Electronic Health Record (EHR) data is often used to solve complicated clinical problems (Safran, Bloomrosen & Hammond, 2007; Weiner, 2009; Hripcsak & Albers, 2012). While an incredibly informative data source, it is notoriously known to violate fundamental assumptions of data quality (Bae et al., 2014); incompleteness of records, inconsistency of records across time, and unexplained missingness. The use of EHR data for research purposes has also grown; and many important health-related questions have and will continue to be answered through the analysis of this data source (Blumenthal & Tavenner, 2010). While many efforts are currently being made to define and establish methods to reliably validate these types of data (Kahn et al., 2012; Weiskopf & Weng, 2013), accurate, standardized methodologies have yet to be identified. Weiskop (2014) developed a 3x3 data quality assessment (DQA) framework that includes the assessment of correctness, completeness, and currency data quality (DQ) constructs across three dimensions of data (patients, variables, and time). The current project aimed to build on Weiskopf’s (2014) 3x3 DQA framework by developing statistical definitions for each of the cells in the table using a dataset pulled from the CHCO database containing data collected during 2010-2014.