North American efforts to improve quality and build evidence for reimbursement for point-of-care 3D printing
Bibliographic record
Abstract
Motivation: While there is a long history of 3D printed anatomic models and guides created from medical images, there is a transition from medical 3D printing performed almost exclusively by industry to a shared responsibility that includes in-hospital 3D printing within a department of radiology. As the subspecialty grows, there are concerns that too few restrictions have been applied to physicians who create 3D models at the point-of-care. Materials and Methods: To promote quality care that uses 3D printing, the Radiological Society of North America (RSNA) 3D Printing Special Interest Group (SIG) was created. The SIG is actively working to establish guidelines for what clinical scenarios 3D printing is appropriate, to create quality assurance measures for validating the accuracy of 3D printed anatomic models, and to accumulate evidence to be used for support reimbursement strategies for 3D printing as applied to patient care. Results, Discussion and Conclusion: In November 2018, the SIG published its first paper on appropriate clinical use of 3D printed anatomic models for diagnostic use in the care of specific medical conditions including congenital heart disease, craniomaxillofacial pathologies, genitourinary pathologies, musculoskeletal disease, vascular disease, and breast pathologies. On July 1, 2019, the American Medical Association (AMA) CPT Editorial Panel released four Category III CPT codes for 3D printing (Figure). To gain evidence towards reimbursement, the RSNA SIG leadership team is working on implementation guidelines and vehicles for important data collection regarding 3D printed anatomic models and guides. 3D printing at the point-of-care requires appropriate guidelines to maximize quality and secure reimbursement. North American initiatives are ongoing to meet these clinical needs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".