Using Behavioral Economics and Implementation Science to Advance the Use of Genomic Medicine Utilizing an EHR Infrastructure across a Diverse Health System

因基因检测结果而改变医疗管理的疾病数量 患者呈指数级增长。然而，少数符合条件的患者接受基因检测，尽管 对下游护理的影响。系统-（识别合格患者并返回结果的方法）、临床医生（ 例如，在一个实施例中，知识，有限的劳动力）和患者-（例如，对成本和不良反应的担忧）水平 障碍助长了不确定性和依赖现状的倾向--未能利用基因组信息来指导 医疗等实施科学方法和框架是解决这一实践差距的理想选择， 特别是那些考虑到多层次障碍和人类决策的作用， 不确定性我们的团队已经建立了基础设施，以解决提供基因检测的系统障碍 电子健康记录（EHR）中的集成系统，可以直接 基因检测的排序和结果作为结构化数据-现在有多个传播请求。我们 该团队还使用行为经济学作为实施科学框架，通过以下方式改善医疗保健： 使用轻推（EHR默认值、患者预充）同时克服临床医生和患者障碍 解决健康差距（例如，少数民族之间的差距更大）。合并这些区域，我们 我提出了一个高度创新的项目，该项目将首次评估对临床医生的推动（EHR 默认值：1）参考遗传学诊所或2）订购基因检测）和/或轻推患者 （在预约前与主要患者沟通基因检测的好处）。在目标1中，我们 为10种临床疾病开发电子表型算法，这将推动诊断特异性遗传学 转诊和检测;我们将与利益相关者咨询理事会合作，完善我们的推动措施。在目标2中，我们 采用228名临床医生的群集随机设计，进行混合型3实施研究 （医生，高级执业医师）作为随机化单位（N= 120个群集）和16，500例患者 用10个条件之一来检查对基因检测率的影响：患者启动轻推， 两个临床医生轻推，结合患者和每个临床医生轻推，与一般的最佳实践 警报（BPA）（无临床医生或患者轻推）。我们将检查患者（例如，种族），临床医生（例如，专业），以及 系统（例如，社区与学术中心）对基因检测率和评估的推动效应的主持人 有效性结果（鉴定致病性变体后临床医生采取行动的比率）。在目标3中， 将从事系统的方法来传播我们的EHR整合基因检测，EHR为基础的 算法，以及通过Epic、PheKB、NHGRl的AnVIL为临床试验构建的其他材料和系统， 和GitHub。我们的研究将是非常有影响力的，因为它将产生一个新的，有效的，和可转移的EHR为基础的 基础设施，使基因组药物的可持续提供，大大推进该领域。

The number of medical conditions for which the results of genetic testing change the medical management of patients is exponentially increasing. However, a minority of eligible patients receive genetic testing, despite the implications for downstream care. System- (methods to identify eligible patients and return results), clinician( e.g., knowledge, limited workforce), and patient- (e.g. , concerns about costs and adverse effects) level barriers foster uncertainty and a tendency to rely on the status quo - failing to use genomic information to guide medical care. Implementation science methods and frameworks are ideal for addressing this practice gap, especially those that consider multi-level barriers and the role of human decision-making in contexts with uncertainty. Our team has built the infrastructure to address system-barriers to delivering genetic testing across our health system - an integrated system within the electronic health record (EHR) that enables direct ordering and resulting of genetic tests as structured data - now with multiple requests for dissemination. Our team also is using behavioral economics as an implementation science framework to improve healthcare by using nudges (EHR defaults, patient priming) to overcome clinician and patient barriers, concurrently addressing health disparities (e.g., higher practice gaps among racial minorities). Merging these areas, we propose a highly innovative project that will evaluate, for the first time, the use of nudges to clinicians (EHR defaults for either: 1) referring to genetics clinic or 2) ordering for genetic testing) and/or nudges to patients (communication to prime patients about the benefits of genetic testing prior to appointment). In Aim 1, we will develop electronic phenotyping algorithms for 10 clinical conditions, which will drive diagnosis-specific genetics referral and testing; we will refine our nudges working with a Stakeholder Advisory Council. In Aim 2, we will conduct a hybrid type 3 implementation study, using a cluster randomized design with 228 clinicians (physician, Advanced Practice Practitioners) as the unit of randomization (N= 120 clusters) and 16,500 patients with one of the 10 conditions to examine the impact on the rate of genetic testing of: the patient priming nudge, the two clinician nudges, combining the patient and each of the clinician nudges, vs. a generic best practice alert (BPA) (no clinician or patient nudge). We will examine patient (e.g., race), clinician (e.g., specialty), and system (e.g. , community vs. academic center) moderators of nudge effects on genetic testing rate and assess an effectiveness outcome (rate of clinician action following identification of a pathogenic variant). In Aim 3, we will engage in systematic methods to disseminate our EHR integration of genetic testing, EHR-based algorithms, and other materials and systems built for the clinical trial through Epic, PheKB, NHGRl's AnVIL, and GitHub. Our study will be immensely impactful, as it will yield a novel, effective, and transferrable EHRbased infrastructure that enables the sustainable delivery of genomic medicine, greatly advancing the field.