The Geisinger MyCode community health initiative: an electronic health record-linked biobank for precision medicine research.
The Geisinger MyCode community health initiative: an electronic health record-linked biobank for precision medicine research.
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DOI：
10.1038/gim.2015.187
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发表时间：
2016-09
期刊：
影响因子：
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通讯作者：
Ledbetter DH
中科院分区：
文献类型：
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作者：
Carey DJ;Fetterolf SN;Davis FD;Faucett WA;Kirchner HL;Mirshahi U;Murray MF;Smelser DT;Gerhard GS;Ledbetter DH
盖辛格健康系统(GHS)为精密医疗提供了一个理想的平台。关键要素是综合卫生系统、稳定的患者人口和电子健康记录(EHR)基础设施。2007年，盖辛格推出了MyCode®，这是一个系统范围的生物库计划，将样本和EHR数据链接起来，用于广泛的研究用途。使用参与者焦点小组获得MyCode®中以患者为中心的意见。参与MyCode®是基于选择加入的知情同意，并允许重新联系，这有助于收集不在EHR中的数据，自2013年以来，将临床可操作的结果返回给参与者。MyCode®利用盖辛格的技术和临床基础设施进行参与者跟踪和样本采集。MyCode®的同意率为85%，目前有90,000多名参与者，每月有约4,000人注册。MyCode®样本已用于生成分子数据，包括高密度基因和外显子组序列数据。基因型和EHR衍生的表型数据重复了先前报道的遗传关联。MyCode®项目创造了资源，使转化性研究的新模式比传统临床研究方法更快、更灵活、更具成本效益。新的模式是可扩展的，随着这些资源的增长并在多个研究平台上采用，新模式的价值将会增加。
Geisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007 Geisinger launched MyCode®, a system-wide biobanking program to link samples and EHR data for broad research use. Patient-centered input into MyCode® was obtained using participant focus groups. Participation in MyCode® is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR, and, since 2013, the return of clinically actionable results to participants. MyCode® leverages Geisinger’s technology and clinical infrastructure for participant tracking and sample collection. MyCode® has a consent rate of >85% with more than 90,000 participants currently, with ongoing enrollment of ~4,000 per month. MyCode® samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations. The MyCode® project has created resources that enable a new model for translational research that is faster, more flexible, and more cost effective than traditional clinical research approaches. The new model is scalable, and will increase in value as these resources grow and are adopted across multiple research platforms.