Diabetes Performance Improvement Program With Continuous Glucose Monitoring, Pharmacist Intervention, and Team Management
Bibliographic record
Abstract
The growing prevalence of diabetes in the USA continues to be a significant public health concern. A significant proportion of patients with type 2 diabetes (T2D) have elevated glucose levels, as evidenced by a glycated hemoglobin (HbA1c) level > 9.0%. Persistent hyperglycemia results in the development of chronic macrovascular and microvascular complications. Previous strategies to assist this high-risk population in achieving optimal glycemic control have not been as successful as desired. As the demand for healthcare providers and services continues to grow at an unprecedented pace, the USA is facing a national deficit in physicians, nurse practitioners, and physician assistants. Conversely, the number of pharmacists is projected to increase at a rate of 3% annually over the next three decades. Studies have demonstrated that pharmacist involvement in diabetes patient management has resulted in improvements in HbA1c, lowering of low-density lipoprotein (LDL)-cholesterol levels, and achievement of blood pressure targets. This suggests the potential for pharmacists to play a key role in narrowing the gap. We implemented a Diabetes Performance Improvement Program (DPIP) that facilitates a comprehensive lifestyle intervention designed to improve diabetes management and outcomes. The DPIP care team comprises endocrinologists, certified diabetes educators, pharmacists, and supporting staff. The intervention includes utilizing continuous glucose monitoring (CGM) supported by diabetes self-management training (DSMT) and medical nutrition therapy (MNT) delivered by a certified diabetes education specialist (CDES) and registered dietitian (RD). This article reviews the evidence supporting the use of an interdisciplinary team-based approach to diabetes care, describes the DPIP components, and provides guidance for implementing the program in clinic-community settings. J Endocrinol Metab. 2024;14(5):213-220 doi: https://doi.org/10.14740/jem1013
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".