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C-CDA to FHIR Mapping: Fully Unleashing the Power of Data

The first weekend of May in downtown New Orleans was full of energy. This reputation is not solely attributed to its renowned annual Jazz & Heritage Festival, but also thanks to the 33rd Health Level Seven International® (HL7) Fast Healthcare Interoperability Resources (FHIR®) Connectathon. The FHIR Connectathon is a forum that brings together developers, implementers, regulatory bodies, standard advocates, and other stakeholders to share their perspectives, challenges, and collaborate on progressing standards development for the healthcare interoperability ecosystem. As usual, the big room was electrifying during this two-day event, with more than 300 people attending 36 tracks.

I was responsible for leading the Consolidated Clinical Document Architecture (C-CDA) to FHIR mapping track. Attention and participation in this track and the underlying HL7 project has grown since we introduced it in 2022. The growth is driven by a number of factors, including the increase in uptake of the FHIR standard, large volume of C-CDA documents exchanged (Epic is reporting 24 million daily volume), and the overall growing demand for clinical data in healthcare. Many vendors have started developing proprietary mapping between C-CDA and FHIR to ensure their data can be used to power their data warehouses or downstream use cases regardless of data’s original format. However, these efforts, done in silos, can lead to inconsistency, confusion, and even errors when trying to use converted data at the industry level, and would require significant efforts to harmonize later.

The C-CDA to FHIR mapping project was created to help coordinate and align these efforts. This project aims to establish HL7 mapping guidance for C-CDA to FHIR and FHIR to C-CDA using the document types in C-CDA R2.1, FHIR US core and USCDI V1 data classes. Through close collaborations with standard experts and leading industry vendors, the C-CDA to FHIR mapping cross-project team have made great progress.

Most recently, we drafted our mapping guidance and example artifacts for a core set of clinical domains (patient, allergies, immunizations, medications, problems/conditions, procedures) and went through the HL7 ballot in May 2023. The ballot received over 130 comments from representatives from payers, vendors, government/academia, providers, pharmaceutical and other healthcare sectors, and the overall feedback has been positive.

During the Connectathon, the C-CDA to FHIR mapping cross-project team convened to review and triage the ballot comments, and gather consensus on some issues that may not have straightforward solutions, such as text mapping, negation translation, problem concern mapping, etc. We also discussed the roadmap for next steps, which included disposition of the comments/tickets, creating more C-CDA and FHIR mapping examples to provide broader coverage of the possible data elements, extending the guidance to the other clinical domains, enhancing vendor participation, making our mapping artifacts more accessible, and more.

We started working through mapping examples for the next batch of priority clinical domains during the Connectathon (lab results and vital signs). To do this, we put together the FHIR resource mapping generated from the same inbound CCD by different vendors’ (Cerner/Oracle, Availity, Google, MDIX, Redox), and compared each mapped data element side-by-side and line-by-line. We then used the group’s consensus to create best practice recommendations, documentation of known issues, and issues or improvement request from related standards.

The Connectathon was highly productive, but we have more work to do. I left the track feeling excited about the progress and impact that our workgroup is making, and confident about where we are going next. If you are interested in learning more or participating in this effort, feel free to reach out to me ([email protected]).

Download our FHIR eBook to learn more about Availity’s approach to automating legacy data at scale to FHIR conversion, while addressing data quality issues to meet government mandates, improve care quality, and increase analytics accuracy.