PROJECT SUMMARY Older adults with multiple chronic conditions (MCCs) are often hospitalized and are at risk for adverse events (AE) during acute episodes of care. Fragmented care across different healthcare providers is common for these patients and increases risk of AEs, especially in elderly patients with unrecognized geriatric conditions. Patient safety research that attempts to address these risks are limited by available data and often lack of access to electronic health records (EHR) from providers external to the health system conducting the research. We propose leveraging emerging interoperability standards, and public policies that require their adoption, to empower patients to locate, retrieve, and share their EHRs with our research team. We will partner with two studies that will enroll older adults and aim to reduce AEs during acute care episodes. Both studies have timelines suitable to consenting participants for this demonstration study. To accomplish our aims, we will leverage and enhance our existing digital infrastructure (a web-based application and secure backend cloud technology) that we have developed and implemented in past and ongoing work. Leadership of both partner studies will play major roles in this work, ensuring strong coordination. In Aim 1, we will enhance our existing digital infrastructure using two open-source projects. We will leverage infrastructure from the Sync-for-Science (S4S) Procure project (used in the All Of Us Research Program) enabling patients to use HL7 FHIR Services to find and share their EHRs with research teams. The MCC e-Care Plan project will supply clinical information models and value sets, ensuring that data collected can be used for both research and clinical care. User needs and requirements for identifying prior sites of care and sharing EHR data from those sites with the research team will be elicited via a rigorous user-centered design process. In Aim 2, we will implement and iteratively refine workflows defined in Aim 1 using mixed methods. In Aim 3 we will develop analytic methods for harmonizing aggregated EHR data and metrics relevant to our partner studies. These metrics will reflect care fragmentation based on EHR data aggregated using FHIR services, including unrecognized geriatric syndromes identified by applying natural language processing to unstructured text in retrieved clinical notes. In Aim 4, we will use these metrics as risk factors in a multivariable regression model to assess their effect on the safety outcomes of our partner studies. This analysis will demonstrate how novel assessments of care fragmentation and conditions common in geriatric populations contribute to AEs during acute care episodes. Our expert advisors, consultants, and software developers will assist with all clinical and technology aspects of this work. Our demonstration study will produce foundational knowledge regarding how to empower patients to collect and share their data with research teams, ...