The ins and outs of errors in oncology imaging: the DAC framework for radiologists

Abstract

With the increasingly central role of imaging in medical diagnosis, understanding and monitoring radiological errors has become essential. In the field of oncology, the severity of the disease makes radiological error more visible, with both individual consequences and public health issues. The quantitative trend radiology allows to consider the diagnostic task as a problem of classification supported by the latest neurocognitive theories in explaining decision making errors, this purposeful model provides an actionable framework to support root cause analysis of diagnostic errors in radiology and envision corresponding risk-management strategies. The D for Data, A for Analysis and C for Communication are the three drivers of errors and we propose a practical toolbox for our colleagues to prevent individual and systemic sources of error.

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