Uses of Virtual Care in Primary Care: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic catalyzed an uptake in virtual care. However, the rapid shift left unanswered questions about the impact of virtual care on the quality of primary care and its appropriateness and effectiveness. Moving forward, health care providers require guidance on how best to use virtual care to support high-quality primary care. OBJECTIVE: This study aims to identify and summarize clinical studies and systematic reviews comparing virtual care and in-person care in primary care, with a focus on how virtual care can support key clinical functions such as triage, medical assessment and treatment, counseling, and rehabilitation in addition to the management of particular conditions. METHODS: We conducted a scoping review following an established framework. Comprehensive searches were performed across the following databases: Embase, MEDLINE, PsycInfo, Emcare, and Cochrane Database of Systematic Reviews. Other well-known websites were also searched. PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines were followed. Articles were selected by considering article type, language, care provided, intervention, mode of care delivery, and sample size. RESULTS: A total of 13,667 articles were screened, and 219 (1.6%) articles representing 170 studies were included in the review. Of the 170 studies included, 142 (83.5%) were primary studies, and 28 (16.5%) were systematic reviews. The studies were grouped by functions of primary care, including triage (16/170, 9.4%), medical assessment and treatment of particular conditions (63/170, 37.1%), rehabilitation (17/170, 10%), and counseling (74/170, 43.5%). The studies suggested that many primary care functions could appropriately be conducted virtually. Virtual rehabilitation was comparable to in-person care and virtual counseling was found to be equally effective as in-person counseling in several contexts. Some of the studies indicated that many general primary care issues could be resolved virtually without the need for any additional follow-up, but data on diagnostic accuracy were limited. Virtual triage is clinically appropriate and led to fewer in-person visits, but overall impact on efficiency was unclear. Many studies found that virtual care was more convenient for many patients and provided care equivalent to in-person care for a range of conditions. Studies comparing appropriate antibiotic prescription between virtual and in-person care found variable impact by clinical condition. Studies on virtual chronic disease management observed variability in impact on overall disease control and clinical outcomes. CONCLUSIONS: Virtual care can be safe and appropriate for triage and seems equivalent to in-person care for counseling and some rehabilitation services; however, further studies are needed to determine specific contexts or medical conditions where virtual care is appropriate for diagnosis, management outcomes, and other functions of primary care. Virtual care needs to be adapted to fit a new set of patient and provider workflows to demonstrate positive impacts on experience, outcomes, and costs of care.
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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 | 0,028 | 0,110 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,008 |
| Bibliométrie | 0,029 | 0,027 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».