Glycosylated Hemoglobin A1C (A1C) is the widely established standard diabetes management quality glucose metric due its association with diabetes related complications, ease of measurement, and acceptance by Food and Drug Administration (FDA) to measure glucose lowering efficacy. The use of Continuous Glucose Monitoring (CGM) is increasing worldwide and has become a standard of care for the management of type 1 diabetes and insulin treated individuals with type 2 diabetes. The expanded use of CGM provides an opportunity to modernize diabetes quality measures beyond A1C by leveraging CGM metrics for assessment of glycemic status, diabetes quality improvement, and population health tracking. This session will be broken into 3 sections. Section 1 will examine the limitations of using A1C as a diabetes quality metric including its inability to accurately represent glucose variability along with factors that can impact its accuracy. It will provide a brief review of CGM devices, metrics, and reports, including the Ambulatory Glucose Profile Report that is the recommended way to organize CGM data in a clinically useful way. Section 2 will describe how CGM metrics can be a modern adjunct to A1C-based quality metrics. This section will highlight the advantages and disadvantages of CGM-based diabetes quality metrics such as the glucose management indicator (GMI), CGM-derived average glucose, time in range, time above range, and time below range. It will outline how NCQA is integrating GMI as a glycemic status measure indicator in their Healthcare Effectiveness Data and Information Set and Diabetes Recognition Program. In addition, options for developing a CGM process measure by reporting the percentage use of this technology in individuals with diabetes at highest risk (e.g. those with type 1 diabetes and insulin treated type 2 diabetes) will be discussed. Section 3 will focus on the importance of the direct integration of CGM metrics into the electronic health record (EHR). This section will highlight International Diabetes Center, Health Partners Institute (IDC) work with CGM manufactures to seamlessly integrate CGM data into HealthPartners Epic EHR. It will highlight the benefits and tips for successful integration of CGM data into the EHR. Section 3 will end with a brief overview of findings from a quality improvement project funded by the Helmsley Charitable Trust bringing together IDC, NCQA and Minnesota Community Measurement (MNCM) to pilot transfer of CGM quality measures directly from the EHR into the MNCM data warehouse for diabetes population health tracking. Data from our analysis of correlation of CGM metric data (e.g., GMI, CGM derived average glucose and time in range) to lab A1C to establish benchmark equivalencies and clinical implications will be presented. The session will end with a short question and answer period.