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

Area of Science:

• Bioinformatics
• Clinical Informatics
• Genomic Medicine

Background:

• Genetic variant data in electronic health records (EHRs) are often stored as unstructured text, hindering retrieval and clinical application.
• Clinicians face difficulties accessing historical genetic results for oncology patients, impacting treatment decisions.
• Identifying patients eligible for new therapies based on specific genetic mutations within EHRs is currently inefficient.

Purpose of the Study:

• To present and demonstrate a novel approach for incorporating discrete, computable genetic variant data into EHRs.
• To improve the accessibility and reusability of genetic sequence information for clinical decision-making.
• To facilitate population management and laboratory quality audits through standardized genetic data representation.

Main Methods:

• Utilized standard Health Level 7 (HL7) laboratory result message formats.
• Integrated international standards: Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Human Genome Variant Society (HGVS) nomenclature.
• Developed a scalable information management plan for discrete gene sequence data storage within EHRs.

Main Results:

• Demonstrated a method for representing, communicating, and storing discrete gene sequence data within EHRs.
• Enabled scalable data management for genetic variant information.
• Facilitated the integration of genetic data into clinical workflows.

Conclusions:

• The proposed information management plan effectively addresses challenges in managing discrete genetic variant data in EHRs.
• This approach supports clinicians at the point of care, enhances population health management, and aids laboratory quality assurance.
• Standardized representation of genetic data in EHRs is crucial for advancing genomic medicine and improving patient care.