RadLex and SNOMED CT integration: a pilot study for standardising radiology classification
Related Experiment Video
Updated: May 22, 2025
Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
Published on: October 27, 2023
Merit Marquis1, Igor Bossenko2, Peeter Ross2
1Tallinn University of Technology, Tallinn, Estonia. merit.marquis@gmail.com.
Background:
Effective communication and information exchange across diverse platforms are critical in healthcare data systems. However, the presence of multiple coding systems and varying standards creates discrepancies and misalignments, highlighting the need for innovative solutions to address these challenges.
Objective:
The study aimed to develop a technical and semantic interoperability method specifically for radiology procedures, utilising the terminology management tool TermX to facilitate efficient data exchange and utilisation in healthcare.
Results:
The study resulted in a revised RadLex data model using SNOMED CT, accompanied by a mapping guide and a classification system for X-ray and angiography procedures. This classification system consists of nineteen distinct properties, each defined by specific value sets derived from SNOMED CT terminology. A total of 380 concepts were utilised to describe the 622 procedures examined comprehensively.
Conclusion:
Through twelve design cycles involving in-depth analysis and iterative refinement, the mapping of angiography and X-ray procedures was successfully achieved, culminating in the creation and validation of a universal model that enhances both primary and secondary data collection. The efficacy and innovation of this system pave the way for further advancements in healthcare interoperability.
Critical Relevance Statement:
The innovative integration achieved in this study for standardising radiology classification promises to improve data management practices and enhance patient care outcomes through increased interoperability within the healthcare sector.
Key Points:
A universal radiology procedure model to enhance capture would be valuable. A tool to facilitate technical and semantic interoperability for efficient data exchange in healthcare was created. This system could pave the way for futher advancements in healthcare interoperability.
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