Comparing case-mix measures in Ontario, Canada’s primary care models: Assessment of Canadian Institute for Health Information
Bibliographic record
Abstract
Context Case-mix instruments are an important tool in primary care for program evaluation, health system planning, and compensation. There are two different approaches for measuring case-mix that are in common use in Ontario: Johns Hopkins ACGs and the CIHI Population Grouper. The Johns Hopkins ACGs have been used extensively in research applications in Ontario, while the CIHI Population Grouper has been used more commonly for administrative and governmental applications including adjustment of capitation payments. Little is known about how the measures from these two methodologies compare with one another. Objectives To evaluate and compare the case-mix and service utilization of patients who are enrolled in different primary care payment models, and to compare the two approaches for classifying patient case-mix. Study design and analysis In this cross-sectional study the health status, expected resource use, and health conditions of patients in each of the primary care payment models will be compared using measures from both the Johns Hopkins ACGs and the CIHI population grouper. Population studied All residents of Ontario, Canada with a valid health card who are formally or virtually enrolled in a primary care enrolment model on March 31, 2023 or are virtually enrolled to a fee for service (FFS) physician. Outcome Measures The primary outcome is morbidity/case-mix as measured by resource intensity weights output by the two population groupers. Secondary outcomes are the presence of specific health conditions and health care utilization. Patient characteristics include age, sex, income quintile, and geographic location. Results Initial analysis using a beta version of the CIHI grouper found that the CIHI grouper had greater specificity in identifying chronic conditions and predicted a higher proportion of total costs compared to the ACG measures. Both measures performed equally well at predicting health care utilization and both under-predicted high-cost users. This analysis will be updated using the most recent version of the CIHI population grouper and will include comparisons between patients in different primary care payment models. Conclusion The increasing use of the CIHI grouper in the Ontario necessitates a more integrated use of this approach in primary care research and evaluation. This study will provided guidance on which aspects of the CIHI grouper are best suited to be reported on and used on a routine basis.
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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 | 0.056 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".