WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCES

项目摘要 该试点项目建议实施和评估一个可扩展的数字化患者参与战略， 招募参与者加入我们所有人计划（AoU），并利用大量的电子健康记录， (EHR)华盛顿大学和BJC医疗保健（WU-BJC）提供的精确医学信息 research.为了加速复杂的基因发现和翻译，我们认为利用现有的 快速有效地收集数据，并通过数字机制吸引研究参与者。我们的项目提供了 原型数字精准医学研究范式，以提高公众对“公民科学”的兴趣， 参与AoU。通过整合数字招募机制和基于EHR的表型分析，我们可以 以具有成本效益和及时的方式快速吸引，招募和表型新的AoU参与者。 我们的方法是可推广的，适应性强的，最终允许将基因组数据与EHR联系起来- 衍生表型，以加速生物医学研究。我们将在WU-BJC系统中测试这种方法， 密苏里州最大的患者护理提供商（每年超过200万人）。我们将为AoU招募活跃的患者， 支持并使他们同意，并提取和提交他们的EHR衍生表型数据给AoU。 WU-BJC使用通用的云托管EHR（Epic），确保临床数据可用于研究目的 通过提取和协调过程，填充OMOP CDM 5.2数据存储库（研究数据 核心）。通过我们共享的EHR招募患者（特别是通过集成的患者门户网站MyChart）， 将快速有效地创建一个具有相关和良好填充的临床数据集的队列，所有这些都具有最小的 成本和参与者负担。这种前瞻性的“直接面向参与者”模式也可以实施， 与“传统”面对面或预先安排相比，增加新的在线评估和快速数据更新 接近。根据我们的初步研究，我们预计大多数符合条件的个人将同意 参与我们的研究，未来使用他们的数据，以及未来的联系。这种方法将使研究人员 快速有效地针对新出现的健康趋势和研究需求。 我们将：（1）建立一个数字化的、以社区为中心的患者参与、招募和同意战略， 针对代表性不足的少数民族，农村和医疗服务不足的人口， WU-BJC服务的领域;（2）实施表型分析管道，以提取、协调和提交临床数据 评估和优化策略，以招募具有代表性的参与者样本。 我们将为AoU实施数字化、直接面向参与者的参与、招募和同意策略 参与的我们将证明快速和廉价地创建可计算参与者的可行性 表型，从我们的EHR平台提取。我们将评估和证明这种方法的价值， 确定在类似环境中优化和进一步实施的机会。我们的协议和工具 将遵循FAIR（可查找性，可访问性，互操作性和可重用性）原则。

Project Summary This pilot project proposes to implement and evaluate a scalable digital patient engagement strategy intended to recruit participants to the All of Us program (AoU) and harness the vast amount of electronic health record (EHR) information available at Washington University and BJC Healthcare (WU-BJC) for precision-medicine research. To accelerate complex genetic discovery and translation, we believe it is critical to harness existing data quickly and efficiently and to engage research participants via digital mechanisms. Our project provides a prototype digital precision medicine research paradigm to increase public interest in “citizen science” and participation in AoU. By integrating digital recruitment mechanisms and EHR-based phenotyping, we can rapidly engage, recruit, and phenotype new AoU participants in a cost-effective and timely manner. Our approach is generalizable, adaptable, and ultimately allows for the linkage of genomic data with EHR- derived phenotypes to accelerate biomedical research. We will test this approach in the WU-BJC system, Missouri's largest patient care provider (> 2 million individuals annually). We will recruit active patients for AoU, support and enable their consent, and extract and submit their EHR-derived phenotyping data to AoU. WU-BJC uses a common, cloud-hosted EHR (Epic), with ensuing clinical data available for research purposes via extraction and harmonization processes that populate an OMOP CDM 5.2 data repository (Research Data Core). By recruiting patients via our shared EHR (specifically, via the integrated patient portal, MyChart), we will quickly and efficiently create a cohort with associated and well populated clinical data sets, all with minimal costs and participant burden. This forward-thinking “direct-to-participant” model can also be implemented to add new online assessments and rapid data updates compared to “traditional” in-person or pre-scheduled approaches. Based on our preliminary studies, we anticipate a majority of eligible individuals will agree to participate in our study, to future use of their data, and to future contact. This approach will enable researchers to effectively target emerging health trends and research needs quickly and efficiently. We will: (1) establish a digital, community-focused patient engagement, recruitment, and consent strategy, targeting a combination of under-represented minority, rural, and medically underserved populations in the areas served by WU-BJC; (2) implement a phenotyping pipeline to extract, harmonize, and submit clinical data to AoU; and (3) evaluate and optimize strategies to recruit a representative sample of participants. We will implement a digital, direct-to-participant engagement, recruitment, and consent strategy for AoU participation. We will demonstrate the feasibility of rapidly and inexpensively creating computable participant phenotypes, extracted from our EHR platforms. We will evaluate and demonstrate the value of this approach to identify opportunities for optimization and further implementation in analogous settings. Our protocols and tools will be made available, adhering to FAIR (Findability, Accessibility, Interoperability, & Reusability) principles.