Internal Medicine Physicians' Knowledge of Health Care Charges
Bibliographic record
Abstract
BACKGROUND: Concerns over the rising costs of health care have increased interest in educating residents about the cost impact of medical decisions. While many programs educate residents about the effectiveness of care, little is known about how well residents and faculty know charges of diagnostic tests or both groups' interest in this topic. METHODS: We surveyed internal medicine residents and faculty at an academic tertiary care hospital. Both groups rated their agreement with a series of statements about health care charges on a Likert scale of 1 (strongly disagree) to 9 (strongly agree), and they estimated the charges for 15 commonly ordered diagnostic tests. Estimates within 25% of the true charge were considered correct. The Wilcoxon rank sum test was used to compare responses between residents and faculty. RESULTS: Seventy of 126 eligible participants (56%) returned surveys. Participants showed poor knowledge of health care charges but expressed a desire to learn more. Physicians also felt that cost-effectiveness should be considered when ordering diagnostic tests, although faculty members felt more strongly about this than did residents. In estimating the charges for diagnostic tests, less than a quarter of all responses were within 25% of the true charge. CONCLUSIONS: Internal medicine physicians poorly estimate the charges for diagnostic tests but have a strong desire to improve their knowledge, suggesting a possible intervention to improve the cost-effectiveness of medical care.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".