Adapting ISO 55000 Principles for Healthcare: A Case Study on CT Scanner Life Cycle Management in a Quebec Public Hospital
Bibliographic record
Abstract
Healthcare organizations are under growing pressure to optimize resources, extend the useful life of critical medical assets, and maintain uncompromised levels of patient safety. Biomedical assets such as CT scanners are central to diagnosis and treatment, yet their management often lacks strategic alignment with organizational objectives. This study explores the contextual application of ISO 55000 asset management principles in hospital settings. An intrinsic case study was conducted in a Quebec public hospital, focusing on the life cycle of a 128-slice CT scanner. Using qualitative content analysis of procurement, service, and quality documentation, the study identified three paradigmatic modulators—organizational, clinical, and financial—that mediate the translation of ISO 55000 principles into healthcare contexts. Results indicate that while ISO 55000 is highly relevant, its concepts must be adapted to account for collaborative governance structures, the primacy of patient safety, and mission-driven financing. The findings contribute to bridging the gap between industrial asset management theory and healthcare practice, offering a hybrid framework for hospitals seeking sustainable biomedical equipment management.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".