- Browse by Author
Browsing by Author "Vreeman, Daniel J"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item A computable pathology report for precision medicine: extending an observables ontology unifying SNOMED CT and LOINC(Oxford, 2017-09-13) Campbell, Walter S; Karlsson, Daniel; Vreeman, Daniel J; Lazenby, Audrey J; Talmon, Geoffrey A; Campbell, James R; Medicine, School of MedicineBackground 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. Methods 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 Lexicon© SNOMED CT namespace. LOINC codes for each SNOMED CT expression were issued by the Regenstrief Institute. SNOMED CT concepts represented observation answer value sets. Results 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. Discussion 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.Item Impact of Selective Mapping Strategies on Automated Laboratory Result Notification to Public Health Authorities(2012-11) Gamache, Roland E; Dixon, Brian E.; Grannis, Shaun; Vreeman, Daniel JAutomated electronic laboratory reporting (ELR) for public health has many potential advantages, but requires mapping local laboratory test codes to a standard vocabulary such as LOINC. Mapping only the most frequently reported tests provides one way to prioritize the effort and mitigate the resource burden. We evaluated the implications of selective mapping on ELR for public health by comparing reportable conditions from an operational ELR system with the codes in the LOINC Top 2000. Laboratory result codes in the LOINC Top 2000 accounted for 65.3% of the reportable condition volume. However, by also including the 129 most frequent LOINC codes that identified reportable conditions in our system but were not present in the LOINC Top 2000, this set would cover 98% of the reportable condition volume. Our study highlights the ways that our approach to implementing vocabulary standards impacts secondary data uses such as public health reporting.