Performance of the ACG Case-Mix System in Two Canadian Provinces
Bibliographic record
Abstract
BACKGROUND: While the adjusted clinical group (ACG) system has been extensively validated in the United States, its use in other developed nations has been limited. This article examines the performance of the system in 2 Canadian provinces and assesses the extent to which ACGs can account for same-year and next-year health care expenditures. METHODS: The study population included all residents of Manitoba and British Columbia who were continuously enrolled in the provincial health plans from April 1, 1995, to March 31, 1997. ACGs were assigned through diagnoses from fee-for-service physician claims and hospital separation records. "Physician" costs were calculated from the fee-for-service tariffs, and for Manitobans, "total" costs were also computed by combining physician and hospital costs. Linear regression was used to examine the ability of the ACG system to explain variation in individual costs (truncated at the 99th percentile). RESULTS: The British Columbia and Manitoba data were generally acceptable, with fewer than 2% rejected diagnoses. Higher costs were associated with both the accumulation of morbidities and their relative severity. For physician costs, the ACG system explained approximately 50% and approximately 25% of the variation in same-year and next-year truncated costs, respectively. For total costs, the system explained approximately 40% and approximately 14% of these respective costs. CONCLUSIONS: The application of ACGs in Canada is feasible using existing data. The ability of the ACG system to explain variation in costs is similar to that found in US health systems. While application of ACGs in Canada shows promise, further research is required to examine how closely they reflect population morbidity burdens and health care needs.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".