Data Element
Variant Interpretation
Description

The categorical or clinical assessment of the genetic variant data, where interpretation is necessary to fully understand the significance.
Submitted By: Steve Bratt / CodeX (Common Oncology Data Elements eXtensions), a member-driven HL7 FHIR Accelerator
Data Element Information
Use Case Description(s)
Use Case Description Note that while genetics involves the study of genes and their effects, genomics takes a broader approach and assesses a patient’s genes, gene interactions, and the patient’s environment. By studying these complex relationships, genomics can help scientists delineate why come patients get sick and why others don’t (based on specific environmental or exposure triggers or patient behaviors). Genomic and genetic technologies include proteomics, pharmacogenetics, pharmacogenomics, and stem cell therapy.

Enabling semantically meaningful genomic data exchange between laboratories, patients, clinicians, and other stakeholders is critical to personalized precision medicine. Analysis of genomic data and family history can drive research, enhancing the understanding of underlying genetic factors contributing to disease and health, while also contributing to clinical decision-making.

Care incorporating genomic data can be complex, and often involves multiple different healthcare entities, thus underlining the need for interoperable genomic data sharing. For example, a patient undergoing evaluation and treatment for non-small-cell lung cancer may need data to be shared between a local community hospital, a cancer center, a commercial diagnostics lab where next-generation sequencing (NGS) can be done, payer entities for prior authorization, etc.

As the use of next-generation sequencing (NGS) continues to dramatically increase in frequency, the need for interpreting clinical significance for new novel variants increases as well. The demand for genomic data standards is evident by the growing use of nomenclatures for genomic variation, such as that created by the Human Genome Variation Society (HGVS).

Standardizing genomic data representation to facilitate interoperability enhances our capabilities to aggregate genomic data to drive research forward and to improve clinical care for:
* Continual development of the archives/databases needed for genomic research:
==== National Center for Biotechnology Information (NCBI) ClinVar
-------------§ Free, publicly accessible archive for clinical interpretations of genomic variants for reported conditions.
==== National Center for Biotechnology Information (NCBI) dbVar database
-------------§ Database of human genomic structural variation (SV), which can help facilitate clinical interpretation of novel structural variants.
==== National Institutes of Health (NIH) GenBankÒ database
-------------§ A genetic sequence database, with an annotated collection of all publicly available DNA sequences.
* Enhancing ability to share genomic lab reports
* Integrating genomic test results in patient portals
* Accelerating clinically related genomics research via the sharing of genomic data with cancer registries, clinical trial matching organizations, and research databases to promote population health and research development.
* Pharmacogenomics research and medication management: using a medication list and/or pharmacy database, genomic lab results, and a genomic database to identify drug-gene interactions
* Using genomic results to trigger clinical decision support in EHRs and point-of-care apps for precision medicine by linking patient-specific data with multiple lab/reference knowledge bases for information and treatment options:
==== Patient and provider education
==== Population health initiatives
==== Disease risk, medication regimen, and treatment management

Genomic data continues to play a growing role in guiding care for oncology. Genomics also possesses an ever-expanding role in clinical understanding and management in areas such as, but not limited to:
* Pediatrics: structural birth defects, prenatal diagnosis, and testing
* Cardiology: heritable cardiovascular diseases such as Marfan syndrome, long QT syndrome, hypertrophic cardiomyopathy, familial hypercholesterolemia, etc.

Examples of current or recent initiatives targeting interoperability for genomic data include those related to the Sync for Genes initiative, which seeks to standardize sharing of genomic data among laboratories, providers, patients, and researchers. In phase 1 and 2, sites and pilot focus areas included:
* Counsyl and Intermountain Healthcare: Family Health History Genetics
* Food & Drug Administration: Sequencing Quality and Regulatory Genomics
* Foundation Medicine and Vanderbilt: Somatic/Tumor Next Generation Sequencing
* Illumina: Next Generation Sequencing Solutions
* National Marrow Donor Program: Patient and Donor Matching
* Lehigh Valley Health Network: Pharmacogenomics
* Utah Department of Health: Newborn Screening
* Weill Cornell Medicine: Cancer Genomic Decision Support
Estimate the breadth of applicability of the use case(s) for this data element * Patients and their affiliated clinical care teams
---- Specifically for cancer, the American Cancer Society (ACS) projected that 1.8 million new cancer diagnoses were made in 2020. There were 16.9 million American cancer survivors in 2019.
---- In 2020, the American Heart Association (AHA) recommended genetic testing for patients diagnosed with cardiomyopathy, arrhythmias, vascular disorders and lipid disorders (such as familial hypercholesterolemia).
* organizations involved with genomic research and management
---- National Marrow Donor Program
---- American Society of Clinical Oncology (ASCO)
---- American Society for Radiation Oncology (ASTRO)
---- American Heart Association (AHA) o American College of Cardiology (ACC)
---- Food & Drug Administration o National Human Genome Research Institute (NHGRI)
* Genomic databases/archives
---- National Center for Biotechnology Information (NCBI) ClinVar and dbVar database
---- National Institutes of Health (NIH) GenBank database
* EHRs such as Epic and Cerner
* PHR systems, mobile health systems and apps
* Health information exchange systems
* Organizations/laboratories providing patient genetic tests, e.g. Myriad Women’s Health/Counsyl
* integrated systems for genomic analysis, e.g. Illumina, Inc.
* genomic registries, e.g. NCBI Genetic Testing Registry (GTR)
* clinical trials and clinical trial-matching services

