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Americas
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Electronic health record
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
References
Description
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
Scope
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
How SNOMED CT will be used
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.
Why SNOMED CT will be used
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