Improving Care Delivery for Young Adults With Type 1 Diabetes via a Multi-Faceted Quality Improvement Interdisciplinary Intervention
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».