Quality indicators used by trauma centers for performance measurement
Bibliographic record
Abstract
BACKGROUND: To describe the quality indicators (QIs) that trauma centers use for quality measurement and performance improvement. Measuring and reporting quality of care is a critical step to improve the quality of care. QIs compare actual trauma care against ideal criteria and identify patients in whom care may have been suboptimal and should be further reviewed. METHODS: Three hundred thirty verified trauma centers in the United States, Canada, Australia, and New Zealand had their websites reviewed and leadership surveyed regarding QI use. The indicators identified were classified according to definition specifications, phase of care, Institute of Medicine aims, and contents. RESULTS: Two hundred fifty-one centers responded to the survey (76%) and the majority (97%) indicated that they use QIs. We obtained 10,587 QIs from 262 centers (survey responses and website review) of which 1,102 were unique indicators. The QIs primarily assessed the safety (49%), effectiveness (32%), efficiency (27%), and timeliness (22%) of hospital processes (64%) and outcomes (24%). The majority of indicators were used by a small number of centers (551 of 1,102 unique indicators used by single centers). CONCLUSION: Our study provides the first description of the QIs used by verified trauma centers in four high-income countries with similar systems of trauma care. The majority of trauma centers measure QIs designed to examine the safety, effectiveness, efficiency, and timeliness of hospital processes and outcomes. Opportunities exist to standardize existing QIs to allow broader implementation and develop new QIs to examine patient-centered care and equality of care.
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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 | 0.069 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".