Molecular Definition Implementation Guide for Molecular Data Types
1.0.0-ballot1 - ci-build
Molecular Definition Implementation Guide for Molecular Data Types
1.0.0-ballot1 - ci-build
Molecular Definition Implementation Guide for Molecular Data Types, published by HL7 International / Clinical Genomics. This guide is not an authorized publication; it is the continuous build for version 1.0.0-ballot1 built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/HL7/molecular-definition-data-types/ and changes regularly. See the Directory of published versions
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Developing a set of specialized Molecular Definition Profiles to represent discrete genetic data is essential to support clinical use cases that require reliable genetic information across various applications and institutions. The following paragraphs list some of the motivating drivers for this implementation guide.
Modern computational analyses in genomics demand cleaner, more expressive data representations that go beyond the limitations of traditional methods. Simply capturing sections of a PDF report is no longer sufficient, as such unstructured data hinders automated processing, integration, and advanced analytics. To fully leverage the power of computational tools and enable scalable, precise interpretation of genetic information, it is imperative to develop a FHIR resource that supports structured, discrete genetic data that convey both data content and their semantics. This will facilitate more accurate interpretation, seamless data exchange, and robust clinical decision support.
Current approaches to representing genomic data within FHIR—whether through existing resources or profiles—do not adequately address the needs of discrete genomic data representation. Feedback from implementers consistently highlights that these methods are often too complex, ambiguous, and insufficiently granular, leading to challenges in accurate data capture, interpretation, and interoperability. This complexity hampers clinical adoption and limits the effective use of genomic information in decision support and research. Therefore, a dedicated, streamlined FHIR resource is needed to provide clear, precise, and user-friendly representation of discrete genetic variants, improving both implementation feasibility and clinical utility.
By enabling patient-agnostic FHIR artifacts, Molecular Definition resource would facilitate linking genetic variants to disease associations, drug interactions, and risks of adverse drug events (ADEs), thereby enhancing precision medicine and pharmacogenomics. In addition, this resource can enable seamless integration of genetic knowledge bases with clinical systems like EHRs, ensuring interoperability and real-time access to up-to-date variant interpretations. Additionally, it could support the (re-)interpretation of variants as genomic knowledge evolves, allowing clinical decision support systems to provide the most current guidance. This approach addresses current limitations where genetic data is often embedded only within Observations resource instances, restricting broader clinical and research utility.
To ensure broad adoption and interoperability, the Molecular Definition resource and profiles must support stakeholders beyond HL7, including those involved in national and international genomics initiatives such as ONC’s Sync for Genes and NHGRI’s eMERGE phases 3 and 4. Also, it should align with and facilitate integration with the Global Alliance for Genomics and Health (GA4GH) specifications and driver projects such as:
The Molecular Definition resource and its specialized profiles offer cleaner data structures and enhanced semantic expression, aligning more naturally with FHIR granular architecture through focused yet connected resources. This design simplifies the complexity of existing Observation profiles by reducing dependence on loosely structured Observation components that often obscure the true meaning of genetic data. By enabling attributes to be attached at the appropriate levels such as the report, observation, or variation levels. Therefore, it preserves semantic clarity and improves data integrity. This focused and well-structured resource not only streamlines implementation but also enhances the precision and usability of genomic information within clinical workflows and interoperable systems.