Implementation of a continuous glucose monitoring workflow in a complex primary care clinic
Bibliographic record
Abstract
BACKGROUND: Continuous glucose monitors (CGM) are supported by national clinical practice guidelines for glucose monitoring in many people with diabetes. However, CGM data are often underutilized in primary care settings, where most adults with diabetes are treated. LOCAL PROBLEM: Despite a growing patient population using CGM in a complex primary care clinic, the clinic lacks a structured workflow process for manually uploading CGM reports to the electronic health record. As a result, CGM data are inconsistently used by primary care providers for clinical decision-making during routine visits. METHODS: Using the Plan-Do-Study-Act methodology, workflow processes for registered nurses (RNs), licensed practical nurses (LPNs), doctors of medicine (MDs), family nurse practitioners (FNPs), and clinical pharmacists (PharmDs) were examined and improved to support the project goals. INTERVENTIONS: Patients actively using CGM were identified daily. Assigned clinic nurses (n = 3; 1 RN and 2 LPNs) uploaded CGM logs as precharting to the visit, which were then used by providers (n = 3; 1 MD and 2 FNPs) during clinical encounters. When nurses were not available, the MD, FNPs, or PharmD (n = 1) completed the workflow. RESULTS: Ambulatory glucose profiles were uploaded to precharting in 43 of 45 patients (96%) with active CGM during the project evaluation period. Providers discussed CGM in 38 (88%) of these cases, using it correctly 100% of the time. The current procedural terminology code 95251 was billed in 35 (92%) of the applicable visits. CONCLUSIONS: Interprofessional teamwork to implement clinic workflow process improvements supports the delivery of guideline-driven diabetes care for adults using CGM.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".