Shaping Workflows in Digital and Remote Diabetes Care During the COVID-19 Pandemic via Service Design: Prospective, Longitudinal, Open-label Feasibility Trial
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic poses new challenges to health care providers and the delivery of continuous care. Although many diabetes technologies, such as insulin pumps and continuous glucose monitors, have been established, the data from these devices are rarely assessed. Furthermore, telemedicine has not been sufficiently integrated into clinical workflows. OBJECTIVE: We sought to remotely support children with type 1 diabetes and their caregivers, enhance the clinical outcomes and quality of life of children with diabetes, increase multiple stakeholders' engagement with digital care via a participatory approach, evaluate the feasibility of using an interoperable open-source platform in a university hospital setting, and analyze the success factors and barriers of transitioning from conventional care to digital care. METHODS: Service design methods were used to adapt clinical workflows. Remote consultations were performed on a monthly and on-demand basis. Diabetes device data were uploaded from patients' homes to an open-source platform. Clinical and patient-reported outcomes were assessed before, during, and after the COVID-19 lockdown period in Germany. RESULTS: A total of 28 children with type 1 diabetes and their caregivers enrolled in this study and completed 6 months of remote visits. Of these 28 participants, 16 (57%) also opted to attend at least one of their regular visits remotely. After 3 months of remote visits, participants' time in range (P=.001) and time in hyperglycemia (P=.004) significantly improved, and their time in hypoglycemia did not increase. These improvements were maintained during the COVID-19 lockdown period (ie, between months 3 and 6 of this study). Participants' psychosocial health improved after 6 months. CONCLUSIONS: Remote consultations and commonly shared data access can improve the clinical outcomes and quality of life of children with type 1 diabetes, even during challenging circumstances. A service design approach helped with the delivery of comprehensive and holistic solutions that accounted for the needs of multiple stakeholders. Our findings can inform the future integration of digital tools into clinical care during and beyond the pandemic. TRIAL REGISTRATION: German Clinical Trials Register DRKS00016170; https://tinyurl.com/skz4wdk5.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,001 |
| 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 ».