Real-time operational feedback: daily discharge rate as a novel hospital efficiency metric
Bibliographic record
Abstract
BACKGROUND: Part of delivering quality care means providing it in a timely, efficient manner. Improving the efficiency of care requires measurement. The selection of appropriate indicators that are valid and responsive is crucial to focus improvement initiatives. Indicators of operational efficiency should be conceptually simple, generated in real time, calculated using readily available hospital administrative data, sufficiently granular to reveal detail needed to focus improvement, and correlate with other valid indicators of operational efficiency. DISCUSSION: In this paper, the authors propose daily discharge rate as a novel real-time metric of hospital operational discharge efficiency and compare it with average length of stay. The authors also suggest the use of control charts as an effective way to present daily discharge rate data to clinicians and managers in real time to prompt actionable improvements in discharge efficiency. CONCLUSION: The authors conclude that daily discharge rate has the potential to drive timely improvements in the discharge process and warrants consideration and further study by others interested in improving hospital operational efficiency and the delivery of quality 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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".