Assessment of the adequacy of the Fast Healthcare Interoperability Resources (FHIR) Genomics standard for the representation of somatic testing reports

Read full text
Authors: Robert H. Dolin, Bret S.E. Heale, James Patterson, Kevin Power, May Terry, Howard Anton, James Chen, Kashmira Sawant, Srikar Chamala
Cited by: 0
References: 20

Abstract

Objectives: Enabling the unambiguous communication of cancer genetic testing results and the corresponding therapeutic implications of identified genetic variants is of major clinical importance. In this report, we assess the adequacy of the Health Level Seven Fast Healthcare Interoperability Resources (FHIR) Genomics Reporting Implementation Guide, version 3 (also known as "FHIR Genomics") for the structured communication of semantic objects typically appearing in real-world somatic testing reports. Materials and Methods: The GenomeX project team, part of the CodeX FHIR Accelerator, performed the following assessment: (1) gather a convenience sample of public somatic reports; (2) create a representative somatic report that includes a wide range of observations taken from the sample reports; and (3) structure the representative somatic report using the FHIR Genomics standard. Results: Attempted encodings were categorized into 1 of the 4 buckets: (1) full encoding possible (eg, DNA variants); (2) full encoding with potential variability (eg, therapeutic implications); (3) partial encoding (eg, genomic study details); and (4) not encoded (ie, FHIR Genomics has no explicit or acceptable semantic target, such as for coarsely granular pertinent negatives). In our sample somatic report, 47 semantic objects were noted, of which 87% had either full encoding or full encoding with potential variability. Conclusion: While dynamic real-world requirements, as exemplified by cancer genomics, often arise more rapidly than can be standardized, we conclude that the FHIR Genomics version 3 standard is sufficiently robust for the communication of current actionable somatic testing results.

Fields of Study & Subjects

Electronic Health Records Systems Biomedical Text Mining and Ontologies Artificial Intelligence in Healthcare and Education

Publication details

Journal / source
JAMIA Open
Volume
9
Issue
2
Pages
ooag022–ooag022
Publication date
26 February 2026
Language
EN