Finding digital health governance mechanism to support country’s health systems: Thailand case study
Bibliographic record
Abstract
In 2010, a collaboration between the Ministry of Public Health and the World Health Organization Thailand highlighted the urgent need for an effective eHealth governance mechanism in the country. Despite efforts, a consensus-driven governance mechanism remains elusive. This research aimed to investigate suitable digital health governance models for Thailand by examining models from six countries (Malaysia, the Philippines, Australia, England, the USA and Canada) and gathering insights from stakeholders. In stage 1, research gathered data via literature reviews and interviews with 11 executives in Thailand's digital health sectors. The study of six countries showed diverse digital health governance influenced by political, cultural and health factors. Using the Broadband Commission's governance models, most participants preferred a dedicated digital health agency. They emphasized decisive leadership, collaboration to prevent silos and uniform health information standards. In Thailand, the Ministry of Public Health cannot oversee digital health solely but can lead in tandem with other bodies. Effective governance requires collaboration, leadership and the dedicated agency model, underscoring health information standards' significance. Stage 2 published the 'Digital Health Governance Model: Recommendation for Thailand Health Systems', presented to 101 high-level representatives. A survey indicated that over 90% of these stakeholders concurred with the study's findings and recommendations. The research suggests that while the Ministry of Public Health is central, it should not manage alone. Collaborative governance with consistent leadership is crucial for Thailand's digital health progression. Although the study lacked civil society input, its insights are pivotal for Thailand's digital health policy future.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".