Iron-CLAD: securely advancing AoU participant characterization with provenplatforms and collaborations

摘要 精准医学旨在对患者进行准确分类，提高诊断、干预水平 选择和预后。我们所有人研究计划(AoURP)收集了一系列不同的 来自参与者的数据类型，包括调查、电子健康记录、体检 测量、可穿戴设备和生物样本，提供对健康的有价值的见解 轨迹。然而，患者生活的某些方面在收集的 数据，这可能会限制研究和护理的准确性。为了解决这一差距，我们建议 建立我们所有人联系和获取数据中心(CLAD)，以补充 使用被动数据流的现有数据源，并部署集成策略以“将 这个团队汇集了领导大型企业的集体经验 涉及数据获取、联系、协调、质量保证、管道和 平台、治理和安全。 我们将设计和实施数据收集、链接和集成战略，为 为已识别和未识别数据的各种AoURP数据链接奠定基础 融合，包括个人层面的联系，如与死亡率、居住史和 管理声明和地理编码数据管道，以实现与环境的联系 正义指数。CLAD将获取和处理新的数据链接和地理编码数据 基于云的数据链接平台(DLP)，以我们的经验为指导制定 为研究人员准备的具有科学效用的数据集。我们的团队将提供高质量的数据 保证、修复和标准化检查，以确保数据驱动的准确性和健壮性 研究。这一努力将使数据与互操作性标准和临床术语保持一致， 根据需要对其进行扩展，并为每个数据更改和数据创建数据质量控制面板 每个源和站点的运行状况检查数据质量报告。我们还将探索新的 从HINS获取临床数据以减少数据丢失的方法，重点是 通过比较AoURP参与者关联的动态EHR数据来确定代表性不足的人群 来自OCHIN，其中包括Medicaid和未参保的患者，以及来自Health的EHR数据 由Datavant提供服务的系统。不同的包层来源和新颖的分析方法，如 概率模型将被用来揭示护理模式和潜在的干预措施 在生物医学研究中代表性不足的社区。

ABSTRACT Precision medicine aims to accurately classify patients to improve diagnosis, intervention selection, and prognosis. The All of Us Research Program (AoURP) collects a diverse array of data types from participants, including surveys, electronic health records (EHRs), physical measurements, wearable devices, and biosamples, offering valuable insights into health trajectories. However, certain aspects of a patient’s life remain unrepresented in the collected data, which can limit the accuracy of research and care. To address this gap, we propose the creation of the All of Us Center for Linkage and Acquisition of Data (CLAD) to supplement existing data sources using passive data streams and deploy integration strategies to "put the patient back together again." This team brings together collective experience leading large initiatives involving data acquisition, linkage, harmonization, quality assurance, pipelines and platforms, governance, and security. We will design and implement a data collection, linkage, and integration strategy that lays a foundation for a variety of AoURP data linkages for identified, and de-identified data integration, including person-level linkages such as with mortality, residential history, and administrative claims, and geocoded data pipelines to enable linkages with the Environmental Justice Index. The CLAD will acquire and process new data linkages and geocoded data in a cloud-based Data Linkage Platform (DLP), guided by our experience formulating researcher-ready datasets with scientific utility. Our CLAD team will perform data quality assurance, repair, and standardization checks to ensure accuracy and robustness of data-driven research. This endeavor will align data with interoperability standards and clinical terminologies, extend them where necessary, and create a data quality dashboard for every data change and data health check Data Quality reports for each of the sources and sites. We will also explore new methods of clinical data acquisition from HINs to mitigate data missingness with a focus on underrepresented populations by comparing AoURP participant-linked ambulatory EHR data from OCHIN, which includes Medicaid and uninsured patients, with EHR data from health systems served by Datavant. Diverse CLAD sources and novel analytical methods, such as probabilistic models, will be used to reveal patterns of care and potential interventions for communities underrepresented in biomedical research.