Unsupervised Procedures by Surgical Trainees: A Windfall for Private Insurance at the Expense of Graduate Medical Education
Bibliographic record
Abstract
BACKGROUND: Surgical faculty cannot always be present while trainees perform minor procedures. Fees are not obtained for these unsupervised services because Medicare rules do not allow residents and fellows to bill. Medicare already supplements hospitals via medical education funds and thus reimbursement for trainee services would constitute double billing. Private insurance companies, however, do not supplement trainees' salaries and thus benefit when they are not charged for these procedures. The objective is to determine whether significant revenue is lost to private insurers for unsupervised procedures performed by surgical trainees. METHODS: We retrospectively evaluated a prospective database of procedures performed by residents and fellows from March 1998 through 2007. All procedures were entered by the trainees into a computerized electronic note system. Unsupervised procedures were not billed to insurance carriers. RESULTS: During the study period, 14,497 minor procedures were performed without attending supervision, of which 13,343 had valid current procedural terminology codes. Total charges for these procedures would have been $10,096,931. For patients with private insurance companies (PICs), $6,876,000 could have been billed. Using our historic collection ratios, $2,269,083 in revenue was lost, or $232,726 annually. CONCLUSIONS: Trainees perform a significant number of unsupervised procedures on patients with private insurance without charge. This pro bono service represents a significant amount of lost income for teaching institutions. Private insurance companies benefit financially from Medicare billing regulations without contributing to education. Billing for these services might help offset the costs of graduate medical education.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".