IF 1.8 Q3 MEDICAL LABORATORY TECHNOLOGY
Claire E Knezevic, James M Stevenson, Jonathan Merran, Isabel Snyder, Grant Restorick, Christopher Waters, Mark A Marzinke
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引用次数: 0

摘要

背景:药物基因组学已证明可为临床治疗带来益处,包括减少不良事件和节约成本。然而,扩大药物基因组学检测实施范围的障碍包括周转时间过长,以及将结果整合到电子病历中以提供临床决策支持。我们开发并实施了一套临床工作流程,以方便一家大型学术医疗中心在内部生成结果并将其纳入电子病历:方法:使用 QuantStudio 12K Flex 平台开发并验证了 11 个基因的可操作药物基因组学面板。等位基因结果被导出到一个定制驱动程序和规则引擎中,结果信息(包括二联型和预测代谢表型)被发送到电子病历中;电子咨询(eConsult)服务被整合到工作流程中。实施后进行了监测,以评估可操作结果的频率和周转时间:结果:可操作的药物基因组学面板涵盖了 11 个基因中的 39 个等位基因。代谢表型与基因二型同时得出,面向临床医生的表型摘要(基因组指标)也显示在电子病历中。实施后,8 个临床领域使用了药物基因组学检测,其中 56% 的订单发生在门诊环境中;22.1% 的请求包含至少一个可操作的药理基因,67% 的订单与结果前或结果后的电子咨询有关。从样本采集到得出结果的平均周转时间为 4.6 天:药物基因组学流水线已在一家四级学术医疗中心成功运行,并将结果直接整合到电子病历、临床决策支持和电子会诊服务中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of Integrated Clinical Pharmacogenomics Testing at an Academic Medical Center.

Background: Pharmacogenomics has demonstrated benefits for clinical care, including a reduction in adverse events and cost savings. However, barriers in expanded implementation of pharmacogenomics testing include prolonged turnaround times and integration of results into the electronic health record with clinical decision support. A clinical workflow was developed and implemented to facilitate in-house result generation and incorporation into the electronic health record at a large academic medical center.

Methods: An 11-gene actionable pharmacogenomics panel was developed and validated using a QuantStudio 12K Flex platform. Allelic results were exported to a custom driver and rules engine, and result messages, which included a diplotype and predicted metabolic phenotype, were sent to the electronic health record; an electronic consultation (eConsult) service was integrated into the workflow. Postimplementation monitoring was performed to evaluate the frequency of actionable results and turnaround times.

Results: The actionable pharmacogenomics panel covered 39 alleles across 11 genes. Metabolic phenotypes were resulted alongside gene diplotypes, and clinician-facing phenotype summaries (Genomic Indicators) were presented in the electronic health record. Postimplementation, 8 clinical areas have utilized pharmacogenomics testing, with 56% of orders occurring in the outpatient setting; 22.1% of requests included at least one actionable pharmacogene, and 67% of orders were associated with a pre- or postresult electronic consultation. Mean turnaround time from sample collection to result was 4.6 days.

Conclusions: A pharmacogenomics pipeline was successfully operationalized at a quaternary academic medical center, with direct integration of results into the electronic health record, clinical decision support, and eConsult services.

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来源期刊
Journal of Applied Laboratory Medicine
Journal of Applied Laboratory Medicine MEDICAL LABORATORY TECHNOLOGY-
CiteScore
3.70
自引率
5.00%
发文量
137
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