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Enregistrement W4399260815 · doi:10.51731/cjht.2024.905

Virtual Medicine Wards and Hospital-at-Home Programs

2024· article· en· W4399260815 sur OpenAlexaboutno aff
CADTH

Notice bibliographique

RevueCanadian Journal of Health Technologies · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueTelemedicine and Telehealth Implementation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHospital medicineMedical emergencyMedicineFamily medicine

Résumé

récupéré en direct d'OpenAlex

What Is the Issue? In 2021, the occupancy rate of acute care hospital beds in Canada was 86.7%. High occupancy rates without turnover to accommodate all hospitalization needs is an indicator of potential bed shortages and health system pressure. Patients have historically remained in hospital beds until their treatment or recovery is complete. Some patients may be well enough to continue their treatment or recovery at home sooner if provided with the right supports. What Are the Technologies? Virtual wards, also known as hospital-at-home programs, support the provision of inpatient-level acute medical care in a patient’s home. There are 2 main models of these programs: admission avoidance and supported early discharge. This report focuses on the latter type. Many of these programs are technology-enabled and incorporate remote monitoring devices to record the patient’s vital signs and tablets or web portals to facilitate data sharing. Video calls with the clinical team are also used in combination with in-person visits by health care providers. What Is the Potential Impact? Hospital beds can be freed up more quickly to provide space for newly admitted patients with more acute care needs. The safety and effectiveness of virtual ward programs have been examined in several systematic reviews in the existing clinical literature. Factors evaluated include mortality, length of stay, hospital readmissions, and costs as outcomes. Both admission avoidance and early supported discharge via hospital-at-home programs had lower or similar mortality and hospital admission outcomes as inpatient care after completion of care. Patients, caregivers, and health care providers appear to be generally satisfied with their participation in virtual ward programs. Comfort and satisfaction can be improved by allowing patients to receive treatment in a familiar and comfortable environment without compromising patient outcomes. However, increased caregiver burden, lack of sufficient training for participants and staff, and difficulties recruiting health care providers were identified as challenges associated with virtual ward programs. What Else Do We Need to Know? The level of technological support required by patients, caregivers, and staff participating in these programs should be considered when developing a program. Adequate training about how to use provided equipment and other tasks needed to manage care in the home (e.g., drug administration, symptom monitoring, communication with health care professionals) is required for patients and caregivers. There should also be provision of all necessary equipment with supports to overcome any barriers (e.g., visual impairment, physical limitations) to ensure comfort and proficiency. Care coordination and communication among the multidisciplinary care team, the patient, and their caregivers is important. Canadian cost data were not identified, but it is generally accepted that virtual ward programs are associated with reduced costs when compared with traditional in-hospital care. The inclusion of digital monitoring and record keeping technologies as part of virtual ward programs may disproportionately exclude people from some groups, including older people, people living in social housing or without housing, people with lower incomes, people who are unemployed, people living with disabilities, and people who live in rural areas without access to such programs. Key recommendations for development of virtual ward programs in Canada include using a single remote patient-monitoring platform that connects with the hospital’s electronic health record system, choosing a technology to connect patients and providers that best fits the needs of the virtual ward program, and ensuring data security, confidentiality, and data management protocols are in place.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,031
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,038

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,031
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0110,000

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.

Tête enseignante Opus0,030
Tête enseignante GPT0,332
Écart entre enseignants0,302 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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