1257-P: Quality Improvement Project to Increase Insulin Pump Use in Pediatric Type 1 Diabetes
Bibliographic record
Abstract
Background: Insulin pump therapy in type 1 diabetes (T1D) has been associated with lower hemoglobin A1c than multiple daily injections. In the T1D Exchange, 63% of pediatric patients <18 years old in 2013-14 used pumps. However, pump use at our institution in 2015 was much lower at 39.4% (551 of 1399 patients <18 years old with T1D duration ≥1 year attending clinic at least twice yearly). A multidisciplinary team of pediatric endocrinologists, diabetes educators, and hospital leaders implemented a QI initiative aimed to increase pump use to ≥50%. Methods: Pump use was extrapolated from diabetes providers’ visits in the electronic medical record (EMR) and tracked via a run chart. A series of plan-do-study-act (PDSA) cycles were implemented, including: 1. Creation of introductory and advanced pump handouts for patients. 2. Quarterly technology sessions for providers and clinic staff to improve pump knowledge. 3. Creation of pump start orders in the EMR. 4. Certification of the clinic’s diabetes educators in all commonly used pumps. 5. Scheduled diabetes educator visits for all patients after pump initiation. Results: Pump use increased from 39.4% (551 of 1399 patients) in 2015 to 50.6% (730 of 1442 patients) in 2018 (Image 1). Conclusion: The aim of increasing pump use to ≥50% of our clinic’s population was achieved over 3 years. Building on this success, future direction is to evaluate impact on bolus frequency and association with HbA1c. Disclosure S. Lyons: None. N.L. Canada: None. K. Hernandez: None. J.E. Segovia: Consultant; Self; Insulet Corporation. S.K. Bartz: None. D.J. DeSalvo: Consultant; Self; Dexcom, Inc., Insulet Corporation. S. Mckay: None. R. Sonabend: None.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".