Surgical case costing: trauma is underfunded according to resource intensity weights.
Bibliographic record
Abstract
OBJECTIVE: To determine whether rate-based funding using resource intensity weights (RIWs) adequately represents trauma case costs. DESIGN: A prospective time-in-motion resource utilization pilot study to assure the effectiveness of the computerized hospital Transition-One data acquisition system, followed by a retrospective observational case costing study. Patient costs with no identifing data were used, and all costs were tabulated as mean cost per group. SETTING: London Health Sciences Centre, London, Ont., a tertiary care "lead" trauma hospital. PATIENTS: A modified random selection of 4 control case mix groups (CMGs) of surgical patients for the fiscal year 1996-97. The trauma group was selected as a representative resource-intensive CMG. Each patient was assigned to a CMG by Health Records according to the most responsible diagnosis. OUTCOMES MEASURES: Total case costs were tabulated for each patient then combined for a mean case cost per CMG. The RIW assignments for each patient were combined to create a mean RIW per CMG and mean length of stay per CMG. RESULTS: There was no statistically significant difference between the control surgical CMGs and the trauma CMG for mean RIW-adjusted length of stay per CMG, but there was a significant difference (p < 0.0001) between the control CMGs and the trauma CMG for RIW-adjusted mean case cost per CMG. CONCLUSIONS: RIWs underrepresent trauma case costs by a factor of 3.5, which could result in underfinding and potential fiscal difficulties for leading trauma hospitals as has occurred in the United States.
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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 | 0.010 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".