Abstract Data-driven evaluation and interventions have improved emergency care across an array of acute care conditions including acute myocardial infarction, stroke and sepsis -- yet, similar systems to support and advance emergency care for OUD are lacking amidst worsening patient outcomes. Our team completed foundational data infrastructure work revealing gaps in ED data systems as well as identifying opportunities to utilize the American College of Emergency Physician's Clinical Emergency Department Registry (CEDR) as a national data backbone of over 1000 Emergency Departments (EDs) for OUD care. We propose to create new data processes that will generate a collection of data products embedded within the CEDR ecosystem to improve the timeliness, quality, accessibility, and usefulness of CEDR data to address the overdose epidemic. To address these critical needs, we propose an acceleration project that will leverage our prior work developing and refining electronic health record OUD data elements both within and outside of the CEDR registry. This proposal will: 1) automate OUD-related data extraction from participating sites, 2) map ED data to a standardized, scalable Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) 3) Translate relational data within the CDM into research ready datasets with algorithmic extension of important OUD concepts, and 4) create site and public facing ED OUD overdose prevention dashboards for care improvement and surveillance. We hypothesize that the automation and integration of electronic health record and administrative substance use disorder data into CEDR will improve the ability to identify opportunities for improvement in ED OUD care and advance future research initiatives. During this study we plan to assess digital readiness within CEDR and design an enhanced ED OUD data infrastructure suitable for large scale observational research, support of future real-world clinical trials and benchmarking dashboards (R61 phase) and develop and deploy an ED OUD digital infrastructure across clinical, research and surveillance dimensions (R33 phase). Upon study completion, we anticipate an improved national ED data registry using a standardized common data model, automated site level ED OUD dashboards to guide near-real time local quality improvement initiatives, the existence of an independent ED OUD Registry Research dataset, and a public facing, web based near-real time ED OUD care dashboard.