Journal of the American College of Radiology : JACR
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Affordability of care is a major concern for many in the United States. Part of the affordability of care issue is unanticipated medical bills. A 2018 poll found that unexpected medical costs were the public's greatest affordability concern, ahead of prescription drug costs and even food or rent or mortgage. ⋯ There is risk in trying to "price set" with a benchmark value. Establishing a predetermined value for services to mitigate against unexpected bills could have unintended and significant consequences, including disrupting good-faith negotiations between insurance companies and providers and impacting access to care. The data indicate that an alternative dispute resolution process can protect patients, lower the frequency of unexpected OON bills, and reduce costs.
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This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. ⋯ It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.
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Acute pancreatitis (AP) is divided into two types: interstitial edematous and necrotizing. AP severity is classified clinically into mild, moderately severe, and severe, depending on the presence and persistence of organ failure and local or systemic complications. The revised Atlanta classification divides the clinical course of AP into an early (first week) and late phase (after first week) and the clinical phase determines the role of imaging. ⋯ The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Natural Language Processing for Identification of Incidental Pulmonary Nodules in Radiology Reports.
To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations. ⋯ NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management.
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Recent advances in machine learning and artificial intelligence offer promising applications to radiology quality improvement initiatives as they relate to the radiology value network. Coordination within the interlocking web of systems, events, and stakeholders in the radiology value network may be mitigated though standardization, automation, and a focus on workflow efficiency. In this article the authors present applications of these various strategies via use cases for quality improvement projects at different points in the radiology value network. In addition, the authors discuss opportunities for machine-learning applications in data aggregation as opposed to traditional applications in data extraction.