Reporting and use of the OECD Health Care Quality Indicators at national and regional level in 15 countries
Bibliographic record
Abstract
QUALITY PROBLEM OR ISSUE: OECD member states are involved since 2003 in a project coordinated by the OECD on Health Care Quality Indicators (HCQI). All OECD countries are biennially requested by the OECD to deliver national data on the quality indicators for international benchmarking purposes. INITIAL ASSESSMENT: Currently, there is no knowledge whether the OECD HCQI information is used by the countries themselves for healthcare system accountability and improvement purposes. CHOICE OF SOLUTION: The objective of the study is to explore the reporting and use of OECD HCQI in OECD member-states. IMPLEMENTATION: Data were collected through a questionnaire sent to all OECD member-states containing factual questions on the reporting on all OECD HCQ-indicators. Responses were received between June and December 2014. In this timeframe, two reminders were sent to the participants. The work progress was presented during HCQI Meetings in November 2014 and May 2015. EVALUATION: Fifteen countries reported to have a total of 163 reports in which one or more HCQIs were reported. One hundred and sixteen were national and 47 were regional reports. Forty-nine reports had a general system focus, 80 were disease specific, 10 referred to a specific type of care setting, 22 were thematic and 2 were a combination of two (disease specific for a particular type of care and thematic for a specific type of care). Most reports were from Canada: 49. All 15 countries use one or more OECD indicators. LESSONS LEARNED: The OECD quality indicators have acquired a clear place in national and regional monitoring activities. Some indicators are reported more often than others. These differences partly reflect differences between healthcare systems. Whereas some indicators have become very common, such as cancer care indicators, others, such as mental healthcare and patient experience indicators are relatively new and require some more time to be adopted more widely.
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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 | 0.006 | 0.006 |
| 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 | 0.000 | 0.000 |
| 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".