Virtual models of chronic disease management: lessons from the experiences of virtual care during the COVID-19 response
Notice bibliographique
Résumé
Objective This study examined Gold Coast staff and patient experiences with the rapid expansion of a virtual model of chronic disease management during the COVID-19 pandemic. Methods The study undertook a survey of enrolled patients (n=24) and focus groups with clinical and administrative staff (n=44) delivering chronic disease programs at Gold Coast Health in Queensland. The study also examined routinely collected activity data for the chronic disease programs before COVID (January-February 2020) and for the first 3 months of the COVID-19 response (March-May 2020). Results Chronic disease programs continued to provide similar numbers of appointments over the COVID-19 response period, but there was a marked increase in the proportion of appointments that were delivered virtually, either by telephone or video conference. Most patients were satisfied with their virtual care experiences and felt that their health care needs were met. Conclusions The COVID-19 response provided an opportunity to learn and further develop models of virtual care. Staff and patients were generally supportive of continuing to include virtual appointments in the future. Ongoing concerns were predominantly around the support available to patients and staff to ensure they are trained and equipped to manage the technology and new mode of communicating. What is known about the topic? Emerging evidence suggests that virtual models of health care delivery, such as telephone and video consultations and remote patient monitoring, can be safe and cost-effective alternatives to traditional face-to-face chronic disease management programs. Virtual care is associated with equal or improved clinical outcomes, as well as efficiency improvements, such as reduced failure to attend rates. What does this paper add? The increasing burden of chronic disease across Australia, as well as the need to minimise the risk of vulnerable patient groups attending in-hospital appointments where it is safe and appropriate to do so, means that expanding the delivery of virtual chronic disease management will become increasingly necessary. The results of this study provide an opportunity to learn from a rapid rollout of virtual care for these staff and patient groups and will help inform advances in this area. What are the implications for practitioners? Existing evidence, demographic pressures and the COVID-19 pandemic response all point to virtual care as a viable and safe alternative to traditional models of chronic disease management. The lessons presented here provide more detailed guidance on the support that staff and patients require to ensure virtual care is a seamless and safe alternative or adjunct to traditional chronic disease management programs.
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 | 0,013 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,011 | 0,014 |
| Communication savante | 0,007 | 0,006 |
| Science ouverte | 0,002 | 0,016 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».