This submission is made on behalf of CodeX (Common Oncology Data Elements eXtensions), a member-driven HL7 FHIR Accelerator community of professional medical societies, health systems, industry and others seeking to achieve interoperability via the mCODE (minimal Common Oncology Data Elements) standard in order to drive step-change improvements in cancer patient care and research.
https://confluence.hl7.org/display/COD/CodeX+Home

LINKS:
Sync for Genes
https://www.healthit.gov/topic/sync-genes

NCBI ClinVar database
https://www.ncbi.nlm.nih.gov/clinvar/

NCBI dbVar database
https://www.ncbi.nlm.nih.gov/dbvar/

NIH GenBank database
https://www.ncbi.nlm.nih.gov/genbank/

Example of clinical trial involving recruiting patients for genomics research
https://clinicaltrials.gov/ct2/show/NCT01087320

Example of pharmacogenomics clinical decision support for EHRs
https://pubmed.ncbi.nlm.nih.gov/30605914/
Link to use case project page https://www.healthit.gov/topic/sync-genes
Healthcare Aims
  • Improving patient experience of care (quality and/or satisfaction)
  • Improving the health of populations
  • Reducing the cost of care
  • Improving provider experience of care
Maturity of Use and Technical Specifications for Data Element
Applicable Standard(s) * SNOMED CT
* LOINC
* HUGO Gene Nomenclature Committee (HGNC) notation
* Genetic Variant standards:
---- Catalogue of Somatic Mutations in Cancer (COSMIC)
---- International System for Human Cytogenic Nomenclature (ISCN)
---- Human Genome Variation Society (HGVS) Nomenclature

LINKS:

SNOMED CT
https://www.snomed.org/snomed-ct/case-studies/binding-snomed-ct-and-genomic-data-for-cancer-care

LOINC
https://academic.oup.com/jamia/article/22/3/621/773317

HGNC Notation
https://www.genenames.org/

HGVS Nomenclature
https://varnomen.hgvs.org/

COSMIC
https://cancer.sanger.ac.uk/cosmic

ISCN
https://www.coriell.org/0/sections/support/global/iscn_help.aspx?PgId=263
https://varnomen.hgvs.org/
Additional Specifications Variant Call Format
https://samtools.github.io/hts-specs/VCFv4.2.pdf

GHIR Genomic Reporting Implementation Guide
http://hl7.org/fhir/uv/genomics-reporting/

FHIR Genomics Implementation Guide
https://www.hl7.org/fhir/genomics.html

HL7 FHIR Implementation Guide: minimal Common Oncology Data Elements (mCODE)
http://hl7.org/fhir/us/mcode/

GA4GH Variant Representation Specification
https://vrs.ga4gh.org/en/stable/

eMerge FHIR Specification, funded by the National Human Genome Research Institute (NHGRI)
https://emerge-fhir-spec.readthedocs.io/en/latest
Current Use In limited use in production environments
Supporting Artifacts Genomic data is used in production environments, especially in relation to the management and treatment of cancer. There are ever increasing needs and use cases for exchanging genomic data as described above, however the standards for genomic data sharing are not widely standardized.
Extent of exchange 5 or more. This data element has been tested at scale between multiple different production environments to support the majority of anticipated stakeholders.
Supporting Artifacts The Sync for Genes initiative, launched in 2017, accelerates development of genomic data interoperability via standards like FHIR.

Phase 1 targeted standardizing genomic data and involved 5 organizations that included patients, laboratories, providers, government, health IT developers, and academia:
* Counsyl and Intermountain Healthcare: Family Health History Genetics
* Food & Drug Administration: Sequencing Quality and Regulatory Genomics
* Foundation Medicine and Vanderbilt: Somatic/Tumor Next Generation Sequencing
* Illumina: Next Generation Sequencing Solutions
* National Marrow Donor Program: Patient and Donor Matching

Phase 2 focused on advancing sharing of standardized genomic data (including genomic test results) using FHIR.
* Lehigh Valley Health Network: Pharmacogenomics
* National Marrow Donnor Program: Patient and Donor Matching
* Utah Department of Health: Newborn Screening
* Weill Cornell Medicine: Cancer Genomic Decision Support

