Using Emerging Telehealth Technology as a Future Model in Vietnam During the COVID-19 Pandemic: Practical Experience From Phutho General Hospital
Bibliographic record
Abstract
Telehealth has emerged as a model of modern technology for health care services in Vietnam during the COVID-19 pandemic. To actively prevent the outbreak of COVID-19 by using a national digital transformation program, the Vietnamese Ministry of Health launched project 2628/Quyet dinh-Bo y te, which approved a scheme for remote medical examinations and treatments for 2020 to 2025. The project aims to connect 1000 hospitals to strengthen the quality of medical services by using the expertise of central hospitals to support rural areas via provincial hospitals. Phutho General Hospital (PGH) is one of leading provincial hospitals that participated in and applied the early telehealth systems in Vietnam. By using telehealth systems, PGH can offer valuable support to doctors' activities by streamlining and facilitating their work. Telehealth was demonstrated to be feasible, acceptable, and effective at PGH in Vietnam, and it resulted in considerable improvements in health care outcomes. The COVID-19 pandemic has facilitated the acceleration and enhancement of telehealth in Vietnam. The success of telehealth in Phutho may be a useful reference for other parts of the world. However, this telehealth system focuses on the connectivity among doctors rather than the connectivity between doctors and patients, which is an area that needs further assessment.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".