International Comparability of Patient Safety Indicators in 15 OECD Member Countries: A Methodological Approach of Adjustment by Secondary Diagnoses
Bibliographic record
Abstract
OBJECTIVE: To improve the international comparability of patient safety indicators based on administrative hospital data, adjustment of country-specific rates by a proxy measure of diagnostic coding intensity was tested. DATA SOURCES: Secondary data (numerator and denominator counts of patient safety indicators) based on adults discharged from acute care hospitals between 2006 and 2008 was used. STUDY DESIGN: A retrospective cross-sectional study using hospital administrative data was performed. DATA COLLECTION: Belgium, Canada, Denmark, Germany, Italy, Ireland, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom, and the United States provided data according to detailed instructions. PRINCIPAL FINDINGS: Age- and sex-standardized rates varied across countries. An ordinary least squares regression model was estimated for each Patient Safety Indicator (PSI) using the mean number of secondary diagnoses among denominator cases as the predictor (R(2) =23 percent to 56 percent). Estimated country-specific residuals were linearly transformed into adjusted PSI rates. Variation among age-sex standardized PSI rates decreased substantially after this adjustment. CONCLUSIONS: International comparisons of health system performance based on unadjusted patient safety indicators are problematic due to suspected coding or ascertainment bias. The model could be an interim approach to provide comparable information on hospital quality, with a long-term goal of improving international consistency in diagnostic reporting in administrative data.
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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 | 0.234 | 0.379 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".