Use of Case Mix Tools for Utilization Management and Planning
Bibliographic record
Abstract
An aging population, new technologies and drugs, and tightening constraints on financial and human resources have placed increased demands on hospitals to meet health care needs. Canadian hospitals, health regions, and ministries are placing a high priority on the evaluation of appropriateness and efficiency of medical care and on the proactive and targeted reduction of inefficiencies. The CMG+ grouping methodology is a case mix tool produced by the Canadian Institute for Health Information (CIHI) to analyze the acute-care inpatient population in Canada. CMG+ methodology can be applied, for example, to utilization management. The expected length of stay (ELOS) indicator can be used as a benchmark for assessing the length-of-stay distribution of various subgroups of patient activity. It can also be used to compute potentially conservable days and to identify those patient subgroups where gains in efficiency may be realized. We describe CMG+ utilization management using this and other examples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".