12420 Effects Of A Continuous Glucose Monitoring (CGM) Optimization Program On Prescribing Behavior And Glycemic Outcomes
Authors: Sterbenz A , Rosales J Fuentes , Abraham S , Siroya P , Middelbeek R , Mitri J , Rossitto F , Cabana E , Gharse A , Collins L , Aliaga L , Ferro A Millan , Cifuni J , Schwartz A , Kelley E , Gomez C , Aballay M 
Published: October 2024

Disclosure: A. Sterbenz: None. J. Fuentes Rosales: None. S. Abraham: None. P. Siroya: None. R. Middelbeek: None. J. Mitri: None. F. Rossitto: None. E. Cabana: None. A. Gharse: None. L. Collins: None. L. Aliaga: None. A. Millan Ferro: None. J. Cifuni: None. A. Schwartz: None. E. Kelley: None. C. Gomez: None. M. Aballay: None.

Introduction: Continuous glucose monitoring (CGM) improves glucose control in patients with diabetes, as demonstrated in clinical trials. There is limited information available on the real-world application of CGM adoption in a resource-limited setting or safety net outpatient hospital setting. This intervention's aim was to assess the impact of a multifaceted approach on improving CGM utilization and on glycemic control among patients with diabetes. Methods: Wyckoff Heights Medical Center, located in Brooklyn, NY is a resource-limited setting that in partnership with the Joslin Diabetes Center, Boston, MA launched a CGM Clinical Care Optimization Program. This quality improvement (QI) initiative included establishing a diabetes technology team (DTT) which met weekly (n=24 sessions) to establish a highly specialized interprofessional group. The DTT activities included: 1) Establishing a CGM workflow; 2) Expanding Medical Assistant responsibilities related to diabetes technology and training; 3) Creating five bilingual (English-Spanish) patient handouts; 4) Building CGM vendor relationships; 5) Developing a CGM manual for data download, billing and coding, and education to increase CGM knowledge across the system. Data were collected prior to CGM initiation and at most recent visit. Results: The intervention occurred between February and November 2023. Prior to this intervention, 49 patients had a CGM prescription. After 9 months, CGM prescriptions were written for 333 patients (680% increase) with the following baseline characteristics (mean ± SD): age (61.2 ± 14.7 yrs.); 56% female; 44% male; 71% Latino; Race: 67% Other (63% Hispanic or Latino), 25% Black or African American, 4% White, Asian 2%, American Indian or Alaska Native 2%; 38% Spanish-speaking, 59% English-Speaking, 3% Other; 81% government insurance (63% Medicaid, 18% Medicare), 19% commercial insurance. The majority of patients had been diagnosed with type 2 diabetes (91%) and 9% had type 1 diabetes. Baseline HbA1C was 9.7 ± 1.5%. Of note, prior to CGM, 34% of patients had A1C&gt;9%. The impact of CGM use was evaluated on 116 patients that met the following criteria: baseline A1C value within 60 days before or up-to 30 days after prescription, and a A1C &gt;90 days after. After an average duration of 154 days of CGM use, HbA1c decreased significantly by -0.51% (p=0.001), demonstrating improved glycemic control post-CGM use. In those with A1c &gt;9%, HbA1c decreased from 10.5% to 8.8% (p&lt;0.01). Conclusion: Creating a CGM optimization program in a resource-limited setting is critical in improving patient self-awareness and provider insight in glycemic control in patients diagnosed with type 1 and type 2 diabetes. These data demonstrate the feasibility of implementing a CGM optimization program in a resource-limited setting, which is critically important in improving clinical care for people with diabetes.

Presentation: 6/2/2024