A Retrospective Secondary Data Analysis of Telemedicine Service Utilization (2020–2023) Among Patients Covered By The Universal Coverage Scheme in Thailand
Bibliographic record
Abstract
Objective: The National Health Security Office in Thailand introduced a telemedicine program called “Telehealth/Telemedicine” in December 2020, which aimed to reimburse telemedicine services for patients with stable chronic diseases under the Universal Coverage Scheme (UCS). The current study investigated patient characteristics and trends in telemedicine service utilization under the UCS in Thailand and examined the impact of COVID-19 outbreaks on telemedicine services. Methods: A retrospective secondary data analysis using e-claim data from December 1, 2020, to April 18, 2023, was conducted. The analytical methods included descriptive analysis and an interrupted time series analysis. Results: During ∼29 months, 110,153 unique patients used telemedicine services, leading to a total of 259,047 visits. The average age was 54 years, and most of patients were female (57%). Hypertension was the most common diagnosis for patients receiving telemedicine services. Patients with mental health conditions often engaged in telemedicine consultation with drug delivery. During the Delta and Omicron outbreaks, telemedicine service utilization significantly increased compared with that in any nonpandemic periods within the 29-month timeframe (odds ratio [OR]: 3.85, p -value <0.01; OR: 2.55, p -value <0.01). Conclusions: The study findings highlight the initial trend of telemedicine services in Thailand from the start of the COVID-19 pandemic to the beginning of the post-COVID-19 period. As telemedicine will play a critical role in the future of health care, this information can support the scale-up of telemedicine, including monitoring and evaluation plans, to help improve the efficiency of the system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".