Implementation and Evaluation of a Best Practice Advisory to Reduce Inequities in Technology Use for People With Type 1 Diabetes: Protocol for a Mixed Methods, Nonrandomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Continuous advancements in diabetes technologies have improved self-management for people with type 1 diabetes. Continuous glucose monitoring and automated insulin delivery systems have enhanced the quality of life and glycemic outcomes while reducing severe hypoglycemia and diabetes ketoacidosis hospitalizations. Despite these benefits, racial inequities in the use of advanced diabetes technology (ADT) persist. OBJECTIVE: This study aims to develop and evaluate a best practice advisory (BPA) within the electronic medical record (EMR) to reduce racial and ethnic disparities in ADT use. We hypothesize that an EMR-based BPA designed to standardize the prescribing of ADTs will minimize racial and ethnic disparities in ADT adoption or progression in use among pediatric and adult people with type 1 diabetes. METHODS: The Best Practice Advisories to Reduce Inequities in Technology Use (BPA-TECH) study will use a nonrandomized matched pair intervention design. Phase 1 will use qualitative methods to develop and refine the BPA, including focus groups and surveys of health care providers and people with type 1 diabetes or their caregivers. Phase 2 will evaluate the effectiveness of the BPA through a controlled before-after study of people with type 1 diabetes seen at 7 T1D Exchange Quality Improvement Collaborative (T1DX-QI) centers, with control people with type 1 diabetes matched from nonintervention T1DX-QI centers. The baseline and postintervention periods will be the 12 months before and 12 months after deployment of the BPA at the intervention centers, respectively. Eligibility criteria include people with type 1 diabetes aged ≥2 years with an EMR diagnosis of T1D during the baseline period. The primary outcome is the progression in ADT use from the baseline to postintervention periods. RESULTS: ), severe hypoglycemic events, and diabetes ketoacidosis events will be collected via the T1DX-QI coordinating center. The study is powered to detect a between-group difference of 15% in the proportion of patients in the intervention and control groups in meeting the primary endpoint. We anticipate the completion of this study by May 2027. CONCLUSIONS: The BPA-TECH study aims to leverage health IT to address racial and ethnic disparities in ADT use among people with type 1 diabetes. By standardizing the approach to ADT prescribing for people with type 1 diabetes, the BPA-TECH has the potential to promote equity in diabetes management and improve clinical outcomes. The outcomes of this study will inform future efforts to reduce health care disparities. TRIAL REGISTRATION: ClinicalTrials.gov NCT06931275; https://clinicaltrials.gov/search?term=NCT06931275. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/71038.
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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 | 0.045 | 0.047 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.069 | 0.011 |
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".