The Role of Intuitive Cognition in Radiologic Decision Making.

The decision making involved in radiologic interpretation entails distinct cognitive pathways. On one side is analytic reasoning, which represents a deliberate, stepwise process integrating discrete data to formulate an interpretation, in which a range of diagnostic possibilities are directly compared. On the other side is intuition, which represents an automatic, rapid, and holistic form of decision making that generates an interpretation absent the sequential processing of data and direct comparison of possibilities. Nonexpert intuitive cognition often reflects domain-independent heuristics (ie, mental rules of thumb) that are often effective but prone to bias and systematic error. In contrast, expert intuition reflects the domain-specific skills developed among highly experienced practitioners who have gained deep knowledge in a given task domain from extensive practice and feedback. In this article, the authors define intuitive cognition, show evidence for its pervasive use among experts in a variety of fields, and explain its strengths and weaknesses relative to deliberate reasoning. Developing expert intuition requires the opportunity to learn from reliable feedback, and the authors describe various measures that can be used by radiology departments to foster such opportunities. Finally, the authors discuss implications for diagnostic performance and error reduction in clinical radiology.

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.

Diagnostic Error Categorization at Abdominal Imaging Peer Learning Conference.

Recognizing and preventing diagnostic errors is an increasingly emphasized topic across medicine, and abdominal imaging is no exception. Peer-learning strives for quality improvement through understanding why errors occur and identifying opportunities to prevent errors from recurring. In an effort to learn from mistakes, our abdominal imaging section initiated a Peer Learning Conference, where errors are discussed and compartmentalized into one or more of the following categories: Observation, Interpretation, Communication, and Inadequate Data Gathering. In this manuscript, the structure of our Peer Learning Conference is introduced and the components of each discrepancy category are described in detail. Images are included to highlight learning points through exemplary cases from the conference.

The Incentive Dilemma: Intrinsic Motivation and Workplace Performance.

Incentive plans are a core component of many radiology positions and are often considered a major factor in the ability to recruit and retain high-performing radiologists. Financial incentives are widely thought to be effective at motivating individuals, but there is considerable evidence to the contrary. In this report, the authors examine basic assumptions about financial incentives and debate the potential negative impact of financial incentive systems on performance at radiology practices.

Cost-Effectiveness Analysis: An Overview of Key Concepts, Recommendations, Controversies, and Pitfalls.

The field of radiology has witnessed a burst of technological advances that improve diagnostic quality, reduce harm to patients, support clinical needs, and better serve larger more diverse patient populations. One of the critical challenges with these advances is proving that value outweighs the cost. The use of cutting-edge technology is often expensive, and the reality is that our society cannot afford all the screening and diagnostic tests that are being developed. At the societal level, we need tools to help us decide which health programs should be funded. Therefore, decision makers are increasingly looking toward scientific methods to compare health technologies in order to improve allocation of resources. One of such methods is cost-effectiveness analysis. In this article, we review key features of cost-effectiveness analysis and its specific issues as they relate to radiology.

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.

Developing Quality Measures for Diagnostic Radiologists: Part 2.

The ACR convened a cross-specialty, multidisciplinary technical expert panel to identify and define new measures for quality improvement. These measures can be included in the ACR's National Radiology Data Registry and potentially used in the CMS quality reporting programs. The technical expert panel was tasked with developing measures that reflect the most rigorous clinical evidence and address areas most in need of performance improvement. The measures described in these articles represent a new phase in the ACR's efforts to develop meaningful measures for radiologists that promote population health through diagnostic accuracy, clinical effectiveness, and care coordination.

Developing Quality Measures for Diagnostic Radiologists: Part 1.

The ACR convened a cross-specialty, multidisciplinary technical expert panel to identify and define new measures for quality improvement. These measures can be included in the ACR's National Radiology Data Registry and potentially used in the CMS quality reporting programs. The technical expert panel was tasked with developing measures that reflect the most rigorous clinical evidence and address areas most in need of performance improvement. The measures described in these articles represent a new phase in the ACR's efforts to develop meaningful measures for radiologists that promote population health through diagnostic accuracy, clinical effectiveness, and care coordination.

