Exploring the Impact of Physician Assistants on Orthopedic Surgery Service Efficiency: A Literature Review
Bibliographic record
Abstract
Introduction: Orthopedic surgery in Canada faces increasing patient volumes, long wait times, and a shortage of healthcare providers and resources. Physician Assistants (PAs) are increasingly becoming a consideration as a solution to improve healthcare delivery and surgical service efficiency, and continuity of care across the continuum of healthcare. Objectives: This literature review aims to evaluate the impact of PAs on efficiency metrics within orthopedic surgery services and discover what metrics could be positively affected. Methods: A comprehensive literature search was conducted using the University of Manitoba libraries, PubMed and MEDLINE Ovid databases. Inclusion criteria included peer-reviewed, published in English, within the past 25 years, and focus on physician assistants in the orthopedic surgery settings. Five studies – three Canadian and two American – met the criteria and were reviewed for common outcome themes. Results: Across five studies, PAs were found to have a positive impact which could be categorized into five main common themes. Increased surgical throughput and reduced wait times, operating room efficiency and surgeon time optimization, postoperative care and length of stay reduction, cost-effectiveness and resource optimization, and high patient and provider satisfaction. Conclusion: Physician assistants improve efficiency metrics such as surgical throughput, wait times, operating room efficiency, postoperative care, and provider/patient satisfaction, and provide a cost-effective solution to the challenges faced in orthopedic surgery in Canada. Future research should focus on Canadian multi-centre designs, and standardized outcome measures to further validate their impact.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".