The Logistics of Medication and Patient Flow in Video-Based Virtual Clinics During a Sudden COVID-19 Outbreak in Taiwan: Observational Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic was well controlled in Taiwan until an outbreak in May 2021. Telemedicine was rapidly implemented to avoid further patient exposure and to unload the already burdened medical system. OBJECTIVE: To understand the effect of COVID-19 on the implementation of video-based virtual clinic visits during this outbreak, we analyzed the logistics of prescribing medications and patient flow for such virtual visits at a tertiary medical center. METHODS: We retrospectively collected information on video-based virtual clinic visits and face-to-face outpatient visits from May 1 to August 31, 2021, from the administrative database at National Taiwan University Hospital. The number of daily new confirmed COVID-19 cases in Taiwan was obtained from an open resource. RESULTS: There were 782 virtual clinic visits during these 3 months, mostly for the departments of internal medicine, neurology, and surgery. The 3 most common categories of medications prescribed were cardiovascular, diabetic, and gastrointestinal, of which cardiovascular medications comprised around one-third of all medications prescribed during virtual clinic visits. The number of virtual clinic visits was significantly correlated with the number of daily new confirmed COVID-19 cases, with approximately a 20-day delay (correlation coefficient 0.735; P<.001). The patient waiting time for video-based virtual clinic visits was significantly shorter compared with face-to-face clinic visits during the same period (median 3, IQR 2-6 min vs median 20, IQR 9-42 min; rank sum P<.001). Although the time saved was appreciated by the patients, online payment with direct delivery of medications without the need to visit a hospital was still their major concern. CONCLUSIONS: Our data showed that video-based virtual clinics can be implemented rapidly after a COVID-19 outbreak. The virtual clinics were efficient, as demonstrated by the significantly reduced waiting time. However, there are still some barriers to the large-scale implementation of video-based virtual clinics. Better preparation is required to improve performance in possible future large outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".