Efficiency of CYP2C9 genetic test representation for automated pharmacogenetic decision support.

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DOI: 10.3414/ME0570


OBJECTIVES: We investigated the suitability of representing discrete genetic test results in the electronic health record (EHR) as individual single nucleotide polymorphisms (SNPs) and as alleles, using the CYP2C9 gene and its polymorphic states, as part of a pilot study. The purpose of our investigation was to determine the appropriate level of data abstraction when reporting genetic test results in the EHR that would allow meaningful interpretation and clinical decision support based on current knowledge, while retaining sufficient information in order to enable reinterpretation of the results in the context of future discoveries.

METHODS: Based on the SNP & allele models, we designed two separate lab panels within the laboratory information system, one containing SNPs and the other containing alleles, built separate rules in the clinical decision support system based on each model, and evaluated the performance of these rules in an EHR simulation environment using real-world scenarios.

RESULTS: Although decision-support rules based on allele model required significantly less computational time than rules based on SNP model, no difference was observed on the total time taken to chart medication orders between rules based on these two models.

CONCLUSIONS: Both, SNP- and allele-based models, can be used effectively for representing genetic test results in the EHR without impacting clinical decision support systems. While storing and reporting genetic test results as alleles allow for the construction of simpler decision-support rules, and make it easier to present these results to clinicians, SNP-based model can retain a greater amount of information that could be useful for future reinterpretation.

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Methods of information in medicine





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MeSH Keywords

Alleles; Aryl Hydrocarbon Hydroxylases; Cytochrome P-450 CYP2C9; Decision Support Systems; Clinical; Genetic Testing; Humans; Medical Records Systems; Computerized; Pharmacogenetics; Pilot Projects; Polymorphism; Single Nucleotide