149 Virtual Care: A Quality Improvement Project on the Experience of Paediatricians during the COVID-19 Pandemic
Notice bibliographique
Résumé
Abstract Primary Subject area Practice/Office Management Background Prior to the COVID-19 pandemic, in-person visits were the standard of care for paediatricians at our centre. With the pandemic onset, virtual care (VC) was adopted at an unprecedented scale and pace. Studies have reported positive patient VC experience; however, few have explored physician experience. This quality improvement (QI) initiative sought to qualify the VC experience of local paediatricians during the pandemic, with the intention of implementing VC clinical practice changes at the department level. Objectives To determine key factors that have supported and challenged the adoption of, and that will support integration of, VC in the future. Design/Methods The Donabedian model for healthcare QI was used to evaluate VC experience through an online survey with a focus on structure, process, and outcome measures. All physicians affiliated with the Department of Paediatrics (generalists and subspecialists in medicine and surgery) were invited to participate via email. Three reminder emails were sent at 2-week intervals. Descriptive statistics were reported. Results The response rate was 32.3% (63 of 195 physicians). The majority of respondents were subspecialists (84.1%), and at academic centres (87.5%) (Table 1). Pre-pandemic, only 30.1% used VC and saw <10% of patients virtually. During March-May 2020, 93.8% transitioned to VC, with > 50% seeing over 75% of patients virtually. By summer 2020, VC use declined, but remained higher than pre-pandemic (53.6% seeing < 25% of patients). OTN and telephone were platforms most used (32.8% and 28.6%, respectively). Most conducted visits from their work location (55.2%) versus home (44.8%). VC experience was considered positive by most physicians (73.6%), and only 18.8% found VC difficult to use despite technical difficulties reported by 41.5% (Figure 1). Physicians with ≤ 5 years in practice were most likely to find VC convenient (93.8%). Challenges with VC included lack of physical exam, diagnostic uncertainty, lower patient volumes, and poor patient VC etiquette. Regardless of practice location, specialty, years in practice, and prior experience, 96% would continue VC to 25% of patients, ideally for patients who live far away (26.4%) and for follow-ups of patients with established diagnoses (21.4%). Conclusion A rapid transition to VC during the COVID-19 pandemic was associated with challenges but also positive experiences. Willingness to continue VC was high. VC experience could be improved with greater patient education and focus on select patient populations. Future research is needed to improve practice efficiency and to inform regulatory guidelines for VC at a local level.
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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 | 0,015 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».