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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Campbell, WS; Carter, AB; Cushman-Vokoun, AM; Greiner, TC; Dash, RC; Routbort, M; de Baca, ME; Campbell, JR

Published Date

  • May 2019

Published In

Volume / Issue

  • 21 / 3

Start / End Page

  • 408 - 417

PubMed ID

  • 30797065

Pubmed Central ID

  • PMC6521887

Electronic International Standard Serial Number (EISSN)

  • 1943-7811

Digital Object Identifier (DOI)

  • 10.1016/j.jmoldx.2018.12.002


  • eng

Conference Location

  • United States