Quantitative and qualitative impact of physician assistants in a Canadian urology setting
Bibliographic record
Abstract
INTRODUCTION: Physician assistants (PAs) are healthcare professionals who act as physician extenders. PAs are being used more and more in a wide variety of clinic settings throughout Canada to increase access to healthcare and reduce cost. We set out to determine the impact of PAs on a tertiary care center urologic oncology practice. METHODS: We reviewed Ontario Health Insurance Plan (OHIP) billing codes since the introduction of PAs for two attending urologists at Princess Margaret Cancer Centre. Data were grouped into early experience and established experience. In addition, questionnaires were electronically distributed among nurses, physicians, residents, and fellows who work with PAs in clinic. Patient visits conducted by PAs were tracked for one quarter to estimate the amount of annual patients seen by PAs. The costs associated with PAs are presented as recommendations for a new graduate PA hire. RESULTS: On average, PAs increased clinic volume by 11.3 patient visits per day. Furthermore, they individually care for an average of 24 patients per day. PAs did not represent a financial burden on the urology practice plan (revenue gain of $16 800). Our questionnaire demonstrated that PAs were capable healthcare professionals, who decreased workload and contributed to resident/fellow education. CONCLUSIONS: PAs in a Canadian urology practice allow for more patient visits, decrease in physician workload, and positively impact trainee education. PAs saw more patients in clinic than clinic growth, thereby decreasing physician, fellow, and resident workload. The offset of the increase in patient visits made the PAs a cost-neutral investment.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".