ISQUA16-2431HOW NATIONS COMPARE: BENCHMARKING USE OF INFORMATION TECHNOLOGY TO IMPROVE CARE
Bibliographic record
Abstract
While health policies and priorities differ across countries, many nations have implemented strategies that aim to improve access to care, quality of health services, care coordination, and productivity within the health system through effective use of information technology (IT). Under the auspices of the OECD, this study piloted the collection of benchmark measures of health IT availability and use to facilitate cross-country learning. A prior OECD-led effort involving 30 countries resulted in the selection and definition of functionality-based measures for availability and use of electronic health records, health information exchange, personal health records, and telehealth. For this pilot, an OECD working group compiled the results for some or all measures for 38 countries based on new and/or adapted surveys and other data sources from 2012 to 2015. The group then synthesized feedback from a subset of countries to identify key learnings. Electronic records are now widely used to store and manage patient information at the point of care. All but two pilot countries reported use by at least half of their primary care physicians; many had rates of 75% or more. However, there are important differences in the specific data and functions available (e.g. ability to produce lists of patients according to diagnosis or prescriptions), as well as in how frequently healthcare providers use electronic records. Patient information exchange across organizations/settings supporting continuity and coordination of care is less common. For example, a few countries (Canada, Estonia, Finland, Luxembourg, and Malta) reported universal or near universal ability of acute care facilities to exchange radiology results and/or images with outside organizations. However, only about a half (53% in 2012) of European acute care facilities reported this ability. A number of countries within and outside of Europe had much lower rates. Variations in the availability and use of telehealth and personal health records are also large.
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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 | 0.052 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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".