Orlando Lori A, Henrich Vincent C, Hauser Elizabeth R, Wilson Charles, Ginsburg Geoffrey S
Duke Center for Personalized Medicine & Duke Institute for Genome Sciences & Policy, 3475 Erwin Road, Wallace Clinic Ste 204, Durham, NC 27705, USA.
Center for Biotechnology, Genomics & Health Research, University of North Carolina-Greensboro, 3701 MHRA Building, Greensboro, NC 27402, USA.
Per Med. 2013 May;10(3):295-306. doi: 10.2217/pme.13.20.
As an essential tool for risk stratification, family health history (FHH) is a central component of personalized medicine; yet, despite its widespread acceptance among professional societies and its established place in the medical interview, its widespread adoption is hindered by three major barriers: quality of FHH collection, risk stratification capabilities and interpretation of risk stratification for clinical care. To overcome these barriers and bring FHH to the forefront of the personalized medicine effort, we developed the genomic medicine model (GMM) for primary care. The GMM, founded upon the principles of the Health Belief Model, Adult Learning Theory and the implementation sciences, shifts responsibility for FHH onto the patient, uses information technology (MeTree) for risk stratification and interpretation, and provides education across multiple levels for each stakeholder, freeing up the clinical encounter for discussion around personalized preventive healthcare plans. The GMM has been implemented and optimized as part of an implementation-effectiveness hybrid pilot study for breast/ovarian cancer, colon cancer and thrombosis, and risk for hereditary cancer syndromes in two primary care clinics in NC, USA. This paper describes the conceptual development of the model and key findings relevant for broader uptake and sustainability in the primary care community.
作为风险分层的重要工具,家族健康史(FHH)是个性化医疗的核心组成部分;然而,尽管它在专业学会中得到广泛认可,并且在医学问诊中占据既定地位,但其广泛应用受到三大障碍的阻碍:FHH收集质量、风险分层能力以及临床护理中风险分层的解读。为了克服这些障碍并使FHH在个性化医疗努力中处于前沿位置,我们为初级保健开发了基因组医学模型(GMM)。GMM基于健康信念模型、成人学习理论和实施科学的原则,将FHH的责任转移到患者身上,使用信息技术(MeTree)进行风险分层和解读,并为每个利益相关者提供多层次教育,从而腾出临床问诊时间用于讨论个性化预防保健计划。GMM已作为乳腺癌/卵巢癌、结肠癌和血栓形成以及美国北卡罗来纳州两家初级保健诊所遗传性癌症综合征风险的实施-有效性混合试点研究的一部分得以实施和优化。本文描述了该模型的概念发展以及与初级保健社区更广泛采用和可持续性相关的关键发现。