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

Precision medicine aims to accurately classify patients to improve diagnosis, intervention selection, and prognosis. The All of Us Research Program (AoURP) collects an 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 participant’s life remain missing 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" and more deeply assess health outcomes. 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 environmental and economic data. 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. We will also explore new methods of clinical data acquisition to mitigate data missingness by comparing data provided from recruitment sites with EHR data from Health Information Networks. CLAD data sources and novel analytical methods, such as probabilistic models, will be used to reveal patterns of care, health outcomes, and potential interventions for common, chronic, and genetic diseases.