Telemedicine Utilization in Tertiary, Specialized, and Secondary Hospitals in Thailand
Bibliographic record
Abstract
Introduction: COVID-19 has accelerated the adoption of telemedicine for counseling, follow-up examination, and treatment purposes. The official guidelines in Thailand were launched to regulate or frame the protocols for health care professions and teams in different organizations. Objectives: To explore the trend of telemedicine utilization in selected hospitals in Thailand and to understand the characteristics of patients who used telemedicine from 2020 to 2023. Methods: This retrospective secondary data analysis was conducted in four hospitals in Thailand: two tertiary care (T1 and T2) hospitals, one secondary care (SN) hospital, and one specialized (SP) hospital. Data were routinely collected when services were provided and were categorized into telemedicine outpatient department (OPD) visits or onsite OPD visits. The data included demographic information (age, sex), date and year of service, location (province and health region), and primary diagnosis (using International Statistical Classification of Diseases and Related Health Problems 10th Revision codes). Descriptive analysis was conducted using R and STATA software. Results: < 0.001). Conclusion: The situation during the COVID-19 pandemic and the transition to the post-COVID-19 era impacted telemedicine utilization, which could support national monitoring and evaluation policies. However, further studies are needed to explore other aspects, including changes in telemedicine utilization over time for longer timeframes, effectiveness of telemedicine, and consumer satisfaction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".