COVID-19 and Telepsychiatry: Development of Evidence-Based Guidance for Clinicians
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.163 | 0.452 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".