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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Decision Support Systems, Clinical, Electronic Health Records, Exome, Genetic Association Studies, Genetic Variation, Genetics, Medical, High-Throughput Nucleotide Sequencing, Humans, Incidental Findings, Medical Order Entry Systems, Pharmacogenetics.
user
Does development of clinical decision support alert for pharmacogenomic incidental findings from exome sequencing? Context: Electronic health records (EHRs) and their associated decision support tools are potentially important means of disseminating a patient's pharmacogenomic profile to his or her health-care providers. We sought to create a proof-of-concept decision support alert system generated from pharmacogenomic incidental findings from exome sequencing. A pipeline for alerts from exome sequencing tests was created for patients in the New EXome Technology in (NEXT) Medicine study at the University of Washington. Decision support rules using discrete, machine-readable incidental finding results were programmed into a commercial EHR rules engine. An evaluation plan to monitor the alerts in real medical interactions was established. Alerts were created for 48 actionable pharmacogenomic variants in 11 genes and were launched on 24 September 2014 for University of Washington inpatient care. Of the 94 participants enrolled in the NEXT Medicine study, 49 had one or more pharmacogenomic variants identified for return.
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Reflections on the process reveal that while incidental findings can be used to generate decision support alerts, substantial resources are required to ensure that each alert is consistent with rapidly evolving pharmacogenomic literature and is customized to fit in the clinical workflow unique to each incidental finding. Final Decision: yes