Managing Angiography Unit Failure in Interventional Radiology: Lessons in Crisis Management and Considerations in Prevention
Bibliographic record
Abstract
Functional and efficient medical equipment is at the core of modern healthcare delivery, particularly in medical imaging. Growing healthcare costs and constrained budgets can delay equipment renewal. Aging equipment risks malfunction, potentially causing injury to patients and staff, and downtimes delaying patient care and impacting departmental revenue. Extensive equipment failure can lead to significant operational disruption which can compromise the delivery of timely and quality healthcare. Although extensive equipment failure is uncommon, 2 interventional radiology divisions at tertiary academic hospitals in Canada and the UK recently faced such a crisis. Their experiences of crisis and recovery inform this review of angiography equipment failure, and the principles learned. The concept of organizational resilience is introduced as a framework through which we review the crises. This concept can be split into successive and cooperative stages of anticipation, coping, and adaptation. Resilient organizations can identify potential threats, cope with unexpected crises, and recover swiftly to ensure future success. The author's experience of critical angiography unit failure, their response, and lessons learned are reviewed. We find these principles are broadly applicable to other medical imaging divisions and relevant to any system reliant on technology for healthcare delivery.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".