In today's interconnected digital ecosystem, the seamless exchange of information between diverse systems is more critical than ever. A fundamental obstacle to achieving true interoperability lies in how different systems represent context information about concepts. Some systems embed this context directly within the terminology, using codes with rich, nuanced meanings. Others represent context through their information models, structuring data to convey context via relationships and attributes. This disparity creates a landscape where data exchanged between systems may lose its intended meaning unless carefully transformed and interpreted.

This chapter focuses on the strategies and methodologies for implementing transformations between these heterogeneous models. We will delve into the technical challenges of mapping context from terminologies to information models and explore practical solutions to bridge these differences. By examining real-world scenarios and leveraging interoperability standards, we aim to equip you with the knowledge and tools necessary to ensure that context is preserved and accurately conveyed during data exchange. Understanding these transformation processes is essential for developers, architects, and stakeholders striving to enhance interoperability and achieve consistent, reliable communication across varied systems.

Example of a transformation from a "Situation with explicit context" concept to a FHIR FamilyMemberHistory Resource information model

In the context of this SNOMED implementation guide, we have chosen to use the FHIR context model as an example of an information model for several reasons. FHIR is a widely adopted standard in the healthcare industry for exchanging electronic health records and related data. Its modular and extensible design makes it an ideal candidate to demonstrate how context information can be represented outside terminologies like SNOMED CT.

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Clinical Findings with Context vs. FHIR Resources

The following table maps SNOMED CT “Situations with Explicit Context” subhierarchies to relevant FHIR resources, showing how clinical context is represented across terminology and information models.

• Where FHIR Resource is listed as N/A, there may be no direct mapping in the base FHIR specification.

• Context properties such as subject and temporal frame help clarify how the SNOMED concept aligns with FHIR resource attributes like subject, status, or effective[x].

The table below maps specific types of Situations with Explicit contexts to specific FHIR resources and states the associated context properties

As seen in the previous table, depending on the type of context represented in the SWEC concept, it can be transformed into a Condition or Observation resource. Here, we provide some examples of values for different key elements.

Transforming information between the SNOMED concept model and a FHIR representation is a clear use case for interoperability or analytics. Creating a set of rules to reproducibly perform these transformations safely in real-world scenarios is possible.

We represent these rules as a decision table to offer a clear and structured approach to handling the heterogeneity between terminologies and information models. Although the list provided is not exhaustive and serves only as an example, it effectively demonstrates the critical considerations and steps involved in the transformation process. This approach helps readers understand not just the theoretical aspects but also the practical implementation of transformation rules, enabling them to apply similar strategies within their own systems to achieve seamless interoperability.