Advancing Regional and Remote Health Care With Virtual Hospital Implementation: Rapid Review
Notice bibliographique
Résumé
BACKGROUND: Disparities in health equity between metropolitan and rural areas are a global concern, especially in vast countries such as Australia, Canada, and the United States. Virtual care models in health care settings are promising in reducing inequalities, with virtual hospitals (VHs) potentially bridging the gap for isolated or underserved regions. However, evidence-based strategies and the complexities of VH implementation necessitate further research. OBJECTIVE: This rapid review aims to examine the role of VHs in enhancing regional and remote health care by focusing on accessibility, patient and health care provider experiences, and implementation barriers and facilitators. It provides tailored recommendations for large-scale implementation in communities with access issues, contributing to the discussion on equitable health care. METHODS: A rapid review was conducted in accordance with the World Health Organization guidelines. A systematic search was performed across PubMed, MEDLINE, CINAHL, and the La Trobe University Library for peer-reviewed articles published between January 2015 and March 2023. Additional gray literature was identified through Google searches and snowballing from relevant web articles. Studies were included if they focused on regional or remote populations and addressed VHs or virtual care. Studies that solely discussed hybrid models of care were excluded. Data were systematically extracted using a customized Microsoft Excel template. A mixed methods thematic analysis was conducted to identify recurring themes, barriers, facilitators, and recommendations related to VH implementation as well as patterns in clinical outcomes and stakeholder perspectives. RESULTS: A total of 35 articles were included in this review, comprising 23 (66%) peer-reviewed studies and 12 (34%) gray literature sources. Positive clinical outcomes were reported in 9 (26%) articles, highlighting outcomes such as reduced disease transmission, improved patient safety, fewer admissions and readmissions, lower mortality, shorter hospital stays, and better adherence to clinical best practices. Health system outcomes were identified in 15 (43%) articles, including reduced costs, enhanced patient experience and safety, improved care delivery and health care provider support, greater efficiency, broader geographic coverage, and better integration of services. Patient and health care provider perspectives were discussed in 12 (34%) articles, with positive views attributed to convenience, time and cost savings, and improved service quality. Barriers and facilitators were the most frequently discussed themes, appearing in 27 (77%) and 26 (74%) articles, respectively, with challenges and enablers commonly linked to people, processes, technology, and financial sustainability. CONCLUSIONS: VHs have the potential to revolutionize regional and remote health care by overcoming barriers, using facilitators, and following recommended practices, leading to better clinical outcomes and increased satisfaction for patients and health care providers.
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 | 0,017 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,017 | 0,017 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».