Comparing and Monitoring Risk-Adjusted Hospital Performance Measures: A Weighted Estimating Equations Approach
Bibliographic record
Abstract
Background. There is a great deal of interest in evaluating hospital performance in order to monitor and improve health care quality. Increasingly, risk-adjusted performance measures are available to the public and statistical approaches for estimating these measures are considered. Some methods in use currently are based on 3-year aggregates of data since a small number of cases may lead to imprecise estimates and make it hard for stakeholders to detect differences across hospitals over time. However, if quality changes over time, a measure based on these data is a biased estimate of present performance. Methods. We present an alternative approach (weighted estimating equations [WEE]) for combining historical data in estimation that regulates the tradeoff between bias and precision in the measure of present performance. The WEE approach uses all available historical data through estimating functions that down-weight past data. Results. We compare the WEE approach to two current practices using a realistic dataset of the mortality of patients following an elective percutaneous coronary intervention procedure in New York State who meet certain criteria. The width of the uncertainty interval in the realistic example is up to 65% smaller and the difference is more pronounced for hospitals with a small number of cases. Conclusions. The advantage of this approach extends from the example dataset to other datasets. The WEE approach uses all available data rather than data from an arbitrary 3-year window. The effect of borrowing strength from historical data is a more precise estimate of present performance than current practices. Its advantages are important for the comparison of other aspects of medical performance, including surgical or medical practitioner performance.
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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 | 0.056 | 0.153 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".