Use of the electronic health record (EHR) has become a routine part of perioperative care in the United States. Secondary use of EHR data includes research, quality, and educational initiatives. Fundamental to secondary use is a framework to ensure fidelity, transparency, and completeness of the source data. In developing this framework, competing priorities must be considered as to which data sources are used and how data are organized and incorporated into a useable format. In assembling perioperative data from diverse institutions across the United States and Europe, the Multicenter Perioperative Outcomes Group (MPOG) has developed methods to support such a framework. This special article outlines how MPOG has approached considerations of data structure, validation, and accessibility to support multicenter integration of perioperative EHRs. In this multicenter practice registry, MPOG has developed processes to extract data from the perioperative EHR; transform data into a standardized format; and validate, deidentify, and transfer data to a secure central Coordinating Center database. Participating institutions may obtain access to this central database, governed by quality and research committees, to inform clinical practice and contribute to the scientific and clinical communities. Through a rigorous and standardized approach to ensure data integrity, MPOG enables data to be usable for quality improvement and advancing scientific knowledge. As of March 2019, our collaboration of 46 hospitals has accrued 10.7 million anesthesia records with associated perioperative EHR data across heterogeneous vendors. Facilitated by MPOG, each site retains access to a local repository containing all site-specific perioperative data, distinct from source EHRs and readily available for local research, quality, and educational initiatives. Through committee approval processes, investigators at participating sites may additionally access multicenter data for similar initiatives. Emerging from this work are 4 considerations that our group has prioritized to improve data quality: (1) data should be available at the local level before Coordinating Center transfer; (2) data should be rigorously validated against standardized metrics before use; (3) data should be curated into computable phenotypes that are easily accessible; and (4) data should be collected for both research and quality improvement purposes because these complementary goals bolster the strength of each endeavor.