Random Versus Nonrandom Peer Review: A Case for More Meaningful Peer Review.

OBJECTIVE: Random peer review programs are not optimized to discover cases with diagnostic error and thus have inherent limitations with respect to educational and quality improvement value. Nonrandom peer review offers an alternative approach in which diagnostic error cases are targeted for collection during routine clinical practice. The objective of this study was to compare error cases identified through random and nonrandom peer review approaches at an academic center. METHODS: During the 1-year study period, the number of discrepancy cases and score of discrepancy were determined from each approach. RESULTS: The nonrandom peer review process collected 190 cases, of which 60 were scored as 2 (minor discrepancy), 94 as 3 (significant discrepancy), and 36 as 4 (major discrepancy). In the random peer review process, 1,690 cases were reviewed, of which 1,646 were scored as 1 (no discrepancy), 44 were scored as 2 (minor discrepancy), and none were scored as 3 or 4. Several teaching lessons and quality improvement measures were developed as a result of analysis of error cases collected through the nonrandom peer review process. CONCLUSIONS: Our experience supports the implementation of nonrandom peer review as a replacement to random peer review, with nonrandom peer review serving as a more effective method for collecting diagnostic error cases with educational and quality improvement value.

Papillary Thyroid Carcinoma Recurrence: Low Yield of Neck Ultrasound With an Undetectable Serum Thyroglobulin Level.

OBJECTIVES: To assess the yield of neck ultrasound (US) when serum thyroglobulin (Tg) is undetectable (<0.1 ng/mL) compared to elevated serum Tg in patients with differentiated papillary thyroid carcinoma (PTC) treated with thyroidectomy and radioactive iodine 131 (RAI) ablation. METHODS: A retrospective chart review was conducted from 2010 through 2015 at an academic institution evaluating US results in patients with serum Tg levels obtained within 6 months of a neck US examination after thyroidectomy and RAI. The reference standard for recurrence was pathologic results from US-guided fine-needle aspiration (FNA) or follow-up for at least 1 year. RESULTS: Among 76 patients with undetectable serum Tg levels, there were 19 examinations in 18 patients in which US raised the possibility of recurrence. None of these 18 patients had recurrence by FNA (n = 8) or clinical follow-up of at least 1 year (n = 10). Among 65 patients with elevated serum Tg levels, there were 24 examinations in 22 patients in which US raised the possibility of recurrence. Twelve patients underwent FNA, with 9 patients (34.6%) showing PTC; 7 patients had follow-up neck US examinations showing stability of findings; and 3 patients were lost to follow up. The yield of neck US was significantly lower when serum Tg was undetectable compared to when levels were elevated (P = .001). CONCLUSIONS: Neck US did not identify recurrent PTC when the serum Tg level was undetectable in patients who underwent total thyroidectomy and RAI therapy. Eliminating neck US when serum TG levels are undetectable could decrease unnecessary imaging examinations without negatively affecting the ability to detect recurrent disease.

Heuristics and Cognitive Error in Medical Imaging.

OBJECTIVE: The field of cognitive science has provided important insights into mental processes underlying the interpretation of imaging examinations. Despite these insights, diagnostic error remains a major obstacle in the goal to improve quality in radiology. In this article, we describe several types of cognitive bias that lead to diagnostic errors in imaging and discuss approaches to mitigate cognitive biases and diagnostic error. CONCLUSION: Radiologists rely on heuristic principles to reduce complex tasks of assessing probabilities and predicting values into simpler judgmental operations. These mental shortcuts allow rapid problem solving based on assumptions and past experiences. Heuristics used in the interpretation of imaging studies are generally helpful but can sometimes result in cognitive biases that lead to significant errors. An understanding of the causes of cognitive biases can lead to the development of educational content and systematic improvements that mitigate errors and improve the quality of care provided by radiologists.

