From the Department of Radiology/Neuroradiology (A.E.F.), Thomas Jefferson University Hospital, Philadelphia, Pennsylvania
Standards and Guidelines Committee for the American Society of Neuroradiology (J.E.J.), Rancho Palos Verdas, California.
AJNR Am J Neuroradiol. 2019 Jan;40(1):14-18. doi: 10.3174/ajnr.A5780. Epub 2018 Sep 20.
The American Society of Neuroradiology has teamed up with the American College of Radiology and the Radiological Society of North America to create a catalog of neuroradiology common data elements that addresses specific clinical use cases. Fundamentally, a common data element is a question, concept, measurement, or feature with a set of controlled responses. This could be a measurement, subjective assessment, or ordinal value. Common data elements can be both machine- and human-generated. Rather than redesigning neuroradiology reporting, the goal is to establish the minimum number of "essential" concepts that should be in a report to address a clinical question. As medicine shifts toward value-based service compensation methodologies, there will be an even greater need to benchmark quality care and allow peer-to-peer comparisons in all specialties. Many government programs are now focusing on these measures, the most recent being the Merit-Based Incentive Payment System and the Medicare Access Children's Health Insurance Program Reauthorization Act of 2015. Standardized or structured reporting is advocated as one method of assessing radiology report quality, and common data elements are a means for expressing these concepts. Incorporating common data elements into clinical practice fosters a number of very useful downstream processes including establishing benchmarks for quality-assurance programs, ensuring more accurate billing, improving communication to providers and patients, participating in public health initiatives, creating comparative effectiveness research, and providing classifiers for machine learning. Generalized adoption of the recommended common data elements in clinical practice will provide the means to collect and compare imaging report data from multiple institutions locally, regionally, and even nationally, to establish quality benchmarks.
美国神经放射学会与美国放射学会和北美放射学会合作，创建了一个神经放射学通用数据元素目录，该目录针对特定的临床用例。从根本上讲，通用数据元素是一个具有一组受控响应的问题、概念、测量值或特征。这可以是测量值、主观评估或有序值。通用数据元素可以是机器生成的，也可以是人工生成的。其目标不是重新设计神经放射学报告，而是确定报告中应包含的“基本”概念的最小数量，以解决临床问题。随着医学向基于价值的服务补偿方法转变，基准质量护理并允许所有专业领域的同行之间进行比较的需求将更大。许多政府计划现在都在关注这些措施，最近的措施是基于绩效的激励支付系统和 2015 年医疗保险儿童健康保险计划再授权法案。提倡标准化或结构化报告是评估放射学报告质量的一种方法，通用数据元素是表达这些概念的一种手段。将通用数据元素纳入临床实践可以促进许多非常有用的下游流程，包括为质量保证计划建立基准、确保更准确的计费、改善与提供者和患者的沟通、参与公共卫生计划、创建比较效果研究以及为机器学习提供分类器。在临床实践中广泛采用推荐的通用数据元素将为从本地、地区甚至全国范围内的多个机构收集和比较成像报告数据提供手段，以建立质量基准。
