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 CGMassociated 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 ...

IntroductionContinuous glucose monitoring (CGM) is a burgeoning approach to measuring glycemia, but the ideal implementation method to optimize outcomes while streamlining clinical procedures is unknown. Furthermore, literature on the impact of pharmacist‐driven professional CGM (proCGM) is lacking.ObjectivesThe primary objective was to compare the change in hemoglobin A1c from baseline to 6 months after pharmacist‐driven proCGM implementation with one vs two proCGM data interpretation encounters. A secondary objective was to describe changes in proCGM report metrics for participants with two encounters.MethodsIn this retrospective single‐center study, adults with diabetes identified via Current Procedural Terminology code 95250 or 95251 undergoing pharmacist‐driven proCGM implementation with A1c measured within 6 months were included. Patients with additional CGM during the follow‐up period were excluded. Patients were categorized as having one pharmacist‐driven (RPh1) or two pharmacist‐driven (RPh2) encounters for proCGM data analysis for a single sensor. A1c change was analyzed via paired and independent sample t tests and analysis of covariance. ProCGM report metrics were analyzed via paired t tests.ResultsSixty‐six RPh1 and 56 RPh2 patients were included. Demographics were similar between groups, except RPh1 patients were younger, had higher body mass index, used less bolus insulin, and had less baseline hypoglycemia (P = .003, P = .008, P = .002, and P = .001, respectively). Baseline A1c was 8.2% and 8.3% with a mean reduction by 6 months of 0.75% and 0.87% for RPh1 and RPh2, respectively, and a mean follow‐up A1c of 7.4% for both groups. Significant A1c improvement was seen in each group compared with baseline (P < .001), with no significant difference between groups (P = .655). There was a significant rise in time in range from the first to second encounter without a significant increase in time below range (P < .001 and P = .34, respectively).ConclusionPharmacist‐driven proCGM implementation can significantly improve glycemic control, but no difference was seen in A1c lowering between the two implementation methods.

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