High-volume general practitioners in Alberta: a descriptive analysis
Bibliographic record
Abstract
BACKGROUND: Alberta is considering capping daily fee-for-service physician billings, but little is known about high-volume practice in the province and its impact on patient health outcomes. In this initial study, we conducted a descriptive analysis of general practitioners' patient volumes and billing practices in relation to associated practitioner demographic characteristics. METHODS: We conducted a retrospective descriptive analysis of the associations of practitioner characteristics, including full-time versus non-full-time practice, provider sex, years in practice, geographic location and international medical graduate status, with high-volume (> 50 visits/d) practice using general practice billing data from 2011 to 2016. Use of general practitioner service codes was described and compared by general practitioner volume status, with adjustment for physician demographic characteristics and geographic parameters. RESULTS: We included 3465 general practitioners practising fee-for-service in Alberta between 2011 and 2016, of whom 233 (6.7%) were identified as high-volume providers. Physicians who had been in practice longer (odds ratio [OR] 1.04 per year, 95% confidence interval [CI] 1.02-1.05) and international medical graduates (OR 1.89, 95% CI 1.40-2.54) were more likely to exceed 50 patient visits/day. Female physicians were less likely to exceed 50 patient visits/day (OR 0.14, 95% CI 0.07-0.28). Rural practice location was negatively associated with high-volume practice (OR 0.87, 95% CI 0.79-0.95) when we controlled for zone within the province. Zone 5 (North) was associated with high-volume practice (OR 1.95, 95% CI 1.06-3.58). Less than full-time practice was prevalent (1836 providers [53.0%]). High-volume general practitioners billed fewer service codes requiring longer visits, except for the most highly remunerated code (patients with complex health issues). INTERPRETATION: These results can inform policy-makers when considering payment system changes. Our next step is to examine the association of high-volume practice with outcomes important to patients, such as evidence of treatment failure (emergency department visits and hospital admissions) for conditions sensitive to primary care management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".