Fee-Scheduleincreasesincanada: Implication for Service Volumes among Family and Specialist Physicians
Bibliographic record
Abstract
Physician spending has substantially increased over the last few years in Canada to reach $27.4 billion in 2010. Total clinical payment to physicians has grown at an average annual rate of 7.6% from 2004 to 2010. The key policy question is whether or not this additional money has bought more physician services. So, the purpose of this study is to understand if we are paying more for the same amount of medical services in Canada or we are getting more bangs for our buck. At the same time, the paper attempts to find out whether or not there is a productivity difference between family physician services and surgical procedures. Using the Baumol theory and data from the National Physician Database for the period 2004–2010, the paper breaks down growth in physician remuneration into growth in unit cost and number of services, both from the physician and the payer perspectives. After removing general inflation and population growth from the 7.6% growth in total clinical payment, we found that real payment per service and volume of services per capita grew at an average annual rate of 3.2% and 1.4% respectively, suggesting that payment per service was the main cost driver of physician remuneration at the national level. Taking the payer perspective, it was found that, for the fee-for-service (FFS) scheme, volume of services per physician decreased at an average annual rate of -0.6%, which is a crude indicator that labour productivity of physicians on FFS has fallen during the period. However, the situation differs for the surgical procedures. Results also vary by province. Overall, our finding is consistent with the Baumol theory, which hypothesizes higher productivity growth in technology-driven sectors.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".