Improving Care Delivery for Young Adults With Type 1 Diabetes via a Multi-Faceted Quality Improvement Interdisciplinary Intervention
Bibliographic record
Abstract
Abstract Background: The transition from pediatric to adult type 1 diabetes (TID) care represents a vulnerable period for young adults (YA), and many are eventually lost to follow up. This can result in lost opportunities for patient education, worsened glycemic control and increased rates of acute diabetes complications. To address this, a multi-faceted quality improvement (QI) intervention was implemented at a YA T1D program with the goal of improving patient attendance and care delivery amongst YA with T1D. Methods: The intervention consisted of three main components: a transitional navigator, an interdisciplinary diabetes assessment flowsheet and virtual care via phone or video conference. These components were implemented at the YA T1D program using a stepwise approach beginning in 2019. The attendance of all patients seen between January 2017 and August 2020 were tracked monthly on a run chart to identify any shifts after each component was implemented. A pre-post analysis was also performed in new patients with a minimum follow up period of 12 months to compare secondary outcomes including A1c reduction at 12 months, incidence of diabetes-related ED visits/hospitalizations, incidence of severe hypoglycemia and psychosocial counselling rates. Results: A total of 2240 scheduled appointments was included in the primary analysis. Patient attendance improved from 59% to 79% (p<0.01) with virtual care, demonstrated by a shift in attendance sustained over 6 months after its implementation. Virtual care was utilized in 81.3% of appointments in the post-intervention period. Subgroup analysis showed the improvement in attendance was significant in follow up appointments (80% vs 59%, p<0.01), but there was no difference in attendance for initial consultations (67% vs 58%, p=0.45). Forty-two patients were included in the pre-post analysis (n=27 in the pre-intervention and n=15 in the post-intervention period). There were with no significant difference in baseline characteristics of the two groups. Mean patient age was 20.2±2.9 years. Males comprised of 28.5% of the study population. Mean duration of diabetes was 11.1±5.3 years, and baseline average A1c was 8.6±1.7%. Preliminary analysis demonstrated there was significant improvement in preconception counselling rate (76% vs 100%, p=0.048) following the intervention. There was no significant difference in A1c reduction at 12 months, incidence of diabetes-related ED visits/hospitalizations or incidence of severe hypoglycemia. Conclusion: Virtual care was effective in improving attendance for follow up appointments at a YA T1D clinic. Further data analysis for patients assessed in September to December 2020 is currently underway.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".