This session will explore techniques and processes for integrating radiology data into a unified, standardized data model. Participants will gain insights into the current use of a Common Data Element (CDE)-based model for representing radiology results from multiple sources, serving as a common language across systems. The discussion will include a summary of this model and an update on its standardization within the Integrating the Healthcare Enterprise (IHE) framework.

Large language models (LLMs) support the creation of structured data from radiology reports by developing new CDE definitions and extracting meaningful information from unstructured text. A prominent vendor will showcase how structured data enhances radiology workflows, including its use in automatically incorporating findings from prior exams to support comparison and improve report accuracy.

By integrating structured radiology data into the electronic medical record (EMR), new clinical applications emerge that enhance patient care. Technologies such as EMR Hooks enable real-time provider alerts for significant radiology findings or changes relevant to a patient’s clinical scenario. This session will highlight how these advancements ensure that radiologists and referring providers can seamlessly access and act on critical imaging insights, improving diagnostic accuracy and clinical decision-making.

Objectives

• Describe a Common Data Element (CDE)-based data model for representing radiology results.
• Identify opportunities to leverage large language models to create structured data from radiology reports.
• Discuss future applications for a CDE-based data model and structured radiology report data.