Telemedicine Utilization Patterns and Implications Amidst COVID-19 Outbreaks in Thailand Under Public Universal Coverage Scheme
Bibliographic record
Abstract
During COVID-19 pandemic, telemedicine was a strategy to facilitate healthcare service delivery minimizing the risk of direct exposure among people. In Thailand, the National Health Security Office has included telemedicine services under the Universal Coverage Scheme to support social distancing policies to reduce the spread of COVID-19. This study aimed to determine the patterns of telemedicine service use during major COVID-19 outbreaks including Alpha, Delta, and Omicron in Thailand. We retrospectively analyzed a dataset of telemedicine e-claims from the National Health Security Office, which covers services reimbursed under the Universal Coverage Scheme between December 2020 and August 2022. An interrupted time-series analysis, Pearson correlation analysis and binary logistic regression were performed. Almost 70% of the patients using telemedicine services were over 40 years old. Most patients used services for mental health problems (25.6%) and major noncommunicable diseases, including essential hypertension (12.6%) and diabetes mellitus (9.2%). The daily number of using telemedicine service was strongly correlated with the number of COVID-19 new cases detected. An immediate change in the trend of using telemedicine was detected at the onset of outbreaks along with the surge of infection. The follow-up use of telemedicine services was not substantial among female, older adults patients and those with non-communicable diseases except mental health problems, and infectious diseases. Strategies need to be developed to reinforced healthcare resources for telemedicine during the surge of outbreaks and sustain the use of telemedicine services for chronic and infectious diseases, regardless of the pandemic, and promote the efficiency of healthcare systems.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".