Primary Care Physician Compensation Reform: A Path for Implementation
Bibliographic record
Abstract
The recent report by Alberta’s Blue ribbon panel on the province’s finances denounced Alberta’s Fee-for-Service (FFS) model as a significant source of inefficiency and cost within the health system, going as far as to suggest legislating a non-FFS model. If pursued, Alberta would be the first province since the start of Canadian Medicare to fully shift away from FFS. There has already been considerable study and debate on which FFS alternative is most appropriate in different care settings, but little discussion has been generated on the best practices for implementing such a reform. This paper explores physician compensation reform work for physicians, patients and government alike, focusing on compensation of primary care physicians. Since the early 2000s, family physician costs in Alberta have significantly outpaced specialist costs, which has drawn attention to reconsidering compensation models for primary care specifically. Alberta has two options beyond the FFS-dominant status quo to consider: (1) legislate in an alternative payment plan (APP) to replace FFS, or (2) phase out FFS by implementing policies that make APP a progressively more attractive option for primary care physicians. This paper provides a scan of emerging practice and evidence from across Canada. The practical and political lessons learned point to the need for a systematic phase-out of the FFS payment model in primary care. However, there is no evidence to suggest that legislating in a mandatory replacement of FFS is the optimal way forward. Experience across the country demonstrates that physicians when presented with a viable alternative to FFS will uptake, but for that alternative to be viable the status quo must not retain the upper-hand.
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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 | 0.036 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.027 | 0.023 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".