Electronic health record design and implementation for pharmacogenomics: a local perspective

Abstract

Purpose: The design of electronic health records to translate genomic medicine into clinical care is crucial to successful introduction of new genomic services, yet there are few published guides to implementation.

Methods: The design, implemented features, and evolution of a locally developed electronic health record that supports a large pharmacogenomics program at a tertiary-care academic medical center was tracked over a 4-year development period.

Results: Developers and program staff created electronic health record mechanisms for ordering a pharmacogenomics panel in advance of clinical need (preemptive genotyping) and in response to a specific drug indication. Genetic data from panel-based genotyping were sequestered from the electronic health record until drug-gene interactions met evidentiary standards and deemed clinically actionable. A service to translate genotype to predicted drug-response phenotype populated a summary of drug-gene interactions, triggered inpatient and outpatient clinical decision support, updated laboratory records, and created gene results within online personal health records.

Conclusion: The design of a locally developed electronic health record supporting pharmacogenomics has generalizable utility. The challenge of representing genomic data in a comprehensible and clinically actionable format is discussed along with reflection on the scalability of the model to larger sets of genomic data.

Figure 1. PREDICT EHR Development Timeline

PREDICT has undergone a 4-year process of design, implementation, and iterative refinement. Several milestones, including new drug genome interaction implementation as well as high-impact EHR design features, are highlighted. (DGI, drug genome interaction; CDS, Clinical Decision Support; EHR, electronic health record; PHR, personal health record)

Figure 2. EHR Development and Operational Processes

Pharmacogenomics implementation requires pre-implementation research and assessment, technical development of informatics infrastructure, and integration with laboratory and clinical operations. Accessibility to users, both patient and provider, is integral. (PGx, pharmacogenomics; P&T, Pharmacy and Therapeutics; Rx, prescription; CDS, Clinical Decision Support; EHR, electronic health record; PHR, personal health record)

Figure 3. Task-specific views of genomic results present in the EHR

The Patient Summary, which serves as the front page of each patient’s record, includes a Drug Genome Interaction section detailing the patient’s genotype in star allele nomenclature as well as phenotype and implcations for prescribing (Panel A). Genomic results and phenotypes are also available in the Lab Results section of the EHR (Panel B). When a drug is ordered for a patient with an actionable genotype, Clinical Decision Support (CDS), such as the representative Outpatient Substitution Advisor, is presented to the ordering clinician (Panel C). Similarly, parallel mechanisms offer CDS in the inpatient setting (Panel D).