Abstract 5: Another Inconvenient Truth: The Pernicious Effects of Hospital Case Volumes on the Accuracy of Hospital Report Cards
Bibliographic record
Abstract
Background: Hospital report cards are published with increasing frequency, but their accuracy is controversial, especially when case volumes are small. Our objective was to determine the relationship between hospital case volumes and the accuracy of hospital report cards using simulation studies. Methods: Monte Carlo simulations were used in a setting such that the true hospital rankings were known with certainty and perfect risk-adjustment was possible. Parameters used to generate simulated datasets were obtained from analyses of 31,000 hospitalized AMI patients in Ontario. We varied the number of patients per hospital from 100 to 2000 in increments of 100. For each scenario we simulated 500 datasets and determined the correlation between the true hospital ranking (determined by hospital-specific random effects) and the observed ranking (determined by observed vs. expected (O-E) or predicted vs. expected (P-E) ratios). Baseline simulations used the observed 30-day AMI mortality of 11%. In sensitivity analysis we explored the impact of using a lower event rate (2% mortality) commonly associated with cardiovascular procedures. Results: Provider volume had a strong effect on the accuracy of hospital report cards. When the event rate was high (11%), provider volume had to exceed 500 before the correlation between known and observed rankings exceeded 80%, and for hospitals with 200 or fewer cases the correlation was <60% (Figure). When the event rate was low (2%), provider volume had to exceed 1200 before the correlation exceeded 80%, and for hospitals with 200 or fewer cases the correlation was <40%. Conclusions: These results highlight the substantial amount of error that occurs when profiling hospitals even in the environment of perfect risk adjustment. These random errors increase with decreasing hospital volume and decreasing event rates. Hospital report cards display acceptable levels of accuracy only when provider volumes exceed those seen in many hospitals.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".