HPC Corner: The ASNR-ACR-RSNA Common Data Elements (CDE) Neuroradiology Workgroup

 

The ASNR-ACR-RSNA Common Data Elements (CDE) Neuroradiology Workgroup

Adam Flanders, MD
Chair, CDE Workgroup

The ASNR has teamed up with the American College of Radiology (ACR) and the Radiological Society of North America (RSNA) to create neuroradiology specific common data elements (CDEs) for specific clinical use cases.  CDEs are not reporting templates. Fundamentally a CDE is a question, concept, measurement, or feature with a set of controlled responses.  This could take the form of a measurement (e.g. diameter of a pituitary adenoma), a subjective assessment of severity (e.g. mild, moderate, severe foraminal stenosis) or an ordinal value (e.g. ASPECTS score in acute stroke).  CDEs can be both machine and human-generated. Rather than redesigning neuroradiology reporting, the goal is to establish what the minimum number of “essential” concepts that should be represented in a report to address a clinical question such as expecting a NASCET score for any carotid stenosis measurement, use of an ASPECTS score with an acute stroke CT, or incorporation of the Pfirrmann grading schema for root compression in a degenerative lumbar spine MRI.

The ASNR workgroup has been drafting about a dozen pilot CDE sets for public comment at this year’s annual meeting in Vancouver. The objective is to make these CDEs publicly available and usable in our current speech systems with the longer term goal of ensuring their compatibility with the next generation of reporting systems that are currently in development. These newer reporting systems have the inherent ability to validate report content as it is being created, suggest inclusion of missing elements based upon predecessor concepts, and provide standardized recommendations based upon earlier report content. For example, this new technology could automatically insert a TI-RADS evaluation module into a draft report upon mention of an incidental thyroid nodule on a neck CTA or provide the latest consensus imaging criteria for multiple sclerosis upon describing demyelinating lesions on an initial brain MRI. The end result is to augment the inherent value of the report without diminishing efficiency at the point-of-care.

As medicine shifts towards value-based service compensation methodologies, there will be an even greater need to benchmark quality care and allow for peer-to-peer comparisons in all specialties.  Many government programs are now focusing on these measures, the most recent being MIPS/MACRA.  Standardized or structured reporting is advocated as one method to assess radiology report quality and CDEs are a means for expressing these concepts.  With the generalized adoption of best practice neuroradiology reporting concepts a number of very useful downstream processes can be spawned in the modern IT ecosystem. CDEs by definition can be coded as discrete data elements.  Therefore, they can be utilized to trigger other events automatically based upon their value. This includes quality assurance, communication to providers and patients, billing augmentation, participation in image based data registries and public health initiatives, comparative effectiveness research and to provide classifiers for machine learning.  Generalized adoption of recommended CDEs 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.

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