Defining the BC Provincial Preventive Maintenance Program: World Health Organization Device Type Classification
Bibliographic record
Abstract
There is no slowing down the perennial increase of in-hospital medical devices, which presents an ongoing challenge to BME departments with limited resources available for service demands. A computerized maintenance management system (CMMS) is a prerequisite for the execution and sustainment of a successful preventive maintenance (PM) program. Implementing a provincial database revealed that medical device inspections, PM schedules, and job procedures varied widely between provincial facilities. This paper will focus on the successful implementation of a Provincial PM program in British Columbia and the historical context to arrive at this point. It will also describe the risk and frequency of device types that constitute the PM schedule using the World Health Organizations “Medical Equipment Maintenance Programme” methodology. Over the course of 20 months, the British Columbia Biomedical Engineering (BCBME) CMMS team classified over 1,000 medical device types. Overall, the BCMBE program reduced the total number of device types for both Critical and Normal devices, and increased the number of Not Scheduled devices and improved the efficiency and efficacy of our PM program. It is our hope that others will find value in our approach and its implementation at a provincial level.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".