Phase 3 involved exchange of standardized genomic data with laboratories.
* Baylor College of Medicine Human Genome Sequencing Center: eMERGE FHIR Specification
* The National Marrow Donor Program: Human Leukocyte Antigen (HLA) Reporting IG
https://www.healthit.gov/topic/sync-genes
Potential Challenges
Restrictions on Standardization (e.g. proprietary code) No restrictions on standardization of genomic data elements.
Restrictions on Use (e.g. licensing, user fees) No restrictions on use of genomic data elements.
Privacy and Security Concerns Any use/sharing of genomic data must follow processes in line with:

* the Genetic Information Nondiscrimination Act (GINA, https://www.govinfo.gov/content/pkg/PLAW-110publ233/pdf/PLAW-110publ233.pdf)
* the Health Insurance Portability and Accountability Act (HIPAA, https://www.hhs.gov/hipaa/index.html)
Estimate of Overall Burden --
ONC Evaluation Details
Each submitted Data Element has been evaluated based on the following criteria. The overall Level classification is a composite of the maturity based on these individual criteria. This information can be used to identify areas that require additional work to raise the overall classification level and consideration for inclusion in future versions of USCDI
Criterion #1
Maturity - Current Standards		 Level 2 - Data element is represented by a terminology standard or SDO-balloted technical specification or implementation guide.
Criterion #2
Maturity - Current Use 		Level 1 - Data element is captured, stored, or accessed in at least one production EHR or HIT module.
Criterion #3
Maturity - Current Exchange		 Level 1 - Data element is electronically exchanged between two production EHRs or other HIT modules using available interoperability standards.
Criterion #4
Use Case(s) - Breadth of Applicability		 Level 2 - Use cases apply to most care settings or specialties.

Comment

Submitted by suchen on

This data element has…

This data element has support from the HL7® FHIR® CodeX™ Accelerator community. CodeX supported FHIR data standards for cancer, genomics and cardiovascular health have a rapidly growing footprint of traction and adoption. The CodeX community is rapidly growing its membership, with invested Member leaders driving implementation-focused use case pilots forward.

The Genomics data elements currently at Level 1 in USCDI (Gene Studied, Variant Data, Variant Type, Variant Interpretation) have representation in the following CodeX supported FHIR Implementation Guides (IGs):

The Genomics data elements currently at Level 1 in USCDI (Gene Studied, Variant Data, Variant Type, Variant Interpretation) are important to the genomics branch (GenomeX) of the CodeX Accelerator, currently with 2 active use case pilots:

  • Genomics Data Exchange Use Case:
    • https://confluence.hl7.org/display/COD/GenomeX+-+Genomics+Data+Exchange
    • Aims to develop scenario-based profiles/import specifications with the FHIR Genomics Reporting IG to share structured genomic data from a lab to receiving organizations (EHR/genomic data repository) so providers can use the data in patient care. Currently kicking off the initial minimum viable pilot which will include developing a standard data set across participants.
    • Current key aims for Phase 0 of this use case include testing sharing of synthetic genomics lab report with others (including EHRs) via FHIR. Goal to participate in 2024 January HL7 Connectathon
  • Genomics Operations Use Case
    • https://confluence.hl7.org/display/COD/GenomeX+-+Genomics+Operations
    • Aims to create an approach to utilize genomic data such that the FHIR Genomics Reporting IG operations will present a uniform interface and give providers easier access to actionable genetic variation information that could impact treatment options for patients. Currently building community and identifying specific problems and solutions.
    • Current key aims for Phase 0 of this use case include exchange of synthetic data demonstrate genomics operations capability for extracting genomics data from repositories for Molecular Tumor Board applications. Goal to participate in the 2024 January HL7 Connectathon.

 

Further pertinent links illustrating real world adoption and traction of the mCODE and Genomics Reporting IGs include:

Further mCODE traction/adoption & FHIR-based genomics data exchange in Germany (slide 12): https://confluence.hl7.org/display/COD/USCDI%3A+Comments+from+CodeX+Community?preview=/184913335/184927637/2023_09%20CodeX%20Overview%20for%20USCDIv5.pdf

Submitted by bheale on

clarification or new elements

Variant Interpretation – This has been modeled by the HL7 FHIR Clinical Genomics working group to parse out several types of interpretation. Recommend review and determine how to incorporate in the definition or consider breaking into sub-elements.  Molecular implication http://build.fhir.org/ig/HL7/genomics-reporting/StructureDefinition-molecular-consequence.html In interpreting variants, it is common practice to indicate ‘feature-consequence’ (change to a molecular component of gene expression) or ‘functional-effect’ (higher-level functional outcome on the functional aspects of the gene product). Therapeutic implication http://build.fhir.org/ig/HL7/genomics-reporting/StructureDefinition-therapeutic-implication.html In interpreting variants, it is common practice to indicate potential therapeutic indications or counter-indications. Diagnostic implication  http://build.fhir.org/ig/HL7/genomics-reporting/StructureDefinition-diagnostic-implication.html In interpreting variants, it is common practice to indicate a potential diagnosis or the effect the variant has on a possible diagnosis. This includes the important concept of ‘clinical significance.’

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