COVID-19 and Telepsychiatry: Development of Evidence-Based Guidance for Clinicians
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Résumé
BACKGROUND: The coronavirus disease (COVID-19) presents unique challenges in health care, including mental health care provision. Telepsychiatry can provide an alternative to face-to-face assessment and can also be used creatively with other technologies to enhance care, but clinicians and patients may feel underconfident about embracing this new way of working. OBJECTIVE: The aim of this paper is to produce an open-access, easy-to-consult, and reliable source of information and guidance about telepsychiatry and COVID-19 using an evidence-based approach. METHODS: We systematically searched existing English language guidelines and websites for information on telepsychiatry in the context of COVID-19 up to and including May 2020. We used broad search criteria and included pre-COVID-19 guidelines and other digital mental health topics where relevant. We summarized the data we extracted as answers to specific clinical questions. RESULTS: Findings from this study are presented as both a short practical checklist for clinicians and detailed textboxes with a full summary of all the guidelines. The summary textboxes are also available on an open-access webpage, which is regularly updated. These findings reflected the strong evidence base for the use of telepsychiatry and included guidelines for many of the common concerns expressed by clinicians about practical implementation, technology, information governance, and safety. Guidelines across countries differ significantly, with UK guidelines more conservative and focused on practical implementation and US guidelines more expansive and detailed. Guidelines on possible combinations with other digital technologies such as apps (eg, from the US Food and Drug Administration, the National Health Service Apps Library, and the National Institute for Health and Care Excellence) are less detailed. Several key areas were not represented. Although some special populations such as child and adolescent, and older adult, and cultural issues are specifically included, important populations such as learning disabilities, psychosis, personality disorder, and eating disorders, which may present particular challenges for telepsychiatry, are not. In addition, the initial consultation and follow-up sessions are not clearly distinguished. Finally, a hybrid model of care (combining telepsychiatry with other technologies and in-person care) is not explicitly covered by the existing guidelines. CONCLUSIONS: We produced a comprehensive synthesis of guidance answering a wide range of clinical questions in telepsychiatry. This meets the urgent need for practical information for both clinicians and health care organizations who are rapidly adapting to the pandemic and implementing remote consultation. It reflects variations across countries and can be used as a basis for organizational change in the short- and long-term. Providing easily accessible guidance is a first step but will need cultural change to implement as clinicians start to view telepsychiatry not just as a replacement but as a parallel and complementary form of delivering therapy with its own advantages and benefits as well as restrictions. A combination or hybrid approach can be the most successful approach in the new world of mental health post-COVID-19, and guidance will need to expand to encompass the use of telepsychiatry in conjunction with other in-person and digital technologies, and its use across all psychiatric disorders, not just those who are the first to access and engage with remote treatment.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle