CHEST Guidelines-Education-for-Chest-Radiograph-Interpretation-Perf

Education-for-Chest-Radiograph-Interpretation-Perf

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The article discusses issues related to chest radiograph (CXR) interpretation and the potential for error in medical radiology, with reference to a recent publication by Gefter and Hatabu. These errors, ranging from 4% to 30%, are attributable to factors like cognitive biases, image quality, clinical volume, and hospital customs. To mitigate these errors, the authors suggest improving image quality and viewing conditions, reviewing clinical history, using systematic search strategies, employing checklists, and recognizing the role of artificial intelligence in aiding medical decision-making.<br /><br />Gefter and Hatabu emphasize the importance of education and quality improvement strategies in enhancing CXR interpretation within a supportive clinical culture. They propose educational interventions that use errors as learning opportunities, such as missed case reviews, peer-review processes, and error mitigation conferences.<br /><br />The letter by William C. McGaghie highlights the importance of deliberate practice, as emphasized by Ericsson and Pool, in professional expertise development. He stresses that educational methods should be evidence-based and rigorous, incorporating mastery learning expectations to enhance medical education and boost CXR interpretive skills.<br /><br />McGaghie argues that current medical education, which largely depends on case conferences and passive peer review, requires significant improvements. He advocates for a curriculum grounded in the science of expertise, which includes specific learning goals, reliable feedback, and skillful teaching in a controlled educational setting. The goal is to develop "excellence for all" in medical education in the 21st century, including improved CXR interpretation skills. McGaghie supports these educational reforms and improvements to provide better training for physicians and to reduce interpretation errors in medical radiology.

Keywords

chest radiograph interpretation

medical radiology errors

cognitive biases

artificial intelligence

educational interventions

quality improvement

deliberate practice

mastery learning

medical education reform

expertise development