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A computable pathology report for precision medicine: extending an observables ontology unifying SNOMED CT and LOINC
Univ Nebraska Med Ctr, NE 68198 USA.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Indiana Univ Sch Med, IN 46202 USA.
Univ Nebraska Med Ctr, NE 68198 USA.
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2018 (English)In: JAMIA Journal of the American Medical Informatics Association, ISSN 1067-5027, E-ISSN 1527-974X, Vol. 25, no 3, p. 259-266Article in journal (Refereed) Published
Abstract [en]

The College of American Pathologists (CAP) introduced the first cancer synoptic reporting protocols in 1998. However, the objective of a fully computable and machine-readable cancer synoptic report remains elusive due to insufficient definitional content in Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) and Logical Observation Identifiers Names and Codes (LOINC). To address this terminology gap, investigators at the University of Nebraska Medical Center (UNMC) are developing, authoring, and testing a SNOMED CT observable ontology to represent the data elements identified by the synoptic worksheets of CAP. Investigators along with collaborators from the US National Library of Medicine, CAP, the International Health Terminology Standards Development Organization, and the UK Health and Social Care Information Centre analyzed and assessed required data elements for colorectal cancer and invasive breast cancer synoptic reporting. SNOMED CT concept expressions were developed at UNMC in the Nebraska LexiconA (c) SNOMED CT namespace. LOINC codes for each SNOMED CT expression were issued by the Regenstrief Institute. SNOMED CT concepts represented observation answer value sets. UNMC investigators created a total of 194 SNOMED CT observable entity concept definitions to represent required data elements for CAP colorectal and breast cancer synoptic worksheets, including biomarkers. Concepts were bound to colorectal and invasive breast cancer reports in the UNMC pathology system and successfully used to populate a UNMC biobank. The absence of a robust observables ontology represents a barrier to data capture and reuse in clinical areas founded upon observational information. Terminology developed in this project establishes the model to characterize pathology data for information exchange, public health, and research analytics.

Place, publisher, year, edition, pages
OXFORD UNIV PRESS , 2018. Vol. 25, no 3, p. 259-266
Keywords [en]
SNOMED CT; LOINC; Ontology; cancer synoptic reports; interoperability
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Other Computer and Information Science
Identifiers
URN: urn:nbn:se:liu:diva-147131DOI: 10.1093/jamia/ocx097ISI: 000426850500006PubMedID: 29024958OAI: oai:DiVA.org:liu-147131DiVA, id: diva2:1199517
Note

Funding Agencies|Patient Centered Outcomes Research Institute [CDRN-1306-04631]; National Institutes of Health [1U01HG009455-01]; Department of Pathology/Microbiology at the University of Nebraska Medical Center; Department of Internal Medicine at the University of Nebraska Medical Center

Available from: 2018-04-20 Created: 2018-04-20 Last updated: 2018-04-20

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