Measuring hospital outcomes using administrative data: comparing logistics vs multilevel methods
Bibliographic record
Abstract
Over the last years, the growing desire for quality improvement in medical care has led to public reporting of providers’ performance using league tables. Nowadays, clinical outcomes are systematically incorporate within multidimensional performance measurement systems and reported publicly in some countries including USA, England, Canada and Italy. However, there are still several methodological and practical issues related to the use of risk-adjusted outcomes for benchmarking purposes, such as which statistical methods for risk-adjustment among traditional regression or multilevel models have high reliability for differentiating hospital performance. This work focuses on the assessment of the performance between the traditional regression models and hierarchical methods in terms of degree of reliability of risk-adjusted estimates using administrative data. The data stem from the administrative care databases of Lombardy region (Italy) of the year 2013 and the outcomes of interest are 30-day mortality and 30-day readmissions for all causes. We will analyze the performance of each risk-adjustment approach in terms of several aspects such as accuracy of estimates, ability in capturing systematic differences and correctly identify outliers on the basis of different scenarios using administrative data. This study will contribute to provide more definitive recommendations on the appropriate methodology aimed at hospital profiling.
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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 | 0.062 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".