Standardizing imaging findings representation: harnessing Common Data Elements semantics and Fast Healthcare Interoperability Resources structures.

OBJECTIVES: Designing a framework representing radiology results in a standards-based data structure using joint Radiological Society of North America/American College of Radiology Common Data Elements (CDEs) as the semantic labels on standard structures. This allows radiologist-created report data to integrate with artificial intelligence-generated results for use throughout downstream systems. MATERIALS AND METHODS: We developed a framework modeling radiology findings as Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) observations using CDE set/element identifiers as standardized semantic labels. This framework deploys CDE identifiers to specify radiology findings and attributes, providing consistent labels for radiology report concepts-diagnoses, recommendations, tabular/quantitative data-with built-in integration with RadLex, SNOMED CT, LOINC, and other ontologies. Observation structures fit within larger HL7 FHIR DiagnosticReport resources, providing output including both nuanced text and structured data. RESULTS: Labeling radiology findings as discrete data for interchange between systems requires two components: structure and semantics. CDE definitions provide semantic identifiers for findings and their component values. The FHIR observation resource specifies a structure for associating identifiers with radiology findings in the context of reports, with CDE-encoded observations referring to definitions for CDE identifiers in a central repository. The discussion includes an example of encoding pulmonary nodules on a chest CT as CDE-labeled observations, demonstrating the application of this framework to exchange findings throughout the imaging workflow, making imaging data available to downstream clinical systems. DISCUSSION: CDE-labeled observations establish a lingua franca for encoding, exchanging, and consuming radiology data at the level of individual findings, facilitating use throughout healthcare systems. IMPORTANCE: CDE-labeled FHIR observation objects can increase the value of radiology results by facilitating their use throughout patient care.

Structures' validation profiles in Transmission of Imaging and Data (TRIAD) for automated National Clinical Trials Network (NCTN) clinical trial digital data quality assurance.

Transmission of Imaging and Data (TRIAD) is a standard-based system built by the American College of Radiology to provide the seamless exchange of images and data for accreditation of clinical trials and registries. Scripts of structures' names validation profiles created in TRIAD are used in the automated submission process. It is essential for users to understand the logistics of these scripts for successful submission of radiation therapy cases with less iteration.

Using Object Storage Technology vs Vendor Neutral Archives for an Image Data Repository Infrastructure.

The intent of this project was to use object storage and its database, which has the ability to add custom extensible metadata to an imaging object being stored within the system, to harness the power of its search capabilities, and to close the technology gap that healthcare faces. This creates a non-disruptive tool that can be used natively by both legacy systems and the healthcare systems of today which leverage more advanced storage technologies. The base infrastructure can be populated alongside current workflows without any interruption to the delivery of services. In certain use cases, this technology can be seen as a true alternative to the VNA (Vendor Neutral Archive) systems implemented by healthcare today. The scalability, security, and ability to process complex objects makes this more than just storage for image data and a commodity to be consumed by PACS (Picture Archiving and Communication System) and workstations. Object storage is a smart technology that can be leveraged to create vendor independence, standards compliance, and a data repository that can be mined for truly relevant content by adding additional context to search capabilities. This functionality can lead to efficiencies in workflow and a wealth of minable data to improve outcomes into the future.