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C-CDA on FHIR, published by HL7 International / Cross-Group Projects. This guide is not an authorized publication; it is the continuous build for version 1.2.0 built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/HL7/ccda-on-fhir/ and changes regularly. See the Directory of published versions

Mapping Background

Consolidated Clinical Document Architecture (C-CDA) and Fast Healthcare Interoperability Resources (FHIR) US Core are two of the most common standards for exchanging clinical data in the United States. This project’s goals are to establish HL7 mapping transformation guidance to provide clarity and consistency in translating data between C-CDA and FHIR and between FHIR and C-CDA.

In our first publication, we focused on the subset of domains that are recognized as the most exchangeable concepts in the industry. This first publication is limited to Problems, Allergies, Medications, Immunizations, Procedures, and Patient (PAMI+) domains. Acknowledging the various stages of maturity for each domain, we included the entire work for these concepts. This project was scoped independently of the document-level profiles developed in earlier versions of this guide.

Mapping Consensus

Note that C-CDA → FHIR mappings had a significant exposure to achieve multi-vendor consensus. This included through two FHIR Connectathons (September 2022 and January 2023) and regular weekly engagement. Vendors and organizations participating in this process include:

  • Cerner (Oracle)
  • Diameter Health (Availity)
  • Google
  • MDIX
  • Redox
  • Smile Digital Health
  • Veterans Administration (participation only in semantic and logical mappings)

The FHIR → C-CDA mappings have only been piloted by a single company (Cerner/Oracle) at this time and have not achieved multi-vendor consensus or connectathon testing yet.

Transformation Challenges and Limitations

Bi-directional automated transform is possible in constrained use cases but is not lossless due to varying flexibility and expressiveness in the standards. Use of extensions may mitigate the loss of information in transformation but may not be included in this guide. The CDA content is scoped by C-CDA R2.1 and the C-CDA Companion Guide R2, FHIR content by US Core R4, and, by implication, US Core Data for Interoperability (USCDI).

We employed several tactics to meet our goals. Standards developers drafted maps based on the respective specifications, and these were reviewed by stakeholders both offline and at public, regularly scheduled meetings. These meetings included implementors, terminologists, regulatory and public health representatives, and strategists, who engaged in realignment, consensus-seeking, and reformatting of the maps for a variety of audiences. Difficult questions were escalated to the work groups responsible for the specifications. Issues and their resolutions were logged in the publicly accessible project site.

In addition, the project team employed the example-based approach through Connectathons with vendors and experts in the standards community. The approach involves sharing inbound examples among the vendors, comparing the outbound artifacts generated by these vendors, and discussing with the group to achieve alignment in best practice recommendations. Our team has been focused on the generated artifacts, regardless of the transformation technologies, so any vendor is empowered to achieve the same transformation results. Note that, to date, the Connectathons have addresses only the CDA-to-FHIR cases, though two implementers have provided feedback on the FHIR-to-CDA cases.

By establishing the HL7 mapping transformation guidance, the project provides clarity and consistency in translating data between C-CDA and FHIR. This clarity and consistency are critical to ensure interoperability and communication across different healthcare systems, devices, applications and ensure accurate public reporting and analytics. Ultimately, consistent transforms between standards reduces amount of the duplicated patient data artifacts and lead to better patient care and improved healthcare outcomes. In addition, this work clearly identifies elements where divergent assumptions impede reliably correct and unambiguous translation. These elements may present opportunities for refining the standards.

To access the mappings, click below: