ABSTRACT
BACKGROUND: VCF formatted files are the lingua franca of next-generation sequencing, whereas HL7 FHIR is emerging as a standard language for electronic health record interoperability. A growing number of FHIR-based clinical genomics applications are emerging. Here, we describe an open source utility for converting variants from VCF format into HL7 FHIR format. RESULTS: vcf2fhir converts VCF variants into a FHIR Genomics Diagnostic Report. Conversion translates each VCF row into a corresponding FHIR-formatted variant in the generated report. In scope are simple variants (SNVs, MNVs, Indels), along with zygosity and phase relationships, for autosomes, sex chromosomes, and mitochondrial DNA. Input parameters include VCF file and genome build ('GRCh37' or 'GRCh38'); and optionally a conversion region that indicates the region(s) to convert, a studied region that lists genomic regions studied by the lab, and a non-callable region that lists studied regions deemed uncallable by the lab. Conversion can be limited to a subset of VCF by supplying genomic coordinates of the conversion region(s). If studied and non-callable regions are also supplied, the output FHIR report will include 'region-studied' observations that detail which portions of the conversion region were studied, and of those studied regions, which portions were deemed uncallable. We illustrate the vcf2fhir utility via two case studies. The first, 'SMART Cancer Navigator', is a web application that offers clinical decision support by linking patient EHR information to cancerous gene variants. The second, 'Precision Genomics Integration Platform', intersects a patient's FHIR-formatted clinical and genomic data with knowledge bases in order to provide on-demand delivery of contextually relevant genomic findings and recommendations to the EHR. CONCLUSIONS: Experience to date shows that the vcf2fhir utility can be effectively woven into clinically useful genomic-EHR integration pipelines. Additional testing will be a critical step towards the clinical validation of this utility, enabling it to be integrated in a variety of real world data flow scenarios. For now, we propose the use of this utility primarily to accelerate FHIR Genomics understanding and to facilitate experimentation with further integration of genomics data into the EHR.
Subject(s)
Decision Support Systems, Clinical , Genomics , Electronic Health Records , Humans , Knowledge Bases , OncogenesABSTRACT
Introduction: Variant annotation is a critical component in next-generation sequencing, enabling a sequencing lab to comb through a sea of variants in order to hone in on those likely to be most significant, and providing clinicians with necessary context for decision-making. But with the rapid evolution of genomics knowledge, reported annotations can quickly become out-of-date. Under the ONC Sync for Genes program, our team sought to standardize the sharing of dynamically annotated variants (e.g., variants annotated on demand, based on current knowledge). The computable biomedical knowledge artifacts that were developed enable a clinical decision support (CDS) application to surface up-to-date annotations to clinicians. Methods: The work reported in this article relies on the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) Genomics and Global Alliance for Genomics and Health (GA4GH) Variant Annotation (VA) standards. We developed a CDS pipeline that dynamically annotates patient's variants through an intersection with current knowledge and serves up the FHIR-encoded variants and annotations (diagnostic and therapeutic implications, molecular consequences, population allele frequencies) via FHIR Genomics Operations. ClinVar, CIViC, and PharmGKB were used as knowledge sources, encoded as per the GA4GH VA specification. Results: Primary public artifacts from this project include a GitHub repository with all source code, a Swagger interface that allows anyone to visualize and interact with the code using only a web browser, and a backend database where all (synthetic and anonymized) patient data and knowledge are housed. Conclusions: We found that variant annotation varies in complexity based on the variant type, and that various bioinformatics strategies can greatly improve automated annotation fidelity. More importantly, we demonstrated the feasibility of an ecosystem where genomic knowledge bases have standardized knowledge (e.g., based on the GA4GH VA spec), and CDS applications can dynamically leverage that knowledge to provide real-time decision support, based on current knowledge, to clinicians at the point of care.
ABSTRACT
OBJECTIVE: Enabling clinicians to formulate individualized clinical management strategies from the sea of molecular data remains a fundamentally important but daunting task. Here, we describe efforts towards a new paradigm in genomics-electronic health record (HER) integration, using a standardized suite of FHIR Genomics Operations that encapsulates the complexity of molecular data so that precision medicine solution developers can focus on building applications. MATERIALS AND METHODS: FHIR Genomics Operations essentially "wrap" a genomics data repository, presenting a uniform interface to applications. More importantly, operations encapsulate the complexity of data within a repository and normalize redundant data representations-particularly relevant in genomics, where a tremendous amount of raw data exists in often-complex non-FHIR formats. RESULTS: Fifteen FHIR Genomics Operations have been developed, designed to support a wide range of clinical scenarios, such as variant discovery; clinical trial matching; hereditary condition and pharmacogenomic screening; and variant reanalysis. Operations are being matured through the HL7 balloting process, connectathons, pilots, and the HL7 FHIR Accelerator program. DISCUSSION: Next-generation sequencing can identify thousands to millions of variants, whose clinical significance can change over time as our knowledge evolves. To manage such a large volume of dynamic and complex data, new models of genomics-EHR integration are needed. Qualitative observations to date suggest that freeing application developers from the need to understand the nuances of genomic data, and instead base applications on standardized APIs can not only accelerate integration but also dramatically expand the applications of Omic data in driving precision care at scale for all.