Bibliographic record
Abstract
Canadian surgeons are faced with looming demographic challenges. Shortages in surgical manpower cannot be addressed expediently due to 14-year university requirements. A potential solution is to increase the efficiency of surgeons already in practice. Physician assistants (PA’s), may play a role in this regard by allowing surgeons to concentrate on their core competency, namely operating. The purposes of this investigation are to explore the inefficiencies in a current Canadian surgeon’s practice, examine the feasibility of PA employment and evaluate the financial impacts. The study was performed in 3 parts. In part 1, operating room plastic surgery Surgiserver® data for 10 years leading up to 2005 were analyzed to determine daily operating time actually used. In part 2, 4 months of detailed time series data were captured prospectively for every patient care event. The data was analyzed using SPSS (ver. 11.5) to determine the percentage and types of events that could be delegated to a PA. In part 3, PA hiring scenarios were developed using formal business case analyses. Over 3,635 days, mean operating time used in a 10-hour surgical day was 5.93 hours. Of the 806 patients seen in 13 clinics, 53.5% could have been cared for by a PA. In the minor procedure area, 48.8 % of surgical time was spent performing non-essential, PA compatible work. In the main OR, 25.9% of surgical time was PA compatible. Considering the weekly mix of activities, a PA could increase surgical productivity by 36.7%. Hiring a PA was neutrally cost effective at the 37% productivity increase level. However, much greater discounted incremental cash flows, internal rates of return (IRR) and return on investments (ROI) were achieved when PA hiring allowed one surgeon to run 2 OR’s simultaneously. Employing PA’s, in conjunction with increasing OR capacity, have the potential to markedly increase the capability of surgeons to deal with lengthy wait lists in a cost effective manner. McKibbin RC. Cost-effectiveness of physician assistants: A review of recent evidence. Pa J 1978; 8(2):110-115. Maxfield RG. Use of physician's assistants in a general surgical practice. Am J Surg 1978; 131(4):504-508. Kaissi A, Kralewski J, Dowd B. Financial and organizational factors affecting the employment of nurse practitioners and physician assistants in medical group practices. J Ambul Care Manage 2003; 26(3):209-216.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".