Administrative Data Feedback for Effective Cardiac Treatment
Bibliographic record
Abstract
CONTEXT: Hospital report cards are increasingly being implemented for quality improvement despite lack of strong evidence to support their use. OBJECTIVE: To determine whether hospital report cards constructed using linked hospital and prescription administrative databases are effective for improving quality of care for acute myocardial infarction (AMI). DESIGN: The Administrative Data Feedback for Effective Cardiac Treatment (AFFECT) study, a cluster randomized trial. SETTING AND PATIENTS: Patients with AMI who were admitted to 76 acute care hospitals in Quebec that treated at least 30 AMI patients per year between April 1, 1999, and March 31, 2003. INTERVENTION: Hospitals were randomly assigned to receive rapid (immediate; n = 38 hospitals and 2533 patients) or delayed (14 months; n = 38 hospitals and 3142 patients) confidential feedback on quality indicators constructed using administrative data. MAIN OUTCOME MEASURES: Quality indicators pertaining to processes of care and outcomes of patients admitted between 4 and 10 months after randomization. The primary indicator was the proportion of elderly survivors of AMI at each study hospital who filled a prescription for a beta-blocker within 30 days after discharge. RESULTS: At follow-up, adjusted prescription rates within 30 days after discharge were similar in the early vs late groups (for beta-blockers, odds ratio [OR], 1.06; 95% confidence interval [CI], 0.82-1.37; for angiotensin-converting enzyme inhibitors, OR, 1.17; 95% CI, 0.90-1.52; for lipid-lowering drugs, OR, 1.14; 95% CI, 0.86-1.50; and for aspirin, OR, 1.05; 95% CI, 0.84-1.33). In addition, adjusted mortality was similar in both groups, as were length of in-hospital stay, physician visits after discharge, waiting times for invasive cardiac procedures, and readmissions for cardiac complications. CONCLUSIONS: Feedback based on one-time, confidential report cards constructed using administrative data is not an effective strategy for quality improvement regarding care of patients with AMI. A need exists for further studies to rigorously evaluate the effectiveness of more intensive report card interventions.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".