International health IT benchmarking: learning from cross-country comparisons
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
OBJECTIVE: To pilot benchmark measures of health information and communication technology (ICT) availability and use to facilitate cross-country learning. MATERIALS AND METHODS: A prior Organization for Economic Cooperation and Development-led effort involving 30 countries selected and defined functionality-based measures for availability and use of electronic health records, health information exchange, personal health records, and telehealth. In this pilot, an Organization for Economic Cooperation and Development Working Group compiled results for 38 countries for a subset of measures with broad coverage using new and/or adapted country-specific or multinational surveys and other sources from 2012 to 2015. We also synthesized country learnings to inform future benchmarking. RESULTS: While electronic records are widely used to store and manage patient information at the point of care-all but 2 pilot countries reported use by at least half of primary care physicians; many had rates above 75%-patient information exchange across organizations/settings is less common. Large variations in the availability and use of telehealth and personal health records also exist. DISCUSSION: Pilot participation demonstrated interest in cross-national benchmarking. Using the most comparable measures available to date, it showed substantial diversity in health ICT availability and use in all domains. The project also identified methodological considerations (e.g., structural and health systems issues that can affect measurement) important for future comparisons. CONCLUSION: While health policies and priorities differ, many nations aim to increase access, quality, and/or efficiency of care through effective ICT use. By identifying variations and describing key contextual factors, benchmarking offers the potential to facilitate cross-national learning and accelerate the progress of individual countries.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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