Cost-Benefit Analysis of Ultrasonography in the Hand Clinic
Bibliographic record
Abstract
BACKGROUND: Despite previous studies demonstrating the benefit of office-based ultrasonography for musculoskeletal evaluation, many hand surgery clinics have yet to adopt this practice. The authors conducted a cost-benefit analysis of establishing an ultrasound machine in a hand clinic. METHODS: The authors used the Medicare Physician Fee Schedule, Physician/Supplier Procedure Summary, and Physician Compare National Downloadable File databases to estimate provider reimbursement and annual frequency of office-based upper extremity-related ultrasound procedures. Ultrasound machine cost, maintenance fees, and consumable supply prices were gleaned from the literature. The primary outcomes were net cost-benefit difference and benefit-cost ratio at 1 year, 5 years, and 10 years after implementation. Sensitivity analyses were performed by varying factors that influence the net cost-benefit difference. RESULTS: The estimated total initial expense to establish ultrasonography in the clinic was $53,985. The overall cost-benefit difference was -$49,530 per practice at the end of the first year (benefit-cost ratio, 0.3), -$1049 after 5 years (benefit-cost ratio, 1.0), and $52,022 after 10 years (benefit-cost ratio, 1.4). Benefits primarily accrued because of physician reimbursements. One-way sensitivity analysis revealed machine price, annual procedure volume, and reimbursement rate as the most influential parameters in determining the benefit-cost ratio. Ultrasonography was cost beneficial when the machine price was less than $46,000 or if the billing frequency exceeded six times per week. A societal perspective analysis demonstrated a large net benefit of $218,162 after 5 years. CONCLUSIONS: Implementation of office-based ultrasound imaging can result in a positive financial return on investment. Ultrasound machine cost and procedural volume were the most critical factors influencing benefit-cost ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".