Drug Extension Authoring

Model-Driven and Scalable Authoring

Drug extension content is not typically authored manually on a concept-by-concept basis. Instead, it is derived from existing national drug dictionaries using a structured and scalable process.

This approach is comparable to the development of terminologies such as LOINC, where content is generated based on combinations of well-defined attributes.

Process

1

Mapping Atomic Components

The process begins with mapping the atomic elements of the local drug dictionary to SNOMED CT and related standards. These include:

• substances (active ingredients)

• dose forms

• units of measure

• strength representations

SNOMED CT aligns with EDQM (European Directorate for the Quality of Medicines) standards for dose forms. Where local dictionaries also use EDQM, this significantly facilitates mapping and interoperability.

2

Deriving Drug Concepts

Once atomic elements are mapped, higher-level drug concepts are generated based on combinations identified in the local dictionary.

These include:

• Medicinal Products (MP)

• Medicinal Product Forms (MPF)

• Clinical Drugs (CD)

• Real Clinical Drugs (RCD)

• Packaged Clinical Drugs (PCD)

• Real Packaged Clinical Drugs (RPCD)

Concepts are typically created algorithmically, using rules derived from the SNOMED CT concept model.

3

Reuse and Gap Filling

During concept generation:

• Existing concepts in the International Edition (MP, MPF, CD) should be reused wherever available

• Missing concepts are created in the national extension

• These missing concepts may later be submitted for promotion to the International Edition

This ensures consistency while allowing extensions to meet immediate national requirements.

4

Validation and Refinement

The generation process is typically implemented as:

• an automated bulk creation process

• followed by manual review and quality assurance

This hybrid approach balances scalability with clinical and terminological accuracy.

Typical Authoring Challenges

Drug extension development involves several common challenges, including:

• Variations in ingredient representation across local dictionaries

• Limited granularity in source data (e.g. missing strength or form details)

• Rounding rules for strength normalization

• Non-normalized strength expressions, including numerator/denominator inconsistencies

• Differences in units of measure and representation conventions

Addressing these challenges requires careful normalization and alignment with the SNOMED CT concept model.

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