ACR TI-RADS: Pitfalls, Solutions, and Future Directions.

The high prevalence of thyroid nodules combined with the generally indolent growth of thyroid cancer present a challenge for optimal patient care. Risk classification models based on US features have been created by multiple professional societies, including the American College of Radiology (ACR), which published the Thyroid Imaging Reporting and Data System (TI-RADS) in 2017. ACR TI-RADS uses a standardized lexicon for assessment of thyroid nodules to generate a numeric scoring of features, designate categories of relative probability of benignity or malignancy, and provide management recommendations, with the aim of reducing unnecessary biopsies and excessive surveillance. Adopting ACR TI-RADS may require practice-level changes involving image acquisition and workflow, interpretation, and reporting. Significant resources should be devoted to educating sonographers and radiologists to accurately recognize features that contribute to the scoring of a nodule. Following a system that uses approved terminology generates reproducible and relevant reports while providing clarity of language and preventing misinterpretation. Comprehensive documentation facilitates quality improvement efforts. It also creates opportunities for outcome data and other performance metrics to be integrated with research. The authors review ACR TI-RADS, describe challenges and potential solutions related to its implementation based on their experiences, and highlight possible future directions in its evolution. ©RSNA, 2019 See discussion on this article by Hoang.

Fundamentals of Diagnostic Error in Imaging.

Imaging plays a pivotal role in the diagnostic process for many patients. With estimates of average diagnostic error rates ranging from 3% to 5%, there are approximately 40 million diagnostic errors involving imaging annually worldwide. The potential to improve diagnostic performance and reduce patient harm by identifying and learning from these errors is substantial. Yet these relatively high diagnostic error rates have persisted in our field despite decades of research and interventions. It may often seem as if diagnostic errors in radiology occur in a haphazard fashion. However, diagnostic problem solving in radiology is not a mysterious black box, and diagnostic errors are not random occurrences. Rather, diagnostic errors are predictable events with readily identifiable contributing factors, many of which are driven by how we think or related to the external environment. These contributing factors lead to both perceptual and interpretive errors. Identifying contributing factors is one of the keys to developing interventions that reduce or mitigate diagnostic errors. Developing a comprehensive process to identify diagnostic errors, analyze them to discover contributing factors and biases, and develop interventions based on the contributing factors is fundamental to learning from diagnostic error. Coupled with effective peer learning practices, supportive leadership, and a culture of quality, this process can unquestionably result in fewer diagnostic errors, improved patient outcomes, and increased satisfaction for all stakeholders. This article provides the foundational elements for implementing this type of process at a radiology practice, with examples to help radiologists and practice leaders achieve meaningful practice improvement. ©RSNA, 2018.