Visualization of disease distribution with SNOMED CT and ICD-10

Area of Science:

• Health Informatics
• Medical Coding Systems
• Public Health Data Analysis

Background:

• Disease and health problem distribution analysis relies on controlled terminology.
• Current coding systems like ICD-10 have inherent structural limitations.
• SNOMED CT offers a richer, more detailed structure for health data.

Purpose of the Study:

• To explore the impact of controlled terminology structure on disease distribution presentation.
• To compare disease distribution analysis using ICD-10 versus SNOMED CT.
• To present a method for leveraging SNOMED CT's structure to enhance traditional classifications.

Main Methods:

• Analysis of 2.5 million diagnostic codes from Stockholm primary care.
• Mapping of patient encounters coded in ICD-10 to SNOMED CT concepts.
• Utilizing a mapping table to translate between coding systems.
• Developing and demonstrating a method for disease distribution visualization.

Main Results:

• Disease distributions were analyzed through the distinct structures of ICD-10 and SNOMED CT.
• A method was successfully applied to map ICD-10 codes to the more granular SNOMED CT.
• Examples of enhanced disease distribution visualizations were produced.

Conclusions:

• SNOMED CT provides a more detailed structure for presenting disease and health problem distributions.
• Mapping traditional classifications like ICD-10 to SNOMED CT can enrich data analysis.
• This approach offers implications for improved health care data interpretation and utilization.