Expanding access to high-cost medicines under the Universal Health Coverage scheme in Thailand: review of current practices and recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There has been an increasing demand to reimburse high-cost medicines, through public health insurance schemes in Thailand. METHODS: A mixed method approach was employed. First, a rapid review of select high-income countries was conducted, followed by expert consultations and an in-depth review of three countries: Australia, England and Republic of Korea to understand reimbursement mechanisms of high-cost medicines. In Thailand, current pathways for reimbursing high-cost medicines reviewed, the potential opportunity cost estimated, and stakeholder consultations were conducted to identify context specific considerations. RESULTS: High-income countries reviewed have implemented a variety of pathways and mechanisms for reimbursing high-cost medicines under specific eligibility criteria, listing processes, varying cost-effectiveness thresholds and special funding arrangements. In Thailand, high-cost medicines that do not offer good value-for-money are excluded from the reimbursement process. A framework for reimbursing high-cost medicines that are not cost-effective at the current willingness-to-pay threshold was proposed for Thailand. Under this framework, specific criteria are proposed to determine their eligibility for reimbursement such life-saving nature, treatment of conditions with no alternative treatment options, and affordability. CONCLUSION: High-cost medicines may become eligible for reimbursement through alternative mechanisms based on specific criteria which depend on each context. The application of HTA methods and processes is important in guiding these decisions to support sustainable access to affordable healthcare in pursuit of Universal Health Coverage (UHC).
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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 | 0.029 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it