The ASNR-ACR-RSNA Common Data Elements Project: What Will It Do for the House of Neuroradiology?

• Published in: American journal of neuroradiology : AJNR Vol. 40; no. 1; pp. 14 - 18
• Main Authors: Flanders, A E, Jordan, J E
• Format: Journal Article
• Language: English
• Published: United States American Society of Neuroradiology 01.01.2019
• Subjects: Common Data Elements - standards; Humans; Neurology - methods; Neurology - standards; North America; Practice s; Radiology - methods; Radiology - standards; United States; United States; North America
• ISSN: 0195-6108; 1936-959X
• DOI: 10.3174/ajnr.A5780

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.