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.
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.
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