Indicators of quality of care for patients with acute myocardial infarction
Bibliographic record
Abstract
BACKGROUND: There is a wide practice gap between optimal and actual care for patients with acute myocardial infarction in hospitals around the world. We undertook this initiative to develop an updated set of evidence-based indicators to measure and improve the quality of care for this patient population. METHODS: A 12-member expert panel was convened in 2007 to develop an updated set of quality indicators for acute myocardial infarction. The panel identified a list of potential indicators after reviewing the scientific literature, clinical practice guidelines and other published quality indicators. To develop the new list of indicators, the panel rated each potential indicator on 4 dimensions (reliability, validity, feasibility and usefulness in improving patient outcomes) and discussed the top-ranked quality indicators at a consensus meeting. RESULTS: Consensus was reached on 38 quality indicators: 17 that would be measurable using chart-abstracted data and 21 that would be measurable using administrative data. Of the 17 chart-review indicators, 13 address pharmacologic and nonpharmacologic care delivered to patients in hospital. In-hospital mortality was recommended as a key outcome indicator. Three system indicators were recommended to measure the collaborative responsiveness of the health care system from the call for help to intervention. It was recommended that hospitals strive for a minimum target benchmark of 90% or greater on process-of-care indicators. INTERPRETATION: Implementation of strategies by clinicians and hospitals to meet target benchmarks on these quality indicators could save the lives of many individuals with acute myocardial infarction.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".