Budget Impact Analysis of a Breast Rapid Diagnostic Unit
Bibliographic record
Abstract
BACKGROUND: The Odette Cancer Centre's recent implementation of a rapid diagnostic unit (rdu) for breast lesions has significantly decreased wait times to diagnosis. However, the economic impact of the unit remains unknown. This project defined the development and implementation costs and the operational costs of a breast rdu in a tertiary care facility. METHODS: From an institutional perspective, a budget impact analysis identified the direct costs associated with the breast rdu. A base-case model was also used to calculate the cost per patient to achieve a diagnosis. Sensitivity analyses computed costs based on variations in key components. Costs are adjusted to 2015 valuations using health care-specific consumer price indices and are reported in Canadian dollars. RESULTS: Initiation cost for the rdu was $366,243. The annual operational cost for support staff was $111,803. The average per-patient clinical cost for achieving a diagnosis was $770. Sensitivity analyses revealed that, if running at maximal institutional capacity, the total annual clinical cost for achieving a diagnosis could range between $136,080 and $702,675. CONCLUSIONS: Establishment and maintenance of a breast rdu requires significant investment to achieve reductions in time to diagnosis. Expenditures ought to be interpreted in the context of institutional patient volumes and trade-offs in patient-centred outcomes, including lessened patient anxiety and possibly shorter times to definitive treatment. Our study can be used as a resource-planning tool for future rdus in health care systems wishing to improve diagnostic efficiency.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".