Genomics is the new frontier in clinical care and scientific discovery. Genomic assessments of oncogenes and tumor suppressor genes, that is gene loci associated with cancer development and prognosis, is rapidly becoming the standard of care for cancer.

Current molecular pathology reports are represented and stored in the electronic health record (EHR) as pdf documents and are not suitable for clinical decision support and data analytics. Incorporation of genetic data into the EHR as discrete data is not standard practice because of the complexity of the data itself (ADDIN RW.CITE{{2326 Warner,J.L. 2016; 2166 Masys,D.R. 2012}}(1, 2) and underrepresentation of genomic concepts in the SNOMED CT and LOINC concept models (ADDIN RW.CITE{{2327 Hoffman,M. 2005}}(3). 

Investigators at the University of Nebraska Medical Center used the SNOMED CT observables concept model and authored fully defined observable entity concepts for genomic information including Next Generation Sequence (NGS) data and non-sequence based molecular data.  These SNOMED CT concepts are deployed in the clinical information systems at UNMC beginning in the laboratory and molecular pathology information systems and bound to molecular observations. Encoded molecular data is then transmitted to the EHR and the institutional cancer tissue biobank and registry for clinical care, quality and research purposes.


1. Warner JL, Jain SK, Levy MA. Integrating cancer genomic data into electronic health records. Genome Med. 2016 Oct 26;8(1):113.
2. Masys DR, Jarvik GP, Abernethy NF, Anderson NR, Papanicolaou GJ, Paltoo DN, et al. Technical desiderata for the integration of genomic data into Electronic Health Records. J Biomed Inform. 2012 Jun;45(3):419-22.
3. Hoffman M, Arnoldi C, Chuang I. The clinical bioinformatics ontology: a curated semantic network utilizing RefSeq information. Pac Symp Biocomput. 2005:139-50.

Location: Nebraska, United States


The developments supports the following:
  • Biomarker data to genomic data
  • Immunohistochemistry (IHC)
  • FISH Sanger Sequencing
  • Next Generation Sequencing (Targeted gene loci and Whole genome and exome)
  • Diagnosis to Precision Medicine Histopathology – Diagnose IHC – Differential Diagnoses/Prognosis Molecular Pathology Diagnosis/Prognosis and targeted therapy

How SNOMED CT will be used

  • All concepts modeled according to SNOMED-LOINC cooperative agreement
  • LOINC identifiers
  • Full SNOMED CT concept model definitions
  • Observable entity hierarchy
  • New SNOMED CT content required
  • Body structures – gene loci definitions for proto-oncogenes
  • Primitive concepts with reference sets to Human Genome Nomenclature Committee (HGNC) identifiers
  • Inherent location of sample (neoplasm vs. non-neoplastic) differentiates somatic vs. germline mutation
  • New Property type –Sequence variant property
  • New Technique – Nucleotide sequencing technique

Why SNOMED CT was selected

The EHR system currently utilises SNOMED CT. The ontological basis of SNOMED CT supports linkage of clinical data to genetic information, the transfer of linked data and the interrogation of linked data within the Biobank environment.

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