Telehealth and the COVID-19 Pandemic: International Perspectives and a Health Systems Framework for Telehealth Implementation to Support Critical Response
Bibliographic record
Abstract
OBJECTIVES: Telehealth implementation is a complex systems-based endeavour. This paper compares telehealth responses to (COrona VIrus Disease 2019) COVID-19 across ten countries to identify lessons learned about the complexity of telehealth during critical response such as in response to a global pandemic. Our overall objective is to develop a health systems-based framework for telehealth implementation to support critical response. METHODS: We sought responses from the members of the International Medical Informatics Association (IMIA) Telehealth Working Group (WG) on their practices and perception of telehealth practices during the times of COVID-19 pandemic in their respective countries. We then analysed their responses to identify six emerging themes that we mapped to the World Health Organization (WHO) model of health systems. RESULTS: Our analysis identified six emergent themes. (1) Government, legal or regulatory aspects of telehealth; (2) Increase in telehealth capacity and delivery; (3) Regulated and unregulated telehealth; (4) Changes in the uptake and perception of telemedicine; (5) Public engagement in telehealth responses to COVID-19; and (6) Implications for training and education. We discuss these themes and then use them to develop a systems framework for telehealth support in critical response. CONCLUSION: COVID-19 has introduced new challenges for telehealth support in times of critical response. Our themes and systems framework extend the WHO systems model and highlight that telemedicine usage in response to the COVID-19 pandemic is complex and multidimensional. Our systems-based framework provides guidance for telehealth implementation as part of health systems response to a global pandemic such as COVID-19.
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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 | 0.023 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".