Variations in Revenue Generation for the Care of Orthopaedic Trauma Patients
Bibliographic record
Abstract
BACKGROUND: In the background of increasing competition between trauma centers, this study investigated the relative reimbursement of trauma care provided in an urban trauma setting, comparing patients previously unknown (new) to the system, representing potential sources of new revenue, and those who were known (established), having received medical care previously in the same system. METHODS: A retrospective review of 440 patients with high-energy fractures at a single level 1 trauma center was conducted. Payment to charge (P/C) ratios for professional and facilities services within 6 months of injury were calculated. RESULTS: Mean professional charges per patient were $35,522 and $30,639 (P = 0.11), between new and established patients, respectively, whereas mean professional payments were statistically different, $7,894 and $4,365 (P < 0.001). Mean differences in P/C for facilities payments for new and established patients were not statistically significant, but professional P/C was higher in new patients (P < 0.001), consistent with better insured patients. DISCUSSION: Insurance companies reimburse for professional or facilities services with statistically different P/C ratios. Treating new patients at our institution likely benefits our institution by offering exposure to a more favorable payer mix and more complex patients. LEVEL OF EVIDENCE: Retrospective level III.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".