Generating FHIR Types in Python: How @atomic-ehr/codegen Boosts Development Efficiency
@atomic-ehr/codegen: Python FHIR Types - Health Samurai
Summary
@atomic-ehr/codegen is a generator that automatically produces strongly typed Pydantic models from any FHIR package. This eliminates the need for developers to wrestle with raw dictionaries or guess field names, enabling type-safe coding practices.
Details
The article introduces @atomic-ehr/codegen, a tool designed to automatically generate Python Pydantic v2 models for FHIR resources and data types. Developers can use this generator to achieve type safety—including IDE autocomplete and validation—across multiple FHIR packages (like R4 or US Core). Specifically, the output includes Pydantic models with proper field types, aliases, and built-in validation logic for each resource. It also supports integration with `fhirpy` async clients and handles custom extension fields (e.g., `_birthDate`). This approach eliminates the manual effort of mapping the entire FHIR specification into Python code, allowing developers to focus on business logic implementation. This significantly accelerates the development cycle for applications requiring robust data structures, which is highly relevant for advancing standardized and efficient system interoperability in healthcare IT. The process involves installing the generator via npm and running a TypeScript script to generate a ready-to-use Python package containing all necessary type definitions.
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