Overview of different reimbursement strategies among contrast-enhanced mammography (CEM) expert centers on a global level – A survey study
Bibliographic record
Abstract
OBJECTIVES: To perform an international survey among global expert breast radiologists regarding contrast-enhanced mammography (CEM) on the topic of reimbursement strategies. METHODS: An online questionnaire on CEM reimbursement strategies was distributed to 29 selected global expert breast radiologists regarding CEM. Hospital information, CEM implementation, estimated costs, reimbursement availability, registration and declaration codes, as well as personal opinions on CEM reimbursement, were collected. Replies were analyzed using descriptive and non-parametric statistics. RESULTS: Twenty out of 29 global expert breast radiologists regarding CEM responded to this survey. All respondents had implemented CEM at their hospitals between 2011 and 2024 and offered CEM for clinical/diagnostic indications, CEM price ranges were lower than MRI at all but two sites. Sixty percent of hospitals (12/20) reported receiving reimbursement for CEM. The remaining 40% declared not receiving reimbursement, citing reasons such as coverage by department budget, lack of dedicated billing code, or absence of reimbursement by the national healthcare system and/or insurance providers. Among the 12 hospitals receiving reimbursement, 67% (8/12) obtained full reimbursement for all components of CEM, and 75% (9/12) for all indications. Reimbursement sources varied by hospital type, with public and university hospitals mainly publicly funded, and private hospitals relying on insurance or patient payments. Most respondents (75%, 15/20) reported that no dedicated national reimbursement code for CEM currently exists. Of these respondents, 53% (8/15) used the FFDM code. Most hospitals lacked separate codes for the contrast medium (60%, 9/15) or its intravenous injection (93.3%, 14/15). Among hospitals without a national code, 73% (11/15) were aware of efforts to establish one. Although nearly half of the participants (45%, 9/20) faced reimbursement challenges, 65% (13/20) stated that reimbursement strategies did not affect their CEM adoption. CONCLUSION: Substantial heterogeneity in CEM reimbursement strategies exist, with most hospitals securing full coverage through alternative strategies until dedicated national reimbursement codes are established.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".