41 Innovative solutions to support “Virtual First” pediatric endocrine care
Notice bibliographique
Résumé
Abstract Primary Subject area Endocrinology and Metabolism Background With the onset of the SARS-CoV-2 pandemic, health care providers everywhere were forced to rapidly shift the way they deliver care. Within our community-based academic organization, there was variability in response to required changes among different clinical areas, with many clinics ramping down their services while they restructured. In our pediatric endocrinology clinic, we had built the infrastructure to support virtual care using a provincial platform as part of a pilot program for our diabetes population in the year preceding the pandemic. This experience set the stage for a swift pivot to virtual care. To ensure ongoing high quality consultation and follow-up services during the pandemic, our clinic required rapid restructuring to successfully and immediately shift completely to a sustainable “virtual first” approach in March 2020. Objectives In the months following the onset of the SARS-CoV-2 pandemic, we sought to quickly develop and implement innovative strategies, using a quality improvement framework, to supplement virtual care and maintain high quality care delivery. Design/Methods As soon as physical distancing measures were implemented in March 2020, our multidisciplinary team held daily 30-minute meetings to troubleshoot, brainstorm, and strategize potential adaptations in care delivery to ensure we continued to meet patient and family needs with primarily virtual care. Barriers and problems were presented and prioritized, solutions proposed, then implemented with support of operation and e-health teams. Attention to educational needs for medical students, residents and fellows helped shape solutions. Results The following innovative solutions were successfully implemented within three months: • a drive thru hemoglobin A1C clinic for patients with diabetes • a streamlined “low touch” Auxology Clinic to supplement virtual visits when body measurement, vital signs or physical exam assessment were required • pre-visit preparation instructions for patients and families • active promotion of patient portal enrolment • re-design of follow-up orders content to allow providers to accurately indicate suitability of virtual care alone or with support measures • a workflow to allow quick conversion from in-person to virtual visits to prevent cancellations related to isolation requirements • an educational framework to ensure level-appropriate exposure to and involvement in patient care for trainees • auto-faxing of medication and supplies • printer mapping and workflow for external lab requisitions • provider/staff scheduling and role re-assignment to facilitate minimal number of on-site staff • support of the team to adopt best practices for virtual visits Conclusion While virtual care delivery existed before the pandemic, it was rarely used outside of pilot projects, or only from necessity, when travel to a health care facility was not possible. Herein we provide an overview of an innovative, primarily virtual, care delivery model to satisfy patient and family needs in a pediatric endocrinology clinic in an academic centre. Many components of our model have (and can be) applied or adapted to support care delivery in other clinical areas. The people, processes, and digital health adaptations required to support a primarily virtual mode of care were critical to its success.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,005 |
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 source (Gemma direct ou Codex distillé), 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 ».