A Model Information Management Plan for Molecular Pathology Sequence Data Using Standards: From Sequencer to Electronic Health Record.

Incorporating genetic variant data into the electronic health record (EHR) in discrete computable fashion has vexed the informatics community for years. Genetic sequence test results are typically communicated by the molecular laboratory and stored in the EHR as textual documents. Although text documents are useful for human readability and initial use, they are not conducive for data retrieval and reuse. As a result, clinicians often struggle to find historical gene sequence results on a series of oncology patients within the EHR that might influence the care of the current patient. Second, identification of patients with specific mutation results in the EHR who are now eligible for new and/or changing therapy is not easily accomplished. Third, the molecular laboratory is challenged to monitor its sequencing processes for nonrandom process variation and other quality metrics. A novel approach to address each of these issues is presented and demonstrated. The authors use standard Health Level 7 laboratory result message formats in conjunction with international standards, Systematized Nomenclature of Medicine Clinical Terms and Human Genome Variant Society nomenclature, to represent, communicate, and store discrete gene sequence data within the EHR in a scalable fashion. This information management plan enables the support of the clinician at the point of care, enhances population management, and facilitates audits for maintaining laboratory quality.

A computable pathology report for precision medicine: extending an observables ontology unifying SNOMED CT and LOINC.

BACKGROUND: 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.

Semantic analysis of SNOMED CT for a post-coordinated database of histopathology findings.

OBJECTIVE: This research investigated the use of SNOMED CT to represent diagnostic tissue morphologies and notable tissue architectures typically found within a pathologist's microscopic examination report to identify gaps in expressivity of SNOMED CT for use in anatomic pathology. METHODS: 24 breast biopsy cases were reviewed by two board certified surgical pathologists who independently described the diagnostically important tissue architectures and diagnostic morphologies observed by microscopic examination. In addition, diagnostic comments and details were extracted from the original diagnostic pathology report. 95 unique clinical statements were extracted from 13 malignant and 11 benign breast needle biopsy cases. RESULTS: 75% of the inventoried diagnostic terms and statements could be represented by valid SNOMED CT expressions. The expressions included one pre-coordinated expression and 73 post-coordinated expressions. No valid SNOMED CT expressions could be identified or developed to unambiguously assert the meaning of 21 statements (ie, 25% of inventoried clinical statements). Evaluation of the findings indicated that SNOMED CT lacked sufficient definitional expressions or the SNOMED CT concept model prohibited use of certain defined concepts needed to describe the numerous, diagnostically important tissue architectures and morphologic changes found within a surgical pathology microscopic examination. CONCLUSIONS: Because information gathered during microscopic histopathology examination provides the basis of pathology diagnoses, additional concept definitions for tissue morphometries and modifications to the SNOMED CT concept model are needed and suggested to represent detailed histopathologic findings in computable fashion for purposes of patient information exchange and research. TRIAL REGISTRATION NUMBER: UNMC Institutional Review Board ID# 342-11-EP.