Impact of a Physician Extender on Outpatient Rheumatology Capacity and Wait Times
Bibliographic record
Abstract
Objectives To determine the impact of an unlicensed international medical graduate (IMG) in a physician extender role within a multi-doctor urban rheumatology clinic on the number and average wait times of new patient consultations. Methods A retrospective quality improvement framework was adopted. Starting September 2022, an IMG served as a physician extender, assisting with new consultations 1 day per week for each of the 4 rheumatologists in the clinic. All new consultations seen between September 1st, 2021, and August 31st, 2023, were included in the study. The pre-IMG period was established as September 1st, 2021, to August 31st, 2022, and the post-IMG period was from September 1st, 2022, to August 31st, 2023. For each new consult, the referral date and date seen in clinic were extracted from the local electronic medical record system with the wait time calculated as the difference between the referral date and the new consultation date. These data were plotted and analyzed using run chart statistics. Results In the pre-IMG period, the average number of new consults seen per month was 108.0 with an average wait time of 105.1 days compared to 110.6 new consults and 84.6 days in the post-IMG period across all physicians. In the post-IMG period, doctor-specific outcomes varied. For Physician 1, the average wait time decreased by 15.9 days with 121 more new consults seen compared to the pre-IMG period. However, for Physicians 2 and 3, the number of new consultations remained stable but average wait time decreased by 35.9 and 52.5 days respectively. Physician 4 saw fewer new consultations post-IMG with average wait time remaining constant (Figure). Conclusion The use of an IMG in a physician extender role showed mixed results. While wait times reduced in 3 of 4 physicians, new consults did not reliably increase in tandem. While employing an IMG in similar roles may increase clinic capacity and timely access to care, outcomes may be related to each physician’s unique approach in implementing this physician extender. Future research should consider these different approaches.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".