Spread, Scale-up, and Sustainability of Video Consulting in Health Care: Systematic Review and Synthesis Guided by the NASSS Framework (Preprint)
Notice bibliographique
Résumé
BACKGROUND COVID-19 has thrust video consulting into the limelight, as health care practitioners worldwide shift to delivering care remotely. Evidence suggests that video consulting is acceptable, safe, and effective in selected conditions and settings. However, research to date has mostly focused on initial adoption, with limited consideration of how video consulting can be mainstreamed and sustained. OBJECTIVE This study sought to do the following: (1) review and synthesize reported opportunities, challenges, and lessons learned in the scale-up, spread, and sustainability of video consultations, and (2) identify transferable insights that can inform policy and practice. METHODS We identified papers through systematic searches in PubMed, CINAHL, and Web of Science. Included articles reported on synchronous, video-based consultations that had spread to more than one setting beyond an initial pilot or feasibility stage, and were published since 2010. We used the Nonadoption, Abandonment, and challenges to the Scale-up, Spread, and Sustainability (NASSS) framework to synthesize findings relating to 7 domains: an understanding of the health condition(s) for which video consultations were being used, the material properties of the technological platform and relevant peripherals, the value proposition for patients and developers, the role of the adopter system, organizational factors, wider macro-level considerations, and emergence over time. RESULTS We identified 13 papers describing 10 different video consultation services in 6 regions, covering the following: (1) video-to-home services, connecting providers directly to the patient; (2) hub-and-spoke models, connecting a provider at a central hub to a patient at a rural center; and (3) large-scale top-down evaluations scaled up or spread across a national health administration. Services covered rehabilitation, geriatrics, cancer surgery, diabetes, and mental health, as well as general specialist care and primary care. Potential enablers of spread and scale-up included embedded leadership and the presence of a telehealth champion, appropriate reimbursement mechanisms, user-friendly technology, pre-existing staff relationships, and adaptation (of technology and services) over time. Challenges tended to be related to service development, such as the absence of a long-term strategic plan, resistance to change, cost and reimbursement issues, and the technical experience of staff. There was limited articulation of the challenges to scale-up and spread of video consultations. This was combined with a lack of theorization, with papers tending to view spread and scale-up as the sum of multiple technical implementations, rather than theorizing the distinct processes required to achieve widespread adoption. CONCLUSIONS There remains a significant lack of evidence that can support the spread and scale-up of video consulting. Given the recent pace of change due to COVID-19, a more definitive evidence base is urgently needed to support global efforts and match enthusiasm for extending use.
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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 | 0,115 | 0,283 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,011 | 0,013 |
| Bibliométrie | 0,032 | 0,030 |
| Études des sciences et des technologies | 0,002 | 0,004 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».