A province-wide study of the association between hospital resource allocation and length of stay
Bibliographic record
Abstract
The relationship between hospital resource allocation and clinical efficiency is poorly understood. Within the single-payer healthcare system in Ontario, Canada, the association between hospital spending patterns and length of stay was studied using data from 1117090 patient discharges in 1997/8 at 162 of 171 acute care hospitals. A weighted regression model was created using an overall hospital length of stay index (actual length of stay divided by predicted length of stay) as the dependent variable. Control variables included: hospital size, teaching activity, occupancy rate, rural location and geographic region. Four independent spending variables were defined as a percentage of total hospital spending: nursing, ambulatory care, administration and support, and diagnostics and therapeutics. The reduced regression model had an r-squared of 0.45. Across all spending variables, hospitals spending relatively too little or too much had significantly longer length of stay. Hospitals' overall pattern of resource allocation was also significantly associated with length of stay. Thus, measurable clinical effects can be seen with resource allocation decisions made by hospital management, supporting the need for rigorous decision-making processes. Future research should focus on exploring the nature of this relationship and the potential interdependencies among hospital services that cause this effect.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".