How accurate are we? A comparison of resident and staff physician billing knowledge and exposure to billing education during residency training
Bibliographic record
Abstract
Background: Practice management is an overlooked and undertaught subject in medical education. Many physicians feel that their exposure to billing education during residency training was inadequate. The purpose of this study was to compare resident and staff physicians in terms of their billing knowledge and exposure to billing education during residency training. Methods: Senior residents and staff physicians completed a scenario-based clinical billing assessment. Posttest surveys were completed to determine exposure to practice management and billing education during training. Results: A total of 16 resident physicians and 17 staff physicians completed the billing assessment. Overall, the billing accuracy of respondents was poor. Staff physicians had a greater percentage of correct billing codes (55.3% v. 37.5%, p < 0.001) and underbilled codes (6.2% v. 3.4%, p = 0.009), with fewer missed billing codes (38.5% v. 59.1%, p < 0.001), compared with resident physicians. The percentage value of correct billings was significantly higher for staff physicians (71.5% v. 56.8%, p = 0.01). In the posttest survey, 100.0% of residents and 79.0% of staff physicians desired more billing education during training. Conclusion: In general, staff physicians billed more accurately than resident physicians, but even experienced staff physicians missed a substantial amount of potential revenue because of billing errors and omissions. The majority of the residents and staff physicians who participated in our study felt that current billing education is both insufficient and ineffective. Incorporating practice management and billing education into residency training is critical to ensure that the next generation of medical trainees possess the financial competence to required to manage a successful medical practice.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".