Is there an association between hospital occupancy and quality of care?
Bibliographic record
Abstract
Statement of the problem. Hospital occupancy is the number of inpatients divided by the number of beds. It has risen over the last two decades in many countries. This thesis will determine if there is an association between hospital occupancy and quality of care. Setting. The Ottawa Hospital - Civic Campus, a tertiary care teaching hospital in Ottawa, Canada between January 1, 1993 and July 31, 1999. Methods. Daily rates of hospital occupancy and several quality of care indicators were derived using administrative databases. Indicators included: efficiency outcomes (emergency room (ER) delay, hospital length of stay (LOS), off service transfers, bed to bed transfers, and operating room (OR) cancellations); inpatient outcomes (deaths, cardiac arrests, c. difficile infections, medication errors, and falls); and outpatient outcomes (7- and 30-day visits to any ER, urgent readmissions to any hospital, and deaths). Autoregressive Integrated Moving Average (ARIMA) time-series modeling was used to test the association between hospital occupancy and each of the outcomes. Results. Significant, positive associations were identified between daily occupancy rates and the following outcomes: ER delay, off service transfer, bed to bed transfers, and the proportion of patients dying within 30 days of discharge. Significant negative associations were identified between occupancy and length of stay and hospital deaths. Conclusion. This study demonstrates that quality of care is associated with hospital occupancy. Further research is required to validate the clinical importance of the efficiency indicators used and to adjust occupancy for case-mix.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".