Effect of Pharmacist-Driven Professional Continuous Glucose Monitoring in Adults with Uncontrolled Diabetes

BACKGROUND: Diabetes requires close monitoring to achieve optimal outcomes and avoid adverse effects. Continuous glucose monitoring (CGM) is one approach to measuring glycemia and has become more widespread with recent advances in technology; however, ideal implementation of CGM into clinical practice is unknown. CGM can be categorized as personal CGM, which can be for at-home use to replace self-monitoring of blood glucose, or professional CGM (proCGM), which is used intermittently under the direction of a health care professional. The expanding role of the clinical pharmacist allows pharmacists to be at the forefront of implementing proCGM technology, but literature on the effect of pharmacist-driven proCGM is lacking. Pharmacists and physicians within 1 physician-owned clinic used proCGM technology differently. Pharmacists conducted 1 or 2 office visits to interpret data and make interventions, while physicians interpreted data 1 time and relayed interventions via phone. OBJECTIVES: To (a) compare the change in hemoglobin A1c from baseline to 6 months between the different methods of proCGM implementation, and (b) describe and compare the clinical interventions made as a result of the different methods of proCGM implementation. METHODS: In this retrospective cohort study, adults identified in the electronic medical record via Current Procedural Terminology code 95250 or 95251 undergoing proCGM with CGM data interpreted and baseline A1c >= 7% were included. Patients with additional CGM use within the 6-month follow-up period were excluded. Data collection included demographics, A1c at baseline and during the 6-month follow-up period, and CGM-associated interventions. Patients were categorized as undergoing 1 pharmacist-driven encounter (RPh1), 2 pharmacist-driven encounters (RPh2), or 1 physician-driven encounter (MD1) for proCGM implementation. Combined RPh1 and RPh2 (cRPh) data were also used for analysis. The primary outcome was change in A1c from baseline to 6 months, which was evaluated by analysis of covariance. RESULTS: Of 378 patient charts reviewed, 315 instances of proCGM implementation met inclusion criteria (58 RPh1, 35 RPh2, 222 MD1), and 253 had post-implementation A1c data for analysis of the primary outcome (52 RPh1, 30 RPh2, 171 MD1). Baseline A1c was 8.4%, 8.8%, and 9.1% with mean reduction from baseline to 6 months of 1.0%, 1.3%, and 0.6%, respectively. cRPh patients experienced a greater mean reduction in A1c compared with MD1 (P=0.002). RPh2 patients had a statistically significant reduction compared with MD1 (P=0.005), but RPh1 patients did not (P=0.054). The number of CGM-associated pharmacological interventions was 1.33 for RPh1 patients, 1.63 for RPh2 at the first encounter and 1.34 at the second, and 1.17 for MD1. CONCLUSIONS: Pharmacist-driven implementation of proCGM was associated with greater A1c reductions and more pharmacological interventions versus physician-driven implementation. This study demonstrated improved clinical outcomes with pharmacists providing direct patient care through implementation of new diabetes technology. Copyright (C) 2020, Academy of Managed Care Pharmacy. All rights reserved.