cases in stepwise question-format: marking an abnormality, describing the abnormality and giving a diagnosis. Errors were coded by two independent researchers as perception, analysis, diagnosis, or undefined. Erroneous cases were further evaluated for the presence of latent errors or partial knowledge. Inter-rater reliabilities and percentages of cases with latent errors and partial knowledge were calculated. Results: The stepwise question-format procedure applied to 1351 cases completed by 109 medical students revealed 828 errors. Mean inter-rater reliability of error type coding was Cohen’s κ=0.79. Six hundred and fifty errors (79%) could be coded as perception, analysis or diagnosis errors. The stepwise question-format revealed latent errors in 9% and partial knowledge in 18% of cases. Conclusions: A stepwise question-format can reliably distinguish error types in the visual diagnostic process, and reveals latent errors and partial knowledge. |
| Databáze: | OpenAIRE |
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