Semi-Automated Approach to Retrieve SNOMED CT Hierarchy of Clinical Terms by Using Terminology Server

Area of Science:

• Health informatics
• Clinical terminology management
• Semantic interoperability

Background:

• SNOMED CT contains a vast number of clinical concepts, essential for semantic interoperability in healthcare.
• Manual mapping to SNOMED CT is labor-intensive, prone to errors, and presents significant challenges.
• Retrieving concepts within the SNOMED CT hierarchy further complicates user interaction.

Purpose of the Study:

• To investigate the efficiency of using Snowstorm, a Terminology Server, for automating clinical concept retrieval.
• To demonstrate how semi-automated workflows can enhance the process of mapping and retrieving clinical concepts.

Main Methods:

• Utilized Snowstorm, a Terminology Server, to facilitate the retrieval of clinical concepts.
• Implemented semi-automated workflows to streamline the mapping and retrieval processes.
• Evaluated the efficiency gains compared to traditional manual methods.

Main Results:

• Snowstorm demonstrated a significant improvement in the efficiency of the clinical concept retrieval process.
• Semi-automated workflows using Snowstorm reduced the time and potential for errors associated with manual mapping.
• The study confirmed the utility of Terminology Servers in automating data retrieval.

Conclusions:

• Snowstorm, when integrated with semi-automated workflows, substantially enhances the efficiency of mapping clinical concepts to SNOMED CT.
• This approach addresses key challenges in semantic interoperability, particularly in managing large clinical terminologies.
• The findings support the adoption of Terminology Servers for more effective healthcare data management.