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. 2024 Dec 20:15:1458095.
doi: 10.3389/fphar.2024.1458095. eCollection 2024.

Integration of pharmacogenetic data in epic genomic module drives clinical decision support alerts

Kimberly J Newsom  1 , Bradley Hall  2 , Katherine A Martinez  1 , Scott Nelson  3 , Petr Starostik  1 , Khoa Nguyen  2
Affiliations

Integration of pharmacogenetic data in epic genomic module drives clinical decision support alerts

Kimberly J Newsom et al. Front Pharmacol. .

Abstract

Introduction: The Precision Medicine Program (PMP) at the University of Florida (UF) focuses on advancing pharmacogenomics (PGx) to improve patient care.

Methods: The UF PMP, in collaboration with the UF Health Pathology Laboratory (UFHPL), utilized Health Level Seven (HL7) standards to integrate PGx data into Epic's Genomic Module to enhance the management and utilization of PGx data in clinical practice.

Results: A key feature of the Genomic Module is the introduction of genomic indicators-innovative tools that flag actionable genetic information directly within the electronic health record (EHR). These indicators enable the effective presentation of phenotypic information and, when leveraged with existing clinical decision support (CDS) alerts, help provide timely and informed therapeutic decisions based on genomic data.

Discussion: This advancement represents a significant shift in the utilization of genetic data, moving beyond traditional PDF reports to provide a comprehensive understanding of PGx data. Ultimately, this integration empowers healthcare providers with genomics-guided recommendations, enhancing precision and personalization in patient care, contributing significantly to the advancement of personalized medicine.

Keywords: clinical decision support systems; electronic health records (EHR); genomics; health informatics; health level seven (HL7); pharmacogenetics; precision medicine.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
A Rummler-Brache diagram of the PGx workflow. Bolded items represent the new automated workflow utilizing the custom middleware solution. Items with dotted lines and hatched greyed out boxes represent the manual workflow. All other items are common to both workflows.
FIGURE 2
FIGURE 2
(A) Result entry screen in Beaker Clinical Pathology (CP) for Long Read Report (LRR) result types. (B) Result entry screen in Beaker Genomics Module for Variant (VAR) result types. (C) Table of Long Read Report (LRR) and Variant (VAR) component mapping.
FIGURE 3
FIGURE 3
HL7 Messaging for Genomic Data Transmission. (A) Excerpt of a Health Level 7 (HL7) message for CYP2D6, including both Variant (VAR) and Long Read Report (LRR) result components represented as Observation (OBX) and Notes (NTE) segments. The segments are organized into specific fields and field numbers, separated by the pipe (|) symbol, with numbered segments corresponding to those shown in (B) at the bottom. (B) Detailed overview of the Observation (OBX) and Notes (NTE) segments within the HL7 message, focusing on the components for LRR and VAR records. This panel also highlights the data source from the Python script used to generate the HL7 message.
FIGURE 4
FIGURE 4
PDF Output File from Data Generation. The PDF file serves as a preliminary review of data before its integration into the Electronic Health Record (EHR). It provides a comprehensive overview of genotype/phenotype information and individual sample calls.
FIGURE 5
FIGURE 5
Visualization of genomic data in the Electronic Health Record (EHR). (A) Results from the standard Clinical Pathology (CP) Long Read Report (LRR) database displayed in the Results Review tab. (B) Results from the Genomics Module (Variant (VAR)). (C) Link to genomic indicators from the Genomics Module results view.
FIGURE 6
FIGURE 6
Example clinical decision support (CDS) alert.
See this image and copyright information in PMC

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