Machine Learning in Radiology: Applications Beyond Image Interpretation.

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.

Funds Flow in the Era of Value-Based Health Care.

Health care reform is creating significant challenges for hospital systems and academic medical centers (AMCs), requiring a new operating model to adapt to declining reimbursement, diminishing research funding, market consolidation, payers' focus on higher quality and lower cost, and greater cost sharing by patients. Maintaining and promoting the triple mission of clinical care, research, and education will require AMCs to be system-based with strong alignment around governance, operations, clinical care, and finances. Funds flow is the primary mechanism whereby an AMC maintains the triple mission through alignment of the hospital, physician practices, school of medicine, undergraduate university, and other professional schools. The purpose of this article is to discuss challenges with current funds flow models, impact of funds flow on academic and private practice radiology groups, and strategies that can increase funds flow to support radiology practices achieving clinical, research, and teaching missions in the era of value-based health care.

Teaching Communication Skills to Radiology Residents.

The transition of health care in the United States from volume to value requires a systems-based approach aligning clinical services across the continuum of care. The ability to communicate effectively and resolve conflict is a critical skill within the systems-based model. Recognizing the essential role of communication in medicine, the Accreditation Council of Graduate Medical Education has designated interpersonal and communication skills a core competency for all residents regardless of specialty. Yet, communication skills are often developed through on-the-job training or not at all. Traditional educational curricula use a predominantly didactic approach without opportunities for trainees to observe, actively experiment, or reflect on what is learned as a part of the learning process. In this article, we describe a 1-day experiential communication skills workshop customized for radiology residents that consists of Myers-Briggs Type Indicator and conflict management sessions designed to develop interpersonal, communication, and conflict management skills through group discussion, role-play, and simulation. The purpose of this educational initiative was to determine the perceived value of an experiential communication skills workshop designed for radiology trainees.

The Science of Quality Improvement.

Scientific rigor should be consistently applied to quality improvement (QI) research to ensure that healthcare interventions improve quality and patient safety before widespread implementation. This article provides an overview of the various study designs that can be used for QI research depending on the stage of investigation, scope of the QI intervention, constraints on the researchers and intervention being studied, and evidence needed to support widespread implementation. The most commonly used designs in QI studies are quasi-experimental designs. Randomized controlled trials and cluster randomized trials are typically reserved for large-scale research projects evaluating the effectiveness of QI interventions that may be implemented broadly, have more than a minimal impact on patients, or are costly. Systematic reviews of QI studies will play an important role in providing overviews of evidence supporting particular QI interventions or methods of achieving change. We also review the general requirements for developing quality measures for reimbursement, public reporting, and pay-for-performance initiatives. A critical part of the testing process for quality measures includes assessment of feasibility, reliability, validity, and unintended consequences. Finally, publication and critical appraisal of QI work is discussed as an essential component to generating evidence supporting QI initiatives in radiology.

Radiology Research in Quality and Safety: Current Trends and Future Needs.

Promoting quality and safety research is now essential for radiology as reimbursement is increasingly tied to measures of quality, patient safety, efficiency, and appropriateness of imaging. This article provides an overview of key features necessary to promote successful quality improvement efforts in radiology. Emphasis is given to current trends and future opportunities for directing research. Establishing and maintaining a culture of safety is paramount to organizations wishing to improve patient care. The correct culture must be in place to support quality initiatives and create accountability for patient care. Focused educational curricula are necessary to teach quality and safety-related skills and behaviors to trainees, staff members, and physicians. The increasingly complex healthcare landscape requires that organizations build effective data infrastructures to support quality and safety research. Incident reporting systems designed specifically for medical imaging will benefit quality improvement initiatives by identifying and learning from system errors, enhancing knowledge about safety, and creating safer systems through the implementation of standardized practices and standards. Finally, validated performance measures must be developed to accurately reflect the value of the care we provide for our patients and referring providers. Common metrics used in radiology are reviewed with focus on current and future opportunities for investigation.