Digital mHealth and Virtual Care Use During COVID-19 in 4 Countries: Rapid Landscape Review
Notice bibliographique
Résumé
BACKGROUND: As a result of the COVID-19 pandemic, providing health care while maintaining social distancing has resulted in the need to provide care remotely, support quarantined or isolated individuals, monitor infected individuals and their close contacts, as well as disseminate accurate information regarding COVID-19 to the public. This has led to an unprecedented rapid expansion of digital tools to provide digitized virtual care globally, especially mobile phone-facilitated health interventions, called mHealth. To help keep abreast of different mHealth and virtual care technologies being used internationally to facilitate patient care and public health during the COVID-19 pandemic, we carried out a rapid investigation of solutions being deployed and considered in 4 countries. OBJECTIVE: The aim of this paper was to describe mHealth and the digital and contact tracing technologies being used in the health care management of the COVID-19 pandemic among 2 high-income and 2 low-middle income countries. METHODS: We compared virtual care interventions used for COVID-19 management among 2 high-income countries (the United Kingdom and Canada) and 2 low-middle income (Kenya and Rwanda) countries. We focused on interventions used to facilitate patient care and public health. Information regarding specific virtual care technologies was procured from a variety of resources including gray literature, government and health organization websites, and coauthors' personal experiences as implementers of COVID-19 virtual care strategies. Search engine queries were performed to find health information that would be easily accessible to the general public, with keywords including "COVID-19," "contact-tracing," "tool-kit," "telehealth," and "virtual care," in conjunction with corresponding national health authorities. RESULTS: We identified a variety of technologies in Canada, the United Kingdom, Rwanda, and Kenya being used for patient care and public health. These countries are using both video and text message-based platforms to facilitate communication with health care providers (eg, WelTel and Zoom). Nationally developed contact tracing apps are provided free to the public, with most of them using Bluetooth-based technology. We identified that often multiple complimentary technologies are being utilized for different aspects of patient care and public health with the common purpose to disseminate information safely. There was a negligible difference among the types of technologies used in both high-income and low-middle income countries, although the latter implemented virtual care interventions earlier during the pandemic's first wave, which may account for their effective response. CONCLUSIONS: Virtual care and mHealth technologies have evolved rapidly as a tool for health care support for both patient care and public health. It is evident that, on an international level, a variety of mHealth and virtual care interventions, often in combination, are required to be able to address patient care and public health concerns during the COVID-19 pandemic, independent of a country's economic standing.
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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 | 0,006 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».