Severity of illness in the case-mix specification and performance: A study for Italian public hospitals
Bibliographic record
Abstract
Background: Public hospitals’ expenditures in Italy is approximately 45% of total public health financing. The reduction of public debt requires reducing total public health, as well as hospital expenditures in the public sector. Past health reforms introduced rules to improve the efficiency in controlling hospital costs with a better use of resources. The objective of this study is to derive technical efficiency as a performance measurement in the directly managed public hospitals in Italy under different case-mix specifications, as well as to discover the effect of it on technical efficiency. Methods: Two different Data Envelopment Analysis (DEA) models are solved. To control for the influence of the case-mix complexity/severity of illness on technical efficiency, the distributions of DEA efficiency scores are compared applying statistical tests developed in the non-parametric efficiency analysis. Results: On average, in the year 2007, the technical efficiency in the sample is lower (0.8071) in model B (output mix with weighted Case Mix Index) than in model A (0.8748). The bootstrap-corrected efficiency scores of models B and A are respectively 0.7185 and 0.8106. On average, the case mix index in the sample is 0.87859. Statistical tests confirm that the differences in the efficiency scores distribution are statistically significant, confirming that treatment complexity has influenced technical efficiency. At the individual hospital level, the effect is more evident, modifying the rank and the technical efficiency of the hospitals. Conclusions: The different case-mix specifications adjusted with Case Mix Index, generate statistically significant differences in the distribution of the efficiency scores. This evidence permits us to conclude that the performance of the Local Health Trust’s directly managed public Italian hospitals is influenced by the hospitals’ case-mix severity/complexity. As a policy indication, we can observe that the need for policy makers and hospital managers to reduce hospital costs conflicts with the need to guarantee an optimum level of hospital resources with different case-mix complexities of the treated cases.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".