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

利用行为经济学和实施科学来推进基因组医学的使用 在多元化的卫生系统中利用 EHR 基础设施

• 批准号: 10518787
• 负责人: Katherine L. Nathanson
• 金额: $ 92.4万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-09 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10518787
• 关键词: Address; Adopted; Adverse effects; Age; Algorithms; Appointment; Architecture; Area; Caring; Clinic; Clinical; Clinical Trials; Clinical effectiveness; Communication; Communities; Coupled; Decision Making; Development; Diagnosis; Electronic Health Record; Environment; Fostering; Gender; Genetic; Genetic Models; Genomic medicine; Genomics; Health; Health system; Healthcare; Hereditary Disease; Hospitals; Human; Hybrids; Infrastructure; Ingestion; Inherited; Institution; Instruction; Integrated Health Care Systems; Knowledge; Libraries; Measures; Medical; Methods; Minority; Modeling; Outcome; Pathogenicity; Patients; Physicians; Probability; Protocols documentation; Provider; Race; Randomized; Randomized Clinical Trials; Role; Specialist; Structure; System; Test Result; Testing; Translations; Uncertainty; Variant; Work; algorithmic methodologies; base; behavioral economics; clinical care; clinical decision support; clinical encounter; cloud based; cost; design; effectiveness evaluation; effectiveness outcome; evidence base; genetic testing; health disparity; implementation barriers; implementation framework; implementation outcomes; implementation science; implementation strategy; implementation study; implementation trial; improved; innovation; medical specialties; novel; patient-level barriers; phenotyping algorithm; racial minority; structured data; system-level barriers; theories; time use; tool; treatment arm; web site

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.

基因检测结果改变医疗管理的医疗状况数量 患者呈指数级增长。然而，尽管有少数符合条件的患者接受了基因检测 对下游护理的影响。系统-（识别符合条件的患者并返回结果的方法），临床医生（ 例如，知识、有限的劳动力）和患者水平（例如，对成本和不利影响的担忧） 障碍助长了不确定性和依赖现状的倾向——未能使用基因组信息来指导 医疗保健。实施科学方法和框架是解决这一实践差距的理想选择， 尤其是那些考虑多层次障碍以及人类决策在以下情况下的作用的人： 不确定。我们的团队已经建立了基础设施来解决提供基因检测的系统障碍 整个卫生系统 - 电子健康记录 (EHR) 内的一个集成系统，可实现直接 作为结构化数据的基因测试的排序和结果 - 现在有多个传播请求。我们的 团队还使用行为经济学作为实施科学框架来改善医疗保健 同时使用推动（EHR 默认值、患者启动）来克服临床医生和患者的障碍 解决健康差异（例如，少数族裔之间较高的实践差距）。合并这些区域，我们 提出一个高度创新的项目，该项目将首次评估临床医生推动的使用（EHR 默认为：1) 转诊遗传学诊所或 2) 订购基因检测）和/或向患者推送 （在预约前向主要患者传达基因检测的好处）。在目标 1 中，我们将 开发针对 10 种临床病症的电子表型分析算法，这将推动诊断特异性遗传学的发展 转诊和测试；我们将与利益相关者咨询委员会合作完善我们的推动措施。在目标 2 中，我们将 使用整群随机设计，对 228 名临床医生进行混合 3 型实施研究 （医生、高级执业医师）作为随机化单位（N = 120 组）和 16,500 名患者 使用 10 个条件之一来检查对基因检测率的影响：患者启动推动， 两个临床医生的推动，结合患者和每个临床医生的推动，与一般的最佳实践相比 警报 (BPA)（无临床医生或患者推动）。我们将检查患者（例如种族）、临床医生（例如专业）和 系统（例如，社区与学术中心）对基因检测率和评估的推动作用的调节者 有效性结果（确定致病变异后临床医生的行动率）。在目标 3 中，我们 将采用系统方法来传播我们的 EHR 整合基因检测、基于 EHR 的技术 通过 Epic、PheKB、NHGRl 的 AnVIL 为临床试验构建的算法以及其他材料和系统， 和 GitHub。我们的研究将产生巨大影响，因为它将产生一种新颖、有效且可转移的基于 EHR 的方法 能够可持续提供基因组医学的基础设施，极大地推进了该领域的发展。