Odev.tech has participated in various projects involving SNOMED CT, many of which are related to mapping existing databases in hospitals, generating unique expertise on how to address these issues. In this article, we will provide a brief introduction to what SNOMED CT is and the challenges that healthcare providers may face in its implementation.

What is SNOMED CT?

SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) has emerged as a cornerstone in the digital transformation of the healthcare sector. This clinical terminology standard provides a common framework for encoding and exchanging medical information, promoting interoperability, and enhancing the quality of care.

The applications of SNOMED CT in digital health are diverse and extensive, spanning a wide range of areas within healthcare, such as:

Electronic Health Records (EHR):

SNOMED CT is extensively used in electronic health record systems to capture and encode clinical information about patients, including diagnoses, procedures, laboratory results, and prescribed medications. This facilitates standardized storage and retrieval of clinical data, enabling more efficient patient information management.

Interoperability between Healthcare Systems:

SNOMED CT plays a crucial role in interoperability between different healthcare systems and clinical applications. By standardizing clinical terminology, SNOMED CT facilitates information exchange between healthcare systems, enabling more integrated and coordinated healthcare.

Clinical Decision Support Systems:

SNOMED CT is used in the development of clinical decision support systems, providing recommendations and guidance to healthcare professionals in the assessment and treatment of patients. Standardizing clinical terminology ensures the accuracy and relevance of clinical recommendations provided by these systems.

Clinical and Epidemiological Data Analysis:

SNOMED CT is used in the analysis of clinical and epidemiological data to identify patterns, trends, and relationships between different medical conditions, procedures, and health outcomes. This allows researchers and epidemiologists to gain valuable insights into population health and the effectiveness of medical interventions.

Integration with Emerging Technologies:

SNOMED CT integrates with emerging technologies such as artificial intelligence and machine learning to develop advanced applications for clinical data analysis, computer-aided diagnosis, and personalized medicine. These technologies leverage the standardized clinical terminology provided by SNOMED CT to improve the accuracy and efficiency of their applications.

Many health-related companies and institutions, such as pharmacies, hospitals, and mutualistic organizations with their internal databases of medications and commercial products, are interested in adopting SNOMED CT terminology. This adoption offers significant advantages without negatively impacting their management, maintenance, and distribution systems.

Advantages of Linking SNOMED CT with an External Database:

Terminology Standardization:

Linking the database of drugs and commercial products with SNOMED CT ensures a uniform and consistent encoding of medications, facilitating interoperability and information exchange with other healthcare systems.

Coding Accuracy:

By using SNOMED CT, the company can encode medications precisely and granularly, improving data quality and reducing the possibility of errors in clinical documentation and information management.

Interoperability:

Linking the database of drugs and commercial products with SNOMED CT facilitates the exchange of medication information between different systems and platforms, allowing coordinated and coherent care.

Improvement in Research and Data Analysis:

Linking the database of drugs and commercial products with SNOMED CT allows the company to leverage this information for valuable insights into medication use and clinical practices.

Regulatory Compliance:

Using SNOMED CT in medication coding can help the company comply with established health informatics standards and regulations set by regulatory bodies and government entities, ensuring the integrity and security of clinical data.

Some Challenges in the Implementation of SNOMED CT at a Technical Level

The process of manually linking a custom-created database for a specific company with SNOMED CT is often a challenging path, considering several challenges:

Different Data Schemas:

Databases may have different data schemas, making direct correspondence between fields and tables difficult.

Nomenclature Inconsistency:

Field names may vary between databases, complicating the identification of direct correspondences.

Complexity of Terminology:

SNOMED CT is a highly detailed and complex medical coding system that covers a broad range of medical concepts. Managing this complexity and understanding specific terminology can be challenging for those performing mapping.

Concept Hierarchy:

SNOMED CT organizes concepts in a hierarchy that reflects subtype and supertype relationships. Correctly mapping concepts from a database to the SNOMED CT hierarchy can be complicated and requires a deep understanding of the terminology structure.

Changes and Updates:

SNOMED CT is regularly updated to reflect advances in medical practice and research. Staying up-to-date with changes and ensuring mappings are consistent with the latest versions of SNOMED CT can be an ongoing challenge.

Ambiguity in Terminology:

Some terms in SNOMED CT may be ambiguous or have multiple interpretations, making precise mapping to database concepts challenging.

The costs of the mapping process, measured in terms of time and human resources, are often high, and precision may be affected by human errors. Therefore, ODEV.tech has implemented a system that, through Artificial Intelligence, allows associating terms and concepts from any database with the corresponding codes and descriptions in SNOMED CT.

The use of Artificial Intelligence for data mapping has been a key factor in the success of this application, introducing the following advantages:

Efficiency:

By analyzing large volumes of data quickly and accurately, it allows mapping fields and establishing relationships between databases efficiently.

Error Reduction:

It decreases the likelihood of human errors during the mapping process. Artificial Intelligence learns patterns and suggests accurate mappings, minimizing the risks of inconsistencies.

Flexibility in Data Integration:

It integrates with different types of databases, sources, formats, and systems, enabling interoperability between heterogeneous systems.

Advanced Analysis and Visualization:

It provides the capability for analysis and visualization of data once correctly mapped. This allows discovering patterns, trends, and hidden relationships in data that can be valuable for decision-making.

Conclusion

The standardized and detailed clinical terminology provided by SNOMED CT offers several advantages, facilitating interoperability between health information systems and enabling different platforms and healthcare providers to share data effectively and accurately.

Health-related companies and institutions with their own databases of drugs and commercial products that want to adopt SNOMED CT terminology without negatively impacting their systems, face a challenging problem.

While the implementation of a mapping system using Artificial Intelligence to link databases requires a significant initial investment, it offers key advantages, including efficiency, accuracy, adaptability, and advanced analysis capabilities. This enables boosting operational efficiency and informed decision-making within an organization, resulting in substantial cost savings in terms of human resources and time for repetitive and error-prone tasks in the long run.