Preventative maintenance and unscheduled downtime from an economic perspective
Bibliographic record
Abstract
A spreadsheet-based model for economically characterizing the operation of a radiation treatment program has been used to perform a quantitative financial analysis of scheduled and unscheduled downtime. The incremental cost of downtime is broken down into three categories: remuneration of in-house or third party service technologists, decreased patient capacity, and local operating procedures for dealing with downtime. Different service arrangements and operating procedures are simulated to demonstrate the financial cost of treatment machine unavailability due to either preventative maintenance or unexpected breakdown. Depending on the service arrangement and operating policies for accommodating downtime, the combined cost of scheduled and unscheduled downtime (at 5%) can exceed 10% of the total cost of the radiation treatment program. It has also been demonstrated that the greatest cost component of downtime is decreased patient capacity, which can exceed $400,000 (CAN) when unscheduled downtime reaches 5%. The interpretation of this cost depends on the funding environment. Although the emphasis of this study has been the financial consequences of downtime, there are other factors which must be considered when developing policies and procedures for accommodating downtime such as effects on treatment, patient convenience and quality of life for staff. Even though the numerical results are strictly valid only within the context of the simulations performed, they do provide a broad framework within which medical physicists can make recommendations regarding service support and downtime.
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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 | 0.003 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".