Strategic Budget Planning for Complex Medical Devices: A Case Study on Surgical Microscopes
Bibliographic record
Abstract
Dramatic developments in medical device technologies significantly influence the cost of equipment acquisition and operating expenses. Sometimes the budget estimation needed for rudimentary medical equipment can be complicated, even more so for a complex device with several add-on features. In Canada, the budget allocated to capital equipment purchases is challenging because the budget comes from the provincial government to the hospitals. The capital equipment budget amount is challenging because of the public healthcare funding model, whereby fiscal budgets come from the provincial government to the hospitals. The capital equipment budget allocation is limited and restricted in hospital as “big ticket” items compete with other capital requests. Having a strategic budgeting plan, completed by a clinical engineer, ensures a sufficient budget for the capital request. A strategic budgeting plan was central to this study to estimate the required funding for replacing aged existing surgical microscopes at the Children's Hospital of Eastern Ontario. This study demonstrates the development of a methodology to guide budget planning and includes inventory assessment, market analysis, the identification of clinical requirements, cost analysis, and the utilization of the outputs of these steps for capital planning requests. A basic step-by-step approach can be followed by any clinical engineering department before submitting a capital planning request for complex medical devices.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".