The same, only different: Using physician billing data from four provincial payment systems to describe family physician practice patterns in Canada.
Bibliographic record
Abstract
ObjectivesIn Canada physician payment systems and associated billing data vary across provinces. We used linked billing data to develop comparable measures of family physician (FP) service volume, continuity, and comprehensiveness in each of four provinces, with the goal of describing changing patterns over time, relevant to workforce planning policy. ApproachWe accessed linked population and physician registry data, vital statistics, physician billing data, hospital and emergency department records, and (where available) laboratory and prescription drug records in four Canadian provinces from 1996/7 to 2017/8. We tracked changes in primary care physician service volume (patient visits), continuity, and comprehensiveness over two decades, and explored the impacts of career stage and graduation cohort on patterns observed. We also quantified changes in the volume of services that required FP coordination, review, administration, and/or follow-up, reporting changes in service volume per-capita, per community-based family physician and per physician visit. ResultsVisit volume and continuity per provider fell over time in the four provinces examined. Visits increased with years in practice until mid-to-late-career and declined into end-of-career. We found no relationship between graduation cohort and practice volume, continuity, or comprehensiveness of care. Over this time period, the number of FPs per-capita has grown, but the number in comprehensive, community-based practice has remained constant. While primary care visits have declined, the number of prescriptions, lab tests, emergency department visits, and specialist visits per capita have all increased. When expressed per community-based FP, and particularly per community-based FP patient visit, the increases in workload range from 36.1% for lab tests to 55.0% for ED follow-up. Increases in service volume are greatest among patients ages 80 and older, a rapidly-growing population segment. ConclusionLinked data capturing changes in practice patterns and workload suggest that even with an increasing per-capita supply of family physicians, additional resources will be needed to ensure all patients can access comprehensive primary care. Information on the various roles family physicians fill and demographic change will strengthen workforce planning.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".