Variation in length of stay within and between hospitals
Bibliographic record
Abstract
Background and objective: Variation in the delivery of health care services and the lack of association between greater utilization and higher quality care signal inefficient, low value care. The extent to which patient and hospital variables can explain variation in hospital length of stay is unclear. Methods: We examined hospital inpatient length of stay using data from 684 hospitals and 5.4 million discharges in the 2007 Healthcare Cost and Utilization Project’s Nationwide Inpatient Sample. We used a mixed effects model with a random effect for hospitals to quantify variation in length of stay due to differences within and between hospitals. Results: The interquartile range of hospital mean LOS was 3.4 days (3.3-6.7). Fifty-nine percent of the overall variation in length of stay remained unexplained after adjustment for discharge-level disease status, illness-severity, regional poverty, hospital-level contextual factors (e.g. proportion of patients from low-income ZIP-codes, proportion uninsured), and structural variables (e.g. teaching status, urban or rural location). Seventy-seven percent of the explainable variation was due to differences between hospitals. Conclusion: These findings indicate that wide variability in length of stay persists after adjustment for patient and hospital variables, signaling an opportunity for improved productivity and efficiency in the delivery of health care.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".