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Towards FHIR Pattern Languages: Reusable Guidance for HIE
Jörn Guy Süss1, Michael Osborne1, John Carter2
1Australian e-Health Research Centre (AEHRC), CSIRO.
Studies in Health Technology and Informatics
|July 16, 2026
Summary
This study introduces pattern languages for authoring Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) artifacts and implementing large-scale FHIR exchange systems. These patterns promote reuse and explicit trade-offs for better health information exchange outcomes.
Area of Science:
- Health Informatics
- Software Engineering
- Information Systems
Background:
- Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) is widely adopted for health information exchange.
- Inconsistent delivery outcomes and repeated problem-solving hinder efficient FHIR implementation.
- Need for standardized, reusable practices in FHIR artifact authoring and system implementation.
Purpose of the Study:
- To present two complementary pattern languages for authoring FHIR artifacts and implementing distributed FHIR exchange systems.
- To capture and disseminate proven practices for scalable and reliable health information exchange.
- To facilitate reuse of solutions across different teams and contexts.
Main Methods:
- Development of two pattern languages: one for FHIR artifact authoring (profiles, terminology, implementation guides) and one for distributed FHIR systems.
- Explicitly defining forces and trade-offs within the pattern languages.
- Establishing a publication pipeline for generating multiple output formats (website, EPUB, DocBook) from a single source, including an AI-navigable index.
Main Results:
- Two comprehensive pattern languages are presented, addressing FHIR artifact creation and large-scale data exchange.
- A publication pipeline ensures accessibility and reusability of the patterns in various formats.
- A machine-consumable index (llms.txt) is generated to support AI-assisted navigation and quick-start pathways.
Conclusions:
- The proposed pattern languages offer a structured approach to improve FHIR implementation consistency and efficiency.
- Explicitly documenting trade-offs enables informed decision-making in health information exchange projects.
- Future work includes operational validation and development of AI-assisted quick-start pathways for broader adoption.
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